Portuguese_Banking_Problem.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Portuguese Banking Problem.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPgk8F+Ebm8P0iC0Widzppb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nursnaaz/25DaysInMachineLearning/blob/master/Portuguese_Banking_Problem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeiUlxmi64gU",
        "outputId": "f4c3d097-4022-40d6-d2e1-c070a2acf2dd"
      },
      "source": [
        "#Library install \n",
        "!pip install pandas-profiling --upgrade"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas-profiling in /usr/local/lib/python3.6/dist-packages (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (0.25.0)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (1.15.0)\n",
            "Requirement already satisfied: jinja2>=2.8 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (2.11.2)\n",
            "Requirement already satisfied: matplotlib>=1.4 in /usr/local/lib/python3.6/dist-packages (from pandas-profiling) (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pandas-profiling) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pandas-profiling) (1.18.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pandas-profiling) (2018.9)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.8->pandas-profiling) (1.1.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4->pandas-profiling) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4->pandas-profiling) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4->pandas-profiling) (2.4.7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFVau39cb_H7"
      },
      "source": [
        "!pip install mlxtend"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjSYdV2wA6_9"
      },
      "source": [
        "#Libabry Import\n",
        "import pandas as pd\n",
        "import pandas_profiling\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "\n",
        "\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, f1_score\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from mlxtend.classifier import StackingClassifier\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.rc(\"font\", size=100)\n",
        "plt.rcParams[\"figure.figsize\"] = (20,20)"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbMjr07V_rms"
      },
      "source": [
        "import os"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "P_KI1mUq_uib",
        "outputId": "f30fcda3-272f-4c09-de2f-f9002486da65"
      },
      "source": [
        "os.getcwd()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4JKH3Km_2P3",
        "outputId": "18d241b2-9bd5-4df4-df0b-bfa2b5dbfc2c"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LkIsfJVR_4Q4",
        "outputId": "3fd79578-1c3e-45fe-ecdf-130d2f7be4a4"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bank-additional-full.csv  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ggqxmm5699pE"
      },
      "source": [
        "#Load the data \n",
        "data = pd.read_csv(\"bank-additional-full.csv\", sep=\";\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "Rk_X4o4C-PZC",
        "outputId": "6f81daec-4e3d-4529-c661-bb0e543eedf1"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>housemaid</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>226</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>307</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age        job  marital  ... euribor3m nr.employed   y\n",
              "0   56  housemaid  married  ...     4.857      5191.0  no\n",
              "1   57   services  married  ...     4.857      5191.0  no\n",
              "2   37   services  married  ...     4.857      5191.0  no\n",
              "3   40     admin.  married  ...     4.857      5191.0  no\n",
              "4   56   services  married  ...     4.857      5191.0  no\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pmexZ2aq-ZlM",
        "outputId": "a29908a4-07bd-4f4a-bc8d-4f2b74eae6a2"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhfmnHPr-h0M",
        "outputId": "2b230fb1-0cf9-45f5-d02c-59122ddd7db3"
      },
      "source": [
        "data[\"y\"].value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     36548\n",
              "yes     4640\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RecWQWhc-q4S",
        "outputId": "37769b15-ce65-4146-b867-6e800b117c44"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41188, 21)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "hiUne4N--tK5",
        "outputId": "b066b940-2f74-464a-bf20-681d19ef2952"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41188.00000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "      <td>41188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>40.02406</td>\n",
              "      <td>258.285010</td>\n",
              "      <td>2.567593</td>\n",
              "      <td>962.475454</td>\n",
              "      <td>0.172963</td>\n",
              "      <td>0.081886</td>\n",
              "      <td>93.575664</td>\n",
              "      <td>-40.502600</td>\n",
              "      <td>3.621291</td>\n",
              "      <td>5167.035911</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.42125</td>\n",
              "      <td>259.279249</td>\n",
              "      <td>2.770014</td>\n",
              "      <td>186.910907</td>\n",
              "      <td>0.494901</td>\n",
              "      <td>1.570960</td>\n",
              "      <td>0.578840</td>\n",
              "      <td>4.628198</td>\n",
              "      <td>1.734447</td>\n",
              "      <td>72.251528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-3.400000</td>\n",
              "      <td>92.201000</td>\n",
              "      <td>-50.800000</td>\n",
              "      <td>0.634000</td>\n",
              "      <td>4963.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>32.00000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.800000</td>\n",
              "      <td>93.075000</td>\n",
              "      <td>-42.700000</td>\n",
              "      <td>1.344000</td>\n",
              "      <td>5099.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.00000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>93.749000</td>\n",
              "      <td>-41.800000</td>\n",
              "      <td>4.857000</td>\n",
              "      <td>5191.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>47.00000</td>\n",
              "      <td>319.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>93.994000</td>\n",
              "      <td>-36.400000</td>\n",
              "      <td>4.961000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.00000</td>\n",
              "      <td>4918.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.400000</td>\n",
              "      <td>94.767000</td>\n",
              "      <td>-26.900000</td>\n",
              "      <td>5.045000</td>\n",
              "      <td>5228.100000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age      duration  ...     euribor3m   nr.employed\n",
              "count  41188.00000  41188.000000  ...  41188.000000  41188.000000\n",
              "mean      40.02406    258.285010  ...      3.621291   5167.035911\n",
              "std       10.42125    259.279249  ...      1.734447     72.251528\n",
              "min       17.00000      0.000000  ...      0.634000   4963.600000\n",
              "25%       32.00000    102.000000  ...      1.344000   5099.100000\n",
              "50%       38.00000    180.000000  ...      4.857000   5191.000000\n",
              "75%       47.00000    319.000000  ...      4.961000   5228.100000\n",
              "max       98.00000   4918.000000  ...      5.045000   5228.100000\n",
              "\n",
              "[8 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdsS4s5l_Ahq",
        "outputId": "2c33fe92-bad3-4706-f0bf-cf69eddbe27f"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 41188 entries, 0 to 41187\n",
            "Data columns (total 21 columns):\n",
            "age               41188 non-null int64\n",
            "job               41188 non-null object\n",
            "marital           41188 non-null object\n",
            "education         41188 non-null object\n",
            "default           41188 non-null object\n",
            "housing           41188 non-null object\n",
            "loan              41188 non-null object\n",
            "contact           41188 non-null object\n",
            "month             41188 non-null object\n",
            "day_of_week       41188 non-null object\n",
            "duration          41188 non-null int64\n",
            "campaign          41188 non-null int64\n",
            "pdays             41188 non-null int64\n",
            "previous          41188 non-null int64\n",
            "poutcome          41188 non-null object\n",
            "emp.var.rate      41188 non-null float64\n",
            "cons.price.idx    41188 non-null float64\n",
            "cons.conf.idx     41188 non-null float64\n",
            "euribor3m         41188 non-null float64\n",
            "nr.employed       41188 non-null float64\n",
            "y                 41188 non-null object\n",
            "dtypes: float64(5), int64(5), object(11)\n",
            "memory usage: 6.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yv87lvZsAvsy",
        "outputId": "b4baebd9-378a-47a8-aab4-4a7df068e1bd"
      },
      "source": [
        "pandas_profiling.ProfileReport(data)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas_profiling/describe.py:392: FutureWarning: The join_axes-keyword is deprecated. Use .reindex or .reindex_like on the result to achieve the same functionality.\n",
            "  variable_stats = pd.concat(ldesc, join_axes=pd.Index([names]), axis=1)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<meta charset=\"UTF-8\">\n",
              "\n",
              "<style>\n",
              "\n",
              "        .variablerow {\n",
              "            border: 1px solid #e1e1e8;\n",
              "            border-top: hidden;\n",
              "            padding-top: 2em;\n",
              "            padding-bottom: 2em;\n",
              "            padding-left: 1em;\n",
              "            padding-right: 1em;\n",
              "        }\n",
              "\n",
              "        .headerrow {\n",
              "            border: 1px solid #e1e1e8;\n",
              "            background-color: #f5f5f5;\n",
              "            padding: 2em;\n",
              "        }\n",
              "        .namecol {\n",
              "            margin-top: -1em;\n",
              "            overflow-x: auto;\n",
              "        }\n",
              "\n",
              "        .dl-horizontal dt {\n",
              "            text-align: left;\n",
              "            padding-right: 1em;\n",
              "            white-space: normal;\n",
              "        }\n",
              "\n",
              "        .dl-horizontal dd {\n",
              "            margin-left: 0;\n",
              "        }\n",
              "\n",
              "        .ignore {\n",
              "            opacity: 0.4;\n",
              "        }\n",
              "\n",
              "        .container.pandas-profiling {\n",
              "            max-width:975px;\n",
              "        }\n",
              "\n",
              "        .col-md-12 {\n",
              "            padding-left: 2em;\n",
              "        }\n",
              "\n",
              "        .indent {\n",
              "            margin-left: 1em;\n",
              "        }\n",
              "\n",
              "        .center-img {\n",
              "            margin-left: auto !important;\n",
              "            margin-right: auto !important;\n",
              "            display: block;\n",
              "        }\n",
              "\n",
              "        /* Table example_values */\n",
              "            table.example_values {\n",
              "                border: 0;\n",
              "            }\n",
              "\n",
              "            .example_values th {\n",
              "                border: 0;\n",
              "                padding: 0 ;\n",
              "                color: #555;\n",
              "                font-weight: 600;\n",
              "            }\n",
              "\n",
              "            .example_values tr, .example_values td{\n",
              "                border: 0;\n",
              "                padding: 0;\n",
              "                color: #555;\n",
              "            }\n",
              "\n",
              "        /* STATS */\n",
              "            table.stats {\n",
              "                border: 0;\n",
              "            }\n",
              "\n",
              "            .stats th {\n",
              "                border: 0;\n",
              "                padding: 0 2em 0 0;\n",
              "                color: #555;\n",
              "                font-weight: 600;\n",
              "            }\n",
              "\n",
              "            .stats tr {\n",
              "                border: 0;\n",
              "            }\n",
              "\n",
              "            .stats td{\n",
              "                color: #555;\n",
              "                padding: 1px;\n",
              "                border: 0;\n",
              "            }\n",
              "\n",
              "\n",
              "        /* Sample table */\n",
              "            table.sample {\n",
              "                border: 0;\n",
              "                margin-bottom: 2em;\n",
              "                margin-left:1em;\n",
              "            }\n",
              "            .sample tr {\n",
              "                border:0;\n",
              "            }\n",
              "            .sample td, .sample th{\n",
              "                padding: 0.5em;\n",
              "                white-space: nowrap;\n",
              "                border: none;\n",
              "\n",
              "            }\n",
              "\n",
              "            .sample thead {\n",
              "                border-top: 0;\n",
              "                border-bottom: 2px solid #ddd;\n",
              "            }\n",
              "\n",
              "            .sample td {\n",
              "                width:100%;\n",
              "            }\n",
              "\n",
              "\n",
              "        /* There is no good solution available to make the divs equal height and then center ... */\n",
              "            .histogram {\n",
              "                margin-top: 3em;\n",
              "            }\n",
              "        /* Freq table */\n",
              "\n",
              "            table.freq {\n",
              "                margin-bottom: 2em;\n",
              "                border: 0;\n",
              "            }\n",
              "            table.freq th, table.freq tr, table.freq td {\n",
              "                border: 0;\n",
              "                padding: 0;\n",
              "            }\n",
              "\n",
              "            .freq thead {\n",
              "                font-weight: 600;\n",
              "                white-space: nowrap;\n",
              "                overflow: hidden;\n",
              "                text-overflow: ellipsis;\n",
              "\n",
              "            }\n",
              "\n",
              "            td.fillremaining{\n",
              "                width:auto;\n",
              "                max-width: none;\n",
              "            }\n",
              "\n",
              "            td.number, th.number {\n",
              "                text-align:right ;\n",
              "            }\n",
              "\n",
              "        /* Freq mini */\n",
              "            .freq.mini td{\n",
              "                width: 50%;\n",
              "                padding: 1px;\n",
              "                font-size: 12px;\n",
              "\n",
              "            }\n",
              "            table.freq.mini {\n",
              "                 width:100%;\n",
              "            }\n",
              "            .freq.mini th {\n",
              "                overflow: hidden;\n",
              "                text-overflow: ellipsis;\n",
              "                white-space: nowrap;\n",
              "                max-width: 5em;\n",
              "                font-weight: 400;\n",
              "                text-align:right;\n",
              "                padding-right: 0.5em;\n",
              "            }\n",
              "\n",
              "            .missing {\n",
              "                color: #a94442;\n",
              "            }\n",
              "            .alert, .alert > th, .alert > td {\n",
              "                color: #a94442;\n",
              "            }\n",
              "\n",
              "\n",
              "        /* Bars in tables */\n",
              "            .freq .bar{\n",
              "                float: left;\n",
              "                width: 0;\n",
              "                height: 100%;\n",
              "                line-height: 20px;\n",
              "                color: #fff;\n",
              "                text-align: center;\n",
              "                background-color: #337ab7;\n",
              "                border-radius: 3px;\n",
              "                margin-right: 4px;\n",
              "            }\n",
              "            .other .bar {\n",
              "                background-color: #999;\n",
              "            }\n",
              "            .missing .bar{\n",
              "                background-color: #a94442;\n",
              "            }\n",
              "            .tooltip-inner {\n",
              "                width: 100%;\n",
              "                white-space: nowrap;\n",
              "                text-align:left;\n",
              "            }\n",
              "\n",
              "            .extrapadding{\n",
              "                padding: 2em;\n",
              "            }\n",
              "\n",
              "            .pp-anchor{\n",
              "\n",
              "            }\n",
              "\n",
              "</style>\n",
              "\n",
              "<div class=\"container pandas-profiling\">\n",
              "    <div class=\"row headerrow highlight\">\n",
              "        <h1>Overview</h1>\n",
              "    </div>\n",
              "    <div class=\"row variablerow\">\n",
              "    <div class=\"col-md-6 namecol\">\n",
              "        <p class=\"h4\">Dataset info</p>\n",
              "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
              "            <tbody>\n",
              "            <tr>\n",
              "                <th>Number of variables</th>\n",
              "                <td>21 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Number of observations</th>\n",
              "                <td>41188 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Total Missing (%)</th>\n",
              "                <td>0.0% </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Total size in memory</th>\n",
              "                <td>6.6 MiB </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Average record size in memory</th>\n",
              "                <td>168.0 B </td>\n",
              "            </tr>\n",
              "            </tbody>\n",
              "        </table>\n",
              "    </div>\n",
              "    <div class=\"col-md-6 namecol\">\n",
              "        <p class=\"h4\">Variables types</p>\n",
              "        <table class=\"stats\" style=\"margin-left: 1em;\">\n",
              "            <tbody>\n",
              "            <tr>\n",
              "                <th>Numeric</th>\n",
              "                <td>8 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Categorical</th>\n",
              "                <td>11 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Boolean</th>\n",
              "                <td>0 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Date</th>\n",
              "                <td>0 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Text (Unique)</th>\n",
              "                <td>0 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Rejected</th>\n",
              "                <td>2 </td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                <th>Unsupported</th>\n",
              "                <td>0 </td>\n",
              "            </tr>\n",
              "            </tbody>\n",
              "        </table>\n",
              "    </div>\n",
              "    <div class=\"col-md-12\" style=\"padding-left: 1em;\">\n",
              "        \n",
              "        <p class=\"h4\">Warnings</p>\n",
              "        <ul class=\"list-unstyled\"><li><a href=\"#pp_var_previous\"><code>previous</code></a> has 35563 / 86.3% zeros <span class=\"label label-info\">Zeros</span></li><li><a href=\"#pp_var_euribor3m\"><code>euribor3m</code></a> is highly correlated with <a href=\"#pp_var_emp.var.rate\"><code>emp.var.rate</code></a> (ρ = 0.97224) <span class=\"label label-primary\">Rejected</span></li><li><a href=\"#pp_var_nr.employed\"><code>nr.employed</code></a> is highly correlated with <a href=\"#pp_var_euribor3m\"><code>euribor3m</code></a> (ρ = 0.94515) <span class=\"label label-primary\">Rejected</span></li><li>Dataset has 12 duplicate rows <span class=\"label label-warning\">Warning</span></li> </ul>\n",
              "    </div>\n",
              "</div>\n",
              "    <div class=\"row headerrow highlight\">\n",
              "        <h1>Variables</h1>\n",
              "    </div>\n",
              "    <div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_age\">age<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>78</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.2%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>40.024</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>17</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>98</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram-1126928340026350280\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABK0lEQVR4nO3dwQ2CMABAUTWO5BDu5NmdHMKd6gLmR0yUiu/dSXr5aSkU9mOMsQOeOqw9AJjZce0BrOV0uS2%2B5n49f2AkzMwMAkEgEDaxxHpnuQSvMINAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIhE28rPgtS1%2BKdH7k95lBIAgEgkAgCASCQCAIBIJAIHgO8kG%2BvfX7zCAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBCmOw/ih5zMZLpA/p1DVnOxxIIgEAgCgSAQCG7SN2DWnb8tbB7sxxhj7UHArCyxIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIDwA7vMaaQk/2skAAAAASUVORK5CYII%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-1126928340026350280,#minihistogram-1126928340026350280\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives-1126928340026350280\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-1126928340026350280\"\n",
              "                                                  aria-controls=\"quantiles-1126928340026350280\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram-1126928340026350280\" aria-controls=\"histogram-1126928340026350280\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common-1126928340026350280\" aria-controls=\"common-1126928340026350280\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme-1126928340026350280\" aria-controls=\"extreme-1126928340026350280\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-1126928340026350280\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>17</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>26</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>32</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>38</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>47</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>58</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>98</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>81</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>15</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>10.421</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>0.26037</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>0.79131</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>40.024</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>8.4615</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>0.7847</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>1648511</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>108.6</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-1126928340026350280\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nO3deXhU5cH%2B8TuLCUvIBiRYSkXZJAthTw1ISBBQKKDIlr6U0oKCUNJEaBEFReUVFBQVvFjaQqXaOiZtFSgIL2WpC11c0GTAhSivkjeQCDMEQhJIcn5/8GN0DMr2kJkzfD/XxRXzPDPnPLc5A3fOOZkEWZZlCQAAAMYE%2B3oBAAAAgYaCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMC/X1Aq4WZWXHfb2EeoKDgxQb21RHj1aors7y9XKMIZe9BGKuQMwkkctuyHVGy5bNGmBV9XEG6yoWHBykoKAgBQcH%2BXopRpHLXgIxVyBmkshlN%2BTyLQoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMP8umC9/vrrSktLU25ubr25rVu3avjw4erWrZsGDx6sl19%2B2Wt%2B3bp1Gjx4sLp3766srCwVFhZ65qqrq/Xggw%2BqX79%2BSk1NVXZ2tlwul2e%2BuLhYd999t1JTU5WRkaHFixerrq7uygUFAAABxW8L1m9%2B8xstWLBA1113Xb25Dz74QLNmzVJ2drb%2B85//6P7779cjjzyit99%2BW5K0fft2LVu2TE888YTeeustZWRkaOrUqTp58qQkaenSpXI6nXI4HNqyZYssy9KcOXM8258xY4bi4%2BO1bds2rV27Vtu2bdPzzz/fMMEBAIDt%2BW3BCg8PV35%2B/jkLltvt1pQpU3TLLbcoNDRU6enp6tixo6dgORwOjRw5UikpKWrUqJEmT54sSdqxY4dqamqUn5%2BvadOm6dprr1V0dLRycnK0c%2BdOHT58WAUFBfrwww81a9YsNWvWTG3bttXEiRPlcDgaND8AALCvUF8v4NtMmDDhW%2Bf69eunfv36eT6vqalRWVmZ4uPjJUlOp1NDhgzxzAcHB6tz584qKChQ586ddfz4cSUmJnrm27Vrp0aNGsnpdKq0tFStW7dWVFSUZz4xMVGfffaZTpw4oYiIiPOuvbS0VGVlZV5joaFNFBcXd/7gDSgkJNjrY6AwlWvgktdNLKfB/M%2Bsm329hEsSiMdhIGaSyGU35PItvy1YF2PJkiVq0qSJp1S53W6vgiRJUVFRcrlccrvdkqTIyEiv%2BcjISM/8N%2BfObsvlcl1QwXI4HFq%2BfLnX2PTp05WdnX1xwRpIZGRjXy/higjUXN8mJqapr5dwWQLx6xWImSRy2Q25fMPWBcuyLC1ZskQbN27UunXrFB4e7jV3vudeytyFGDt2rDIzM73GQkObyOWquKztmhYSEqzIyMYqL69UbW3g3MQfqLnOx9%2BOrwsViF%2BvQMwkkctuyHWGr775tG3Bqqur05w5c/TBBx/oT3/6k9q0aeOZi4mJ8ZypOsvtdqtDhw6KjY31fN606Vf/048dO6bmzZurtrb2nM8NCgryPPd84uLi6l0OLCs7rpoa/zzAa2vr/HZtlyNQc30bu2cNxK9XIGaSyGU35PIN/76A%2BR0ee%2BwxffLJJ/XKlSQlJSXJ6XR6Pq%2BtrdXevXuVkpKiNm3aKCoqymv%2B448/1qlTp5SUlKSkpCSVlJTo6NGjnvmCggK1b9/eq5ABAAB8G1sWrHfeeUfr16/X6tWrFR0dXW8%2BKytLr7zyivbs2aPKykqtWLFCYWFh6t%2B/v0JCQjRmzBitXLlSJSUlcrlceuqppzRw4EC1aNFCCQkJSk5O1pNPPqkTJ06oqKhIa9euVVZWlg%2BSAgAAO/LbS4TJycmSzvyEoCRt27ZN0pmzSX/%2B8591/PhxZWRkeD2nV69eWrNmjfr166d7771XOTk5OnLkiJKTk7V69Wo1atRIkpSdna2KigqNGDFCNTU1ysjI0Pz58z3befbZZzVv3jz16dNHERERGjdunH784x83QGoAABAIgqzLvaMbF6Ss7Livl1BPaGiwYmKayuWq8Ovr2BfLVK7bnn7T4KquvM05fXy9hEsSiMdhIGaSyGU35DqjZctmDbCq%2Bmx5iRAAAMCfUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPPb30WIwGO3Xz0DAMCl4gwWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPPrgvX6668rLS1Nubm59eY2bdqkYcOGqVu3bho5cqTeeOMNz1xdXZ2WLl2qAQMGqFevXpo0aZK%2B%2BOILz7zb7VZOTo7S0tLUt29fPfDAA6qqqvLM79u3T%2BPHj1ePHj00aNAgrVmz5soGBQAAAcVvC9ZvfvMbLViwQNddd129uX379mn27NmaNWuW/vnPf2rixIn6xS9%2BoUOHDkmSXnzxRW3YsEGrV6/Wjh071LZtW02fPl2WZUmS5s2bp8rKSm3cuFF//vOfVVRUpCVLlkiSqqqqNGXKFP3whz/U66%2B/rqVLl2rVqlXaunVrw4UHAAC25rcFKzw8XPn5%2BecsWHl5eUpPT1d6errCw8M1fPhwdezYUevXr5ckORwOTZw4Ue3atVNERIRyc3NVVFSk999/X19%2B%2BaW2bdum3NxcxcbGKj4%2BXtOmTdOf//xnnT59Wjt37tTp06d1zz33qEmTJkpMTNTo0aPlcDga%2Bn8BAACwKb8tWBMmTFCzZs3OOed0OpWQkOA1lpCQoIKCAlVVVWn//v1e8xEREbruuutUUFCgffv2KSQkRJ06dfLMJyYm6uTJk/r000/ldDrVqVMnhYSEeG27sLDQcEIAABCoQn29gEvhdrsVFRXlNRYVFaX9%2B/fr2LFjsizrnPMul0vR0dGKiIhQUFCQ15wkuVwuud1uRUZGej03OjpabrdbdXV1Cg4%2BfyctLS1VWVmZ11hoaBPFxcVdVM4rLSQk2Osj7C001J5fx0A8DgMxk0QuuyGXb9myYEny3E91KfPne%2B65fL2QnY/D4dDy5cu9xqZPn67s7OyL3m9DiIxs7OslwICYmKa%2BXsJlCcTjMBAzSeSyG3L5hi0LVkxMjNxut9eY2%2B1WbGysoqOjFRwcfM755s2bKzY2VidOnFBtba3nMuDZx56dP3DgQL3nnt3uhRg7dqwyMzO9xkJDm8jlqriYmFdcSEiwIiMbq7y8UrW1db5eDi6Tvx1fFyoQj8NAzCSRy27IdYavvvm0ZcFKSkqqd09UQUGBhg4dqvDwcHXo0EFOp1O9e/eWJJWXl%2Bvzzz9Xly5d1Lp1a1mWpQ8//FCJiYme50ZGRur6669XUlKS/vSnP6mmpkahoaGe%2BZSUlAteX1xcXL3LgWVlx1VT458HeG1tnd%2BuDRfO7l/DQDwOAzGTRC67IZdv%2BPcFzG8xZswYvfXWW9q5c6eqq6uVn5%2BvAwcOaPjw4ZKkrKwsrVu3TkVFRTpx4oSWLFmizp07Kzk5WbGxsRo8eLCefvppHT16VIcOHdJzzz2nUaNGKTQ0VOnp6YqIiNCKFStUWVmp999/X/n5%2BcrKyvJxagAAYBd%2BewYrOTlZklRTUyNJ2rZtm6QzZ5M6duyoJUuWaOHChSouLlb79u21atUqtWzZUpI0btw4lZWV6Sc/%2BYkqKiqUmprqdU/UI488ooceekgDBgzQNddcox/96EeeNzMNCwvTypUr9dBDD2n16tVq0aKFcnNz1b9//wZMDwAA7CzIupQ7vnHRysqO%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%2B%2BKD69eun1NRUZWdny%2BVyNWg2AABgb7YsWDU1Nbr77rvVtWtXvfXWW9q4caOOHj2q%2BfPnq7S0VNOmTdO4ceO0e/duPfDAA5o3b54KCgokSdu3b9eyZcv0xBNP6K233lJGRoamTp2qkydPSpKWLl0qp9Mph8OhLVu2yLIszZkzx5dxAQCAzYT6egGXoqysTGVlZRoxYoTCwsIUFhamgQMHas2aNdqwYYPatm2rUaNGSZLS0tKUmZmpvLw8JScny%2BFwaOTIkUpJSZEkTZ48WevWrdOOHTs0ePBg5efn6/HHH9e1114rScrJydHQoUN1%2BPBhxcfH%2BywzcD63Pf2mr5dwwTbn9PH1EgDgirJlwYqPj1fnzp3lcDj0y1/%2BUlVVVdq6dav69%2B8vp9OphIQEr8cnJCRo8%2BbNkiSn06khQ4Z45oKDg9W5c2cVFBSoc%2BfOOn78uBITEz3z7dq1U6NGjeR0Oi%2B4YJWWlqqsrMxrLDS0ieLi4i418hUREhLs9RFoKKGhXx1zgXgcBmImiVx2Qy7fsmXBCg4O1rJlyzRx4kQ9//zzkqTevXtr5syZmjZtWr0iFB0d7bmPyu12Kyoqyms%2BKipKLpdLbrdbkhQZGek1HxkZeVH3YTkcDi1fvtxrbPr06crOzr7gbTSkyMjGvl4CrjIxMU3rjQXicRiImSRy2Q25fMOWBevUqVOaOnWqbr31Vs/9Uw8//LBmzZp1Qc%2B3LOuy5s9n7NixyszM9BoLDW0il6visrZrWkhIsCIjG6u8vFK1tXW%2BXg6uIl9/LQTicRiImSRy2Q25zjjXN3QNwZYFa/fu3Tp48KDuvfdehYSEqFmzZsrOztaIESN08803e85EneVyuRQbGytJiomJqTfvdrvVoUMHz2PcbreaNv3qC3Ls2DE1b978gtcXFxdX73JgWdlx1dT45wFeW1vnt2tDYDrX8RaIx2EgZpLIZTfk8g3/voD5LWpra1VXV%2Bd1punUqVOSztzU/vW3XZCkwsJCz03tSUlJcjqdXtvau3evUlJS1KZNG0VFRXnNf/zxxzp16pSSkpKuZCQAABBAbFmwunXrpiZNmmjZsmWqrKyUy%2BXSihUr1KtXL40YMULFxcXKy8tTdXW1du3apV27dmnMmDGSpKysLL3yyivas2ePKisrtWLFCoWFhal///4KCQnRmDFjtHLlSpWUlMjlcumpp57SwIED1aJFCx%2BnBgAAdmHLS4QxMTH63e9%2Bp8cff1z9%2BvVTWFiYevfurfnz56t58%2BZatWqVFixYoIcfflitW7fW4sWLdeONN0qS%2BvXrp3vvvVc5OTk6cuSIkpOTtXr1ajVq1EiSlJ2drYqKCo0YMUI1NTXKyMjQ/PnzfZgWAADYTZB1uXd044KUlR339RLqCQ0NVkxMU7lcFQ1yHdtO79OEK%2Bvr74PV0MdhQwjETBK57IZcZ7Rs2awBVlWfLS8RAgAA%2BDMKFgAAgGEULAAAAMOMF6zMzEwtX75cJSUlpjcNAABgC8YL1p133qlNmzbplltu0eTJk7V161bV1NSY3g0AAIDfMl6wpk%2Bfrk2bNunll19Whw4d9Nhjjyk9PV2LFy/WZ599Znp3AAAAfueK3YOVmJio2bNna8eOHbr//vv18ssva8iQIZo0aZI%2B%2BOCDK7VbAAAAn7tiBev06dPatGmT7rrrLs2ePVvx8fGaM2eOOnfurIkTJ2rDhg1XatcAAAA%2BZfyd3IuKipSfn69XXnlFFRUVGjx4sJ5//nn16NHD85hevXpp/vz5GjZsmOndAwAA%2BJzxgjV06FBdf/31mjJlim6//XZFR0fXe0x6erqOHj1qetcAAAB%2BwXjBWrdunXr37n3ex73//vumdw0AAOAXjN%2BD1alTJ02dOlXbtm3zjP3%2B97/XXXfdJbfbbXp3AAAAfsd4wVq4cKGOHz%2Bu9u3be8b69%2B%2Bvuro6LVq0yPTuAAAA/I7xS4RvvPGGNmzYoJiYGM9Y27ZttWTJEv3oRz8yvTsAAAC/Y/wMVlVVlcLDw%2BvvKDhYlZWVpncHAADgd4wXrF69emnRokU6duyYZ%2Bzw4cN6%2BOGHvd6qAQAAIFAZv0R4//336%2Bc//7luuukmRUREqK6uThUVFWrTpo3%2B8Ic/mN4dAACA3zFesNq0aaO//e1v%2Bsc//qHPP/9cwcHBuv7669W3b1%2BFhISY3h0AAIDfMV6wJCksLEy33HLLldg0AACA3zNesL744gs9%2BeST%2BuSTT1RVVVVv/u9//7vpXQIAAPiVK3IPVmlpqfr27asmTZqY3jwAAIDfM16wCgsL9fe//12xsbGmNw0AAGALxt%2BmoXnz5py5AgAAVzXjBWvKlClavny5LMsyvWkAAABbMH6J8B//%2BIfeffdd/eUvf9H3v/99BQd7d7iXXnrJ9C4BAAD8ivGCFRERoX79%2BpneLAAAgG0YL1gLFy40vUkAAABbMX4PliR9%2BumnWrZsmebMmeMZe%2B%2B9967ErgAAAPyO8YK1e/duDR8%2BXFu3btXGjRslnXnz0QkTJvAmowAA4KpgvGAtXbpUv/rVr7RhwwYFBQVJOvP7CRctWqTnnnvO9O4AAAD8jvGC9fHHHysrK0uSPAVLkm699VYVFRWZ3h0AAIDfMV6wmjVrds7fQVhaWqqwsDDTuwMAAPA7xgtW9%2B7d9dhjj%2BnEiROesc8%2B%2B0yzZ8/WTTfdZHp3AAAAfsf42zTMmTNHP/3pT5Wamqra2lp1795dlZWV6tChgxYtWmR6dwAAAH7HeMFq1aqVNm7cqF27dumzzz5To0aNdP3116tPnz5e92QBAAAEKuMFS5KuueYa3XLLLVdi0wAAAH7PeMHKzMz8zjNVvBcWAAAIdMYL1pAhQ7wKVm1trT777DMVFBTopz/9qendAQAA%2BB3jBWvWrFnnHN%2ByZYv%2B9a9/md4dAACA37kiv4vwXG655Rb97W9/a6jdAQAA%2BEyDFay9e/fKsiyj21yxYoX69u2rrl27auLEiTp48KCkM78PcdSoUerevbuGDh2q9evXez1v3bp1Gjx4sLp3766srCwVFhZ65qqrq/Xggw%2BqX79%2BSk1NVXZ2tlwul9F1AwCAwGb8EuG4cePqjVVWVqqoqEiDBg0ytp8XX3xR69ev17p16xQXF6enn35av//973X33Xdr2rRpeuCBBzRs2DC98847uueee3T99dcrOTlZ27dv17Jly/Tb3/5WnTp10rp16zR16lRt3bpVTZo00dKlS%2BV0OuVwONS4cWPNmzdPc%2BbM0cqVK42tHQAABDbjBatt27b1foowPDxco0aN0ujRo43tZ82aNZo9e7ZuuOEGSdLcuXMlSb/73e/Utm1bjRo1SpKUlpamzMxM5eXlKTk5WQ6HQyNHjlRKSookafLkyVq3bp127NihwYMHKz8/X48//riuvfZaSVJOTo6GDh2qw4cPKz4%2B3tj6AQBA4DJesBri3doPHz6sgwcP6tixYxoyZIiOHDmi1NRUzZ8/X06nUwkJCV6PT0hI0ObNmyVJTqdTQ4YM8cwFBwerc%2BfOKigoUOfOnXX8%2BHElJiZ65tu1a6dGjRrJ6XRecMEqLS1VWVmZ11hoaBPFxcVdauQrIiQk2Osj0FBCQ7865gLxOAzETBK57IZcvmW8YL3yyisX/Njbb7/9kvZx6NAhSdJrr72mtWvXyrIsZWdna%2B7cuaqqqqpXhKKjoz33UbndbkVFRXnNR0VFyeVyye12S5IiIyO95iMjIy/qPiyHw6Hly5d7jU2fPl3Z2dkXvI2GFBnZ2NdLwFUmJqZpvbFAPA4DMZNELrshl28YL1gPPPCA6urq6t3QHhQU5DUWFBR0yQXr7HYmT57sKVMzZszQXXfdpbS0tAt%2B/qXOn8/YsWOVmZnpNRYa2kQuV8Vlbde0kJBgRUY2Vnl5pWpr63y9HFxFvv5aCMTjMBAzSeSyG3Kdca5v6BqC8YL129/%2BVmvWrNHUqVPVqVMnWZaljz76SL/5zW80fvx4paamXvY%2BWrRoIcn7TFPr1q1lWZZOnz7tORN1lsvlUmxsrCQpJiam3rzb7VaHDh08j3G73Wra9KsvyLFjx9S8efMLXl9cXFy9y4FlZcdVU%2BOfB3htbZ3frg2B6VzHWyAeh4GYSSKX3ZDLN4xfwFy0aJEWLFigHj16KCIiQs2aNVPPnj31yCOP6PHHH1dYWJjnz6Vq1aqVIiIitG/fPs9YcXGxrrnmGqWnp3u97YIkFRYWem5qT0pKktPp9MzV1tZq7969SklJUZs2bRQVFeU1//HHH%2BvUqVNKSkq65PUCAICri/GCdeDAgXr3OElnzjYVFxcb2UdoaKhGjRqllStX6n//93915MgRPffccxo2bJjuuOMOFRcXKy8vT9XV1dq1a5d27dqlMWPGSJKysrL0yiuvaM%2BePaqsrNSKFSsUFham/v37KyQkRGPGjNHKlStVUlIil8ulp556SgMHDvScNQMAADgf45cIW7durUWLFumXv/ylYmJiJEnl5eV69tln9YMf/MDYfmbOnKlTp05p9OjROn36tAYPHqy5c%2BeqadOmWrVqlRYsWKCHH35YrVu31uLFi3XjjTdKkvr166d7771XOTk5OnLkiJKTk7V69Wo1atRIkpSdna2KigqNGDFCNTU1ysjI0Pz5842tG4B029Nv%2BnoJF2VzTh9fLwGAzQRZht9e/Y033tDMmTNVXl6upk2bKjg4WCdOnFCjRo303HPP6aabbjK5O9soKzvu6yXUExoarJiYpnK5KhrkOrbd/lEFzrrYgtXQr62GQi57IdcZLVs2a4BV1Wf8DFbfvn21c%2BdO7dq1S4cOHZJlWYqPj9fNN9%2BsZs18ExIAAKAhGS9YktS4cWMNGDBAhw4dUps2ba7ELgAAAPyW8Zvcq6qqNHv2bHXr1k233XabpDP3YE2ePFnl5eWmdwcAAOB3jBesxYsXa9%2B%2BfVqyZImCg7/afG1trZYsWWJ6dwAAAH7HeMHasmWLnn32Wd16662eX/ocGRmphQsXauvWraZ3BwAA4HeMF6yKigq1bdu23nhsbKxOnjxpencAAAB%2Bx3jB%2BsEPfqB//etfkrx/p99rr72m733ve6Z3BwAA4HeM/xThj3/8Y82YMUN33nmn6urqtHbtWhUWFmrLli164IEHTO8OAADA7xgvWGPHjlVoaKheeOEFhYSEaOXKlbr%2B%2Buu1ZMkS3XrrraZ3BwAA4HeMF6yjR4/qzjvv1J133ml60wAAALZg/B6sAQMGyPBv3wEAALAV4wUrNTVVmzdvNr1ZAAAA2zB%2BifDaa6/Vf//3f2v16tX6wQ9%2BoGuuucZr/sknnzS9SwAAAL9ivGDt379fN9xwgyTJ5XKZ3jwAAIDfM1awcnNztXTpUv3hD3/wjD333HOaPn26qV0AAADYgrF7sLZv315vbPXq1aY2DwAAYBvGCta5fnKQnyYEAABXI2MF6%2Bwvdj7fGAAAQKAz/jYNAAAAVzsKFgAAgGHGforw9OnTmjlz5nnHeB8sAAAQ6IwVrB49eqi0tPS8YwAAAIHOWMH6%2BvtfAQAAXM24BwsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYFhAFKzHHntMnTp18ny%2Be/dujRo1St27d9fQoUO1fv16r8evW7dOgwcPVvfu3ZWVlaXCwkLPXHV1tR588EH169dPqampys7OlsvlarAsAADA/mxfsPbt26dXX33V83lpaammTZumcePGaffu3XrggQc0b948FRQUSJK2b9%2BuZcuW6YknntBbb72ljIwMTZ06VSdPnpQkLV26VE6nUw6HQ1u2bJFlWZozZ45PsgEAAHuydcGqq6vTQw89pIkTJ3rGNmzYoLZt22rUqFEKDw9XWlqaMjMzlZeXJ0lyOBwaOXKkUlJS1KhRI02ePFmStGPHDtXU1Cg/P1/Tpk3Ttddeq%2BjoaOXk5Gjnzp06fPiwLyICAAAbsnXBeumllxQeHq5hw4Z5xpxOpxISErwel5CQ4LkM%2BM354OBgde7cWQUFBfr88891/PhxJSYmeubbtWunRo0ayel0XuE0AAAgUIT6egGX6ssvv9SyZcv0hz/8wWvc7XYrPj7eayw6OtpzH5Xb7VZUVJTXfFRUlFwul9xutyQpMjLSaz4yMvKi7sMqLS1VWVmZ11hoaBPFxcVd8DYaQkhIsNdHAOcWGnpxr5FAfW2Ry17I5Vu2LVgLFy7UyJEj1b59ex08ePCinmtZ1mXNn4/D4dDy5cu9xqZPn67s7OzL2i2h7RAAABV7SURBVO6VEhnZ2NdLAPxaTEzTS3peoL62yGUv5PINWxas3bt367333tPGjRvrzcXExHjORJ3lcrkUGxv7rfNut1sdOnTwPMbtdqtp06/%2BQj127JiaN29%2BwesbO3asMjMzvcZCQ5vI5aq44G00hJCQYEVGNlZ5eaVqa%2Bt8vRzAb13sazdQX1vkshdynXGp3yBdLlsWrPXr1%2BvIkSPKyMiQ9NUZp9TUVP385z%2BvV7wKCwuVkpIiSUpKSpLT6dQdd9whSaqtrdXevXs1atQotWnTRlFRUXI6nWrdurUk6eOPP9apU6eUlJR0weuLi4urdzmwrOy4amr88wCvra3z27UB/uBSXx%2BB%2Btoil72Qyzf8%2BwLmt7jvvvu0ZcsWvfrqq3r11Ve1evVqSdKrr76qYcOGqbi4WHl5eaqurtauXbu0a9cujRkzRpKUlZWlV155RXv27FFlZaVWrFihsLAw9e/fXyEhIRozZoxWrlypkpISuVwuPfXUUxo4cKBatGjhy8gAAMBGbHkGKyoqyutG9ZqaGklSq1atJEmrVq3SggUL9PDDD6t169ZavHixbrzxRklSv379dO%2B99yonJ0dHjhxRcnKyVq9erUaNGkmSsrOzVVFRoREjRqimpkYZGRmaP39%2BwwYEAAC2FmRd7h3duCBlZcd9vYR6QkODFRPTVC5XRYOcZr3t6Tev%2BD6AK2FzTp%2BLenxDv7YaCrnshVxntGzZrAFWVZ8tLxECAAD4MwoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCYbQtWcXGxpk%2BfrtTUVKWlpem%2B%2B%2B5TeXm5JGnfvn0aP368evTooUGDBmnNmjVez920aZOGDRumbt26aeTIkXrjjTc8c3V1dVq6dKkGDBigXr16adKkSfriiy8aNBsAALA32xasqVOnKjIyUtu3b9df/vIXffLJJ3r88cdVVVWlKVOm6Ic//KFef/11LV26VKtWrdLWrVslnSlfs2fP1qxZs/TPf/5TEydO1C9%2B8QsdOnRIkvTiiy9qw4YNWr16tXbs2KG2bdtq%2BvTpsizLl3EBAICN2LJglZeXKykpSTNnzlTTpk3VqlUr3XHHHXr77be1c%2BdOnT59Wvfcc4%2BaNGmixMREjR49Wg6HQ5KUl5en9PR0paenKzw8XMOHD1fHjh21fv16SZLD4dDEiRPVrl07RUREKDc3V0VFRXr//fd9GRkAANhIqK8XcCkiIyO1cOFCr7GSkhLFxcXJ6XSqU6dOCgkJ8cwlJCQoLy9PkuR0OpWenu713ISEBBUUFKiqqkr79%2B9XQkKCZy4iIkLXXXedCgoK1LVr1wtaX2lpqcrKyrzGQkObKC4u7qJyXmkhIcFeHwGcW2joxb1GAvW1RS57IZdv2bJgfVNBQYFeeOEFrVixQps3b1ZkZKTXfHR0tNxut%2Brq6uR2uxUVFeU1HxUVpf379%2BvYsWOyLOuc8y6X64LX43A4tHz5cq%2Bx6dOnKzs7%2ByKTNYzIyMa%2BXgLg12Jiml7S8wL1tUUueyGXb9i%2BYL3zzju65557NHPmTKWlpWnz5s3nfFxQUJDnv893P9Xl3m81duxYZWZmeo2FhjaRy1VxWds1LSQkWJGRjVVeXqna2jpfLwfwWxf72g3U1xa57IVcZ1zqN0iXy9YFa/v27frVr36lefPm6fbbb5ckxcbG6sCBA16Pc7vdio6OVnBwsGJiYuR2u%2BvNx8bGeh5zrvnmzZtf8Lri4uLqXQ4sKzuumhr/PMBra%2Bv8dm2AP7jU10egvrbIZS/k8g3/voD5Hd59913Nnj1bzzzzjKdcSVJSUpI%2B%2Bugj1dTUeMYKCgqUkpLimS8sLPTa1tn58PBwdejQQU6n0zNXXl6uzz//XF26dLnCiQAAQKCwZcGqqanR3LlzNWvWLPXt29drLj09XREREVqxYoUqKyv1/vvvKz8/X1lZWZKkMWPG6K233tLOnTtVXV2t/Px8HThwQMOHD5ckZWVlad26dSoqKtKJEye0ZMkSde7cWcnJyQ2eEwAA2JMtLxHu2bNHRUVFWrBggRYsWOA199prr2nlypV66KGHtHr1arVo0UK5ubnq37%2B/JKljx45asmSJFi5cqOLiYrVv316rVq1Sy5YtJUnjxo1TWVmZfvKTn6iiokKpqan1blgHAAD4LkEW76DZIMrKjvt6CfWEhgYrJqapXK6KBrmOfdvTb17xfQBXwuacPhf1%2BIZ%2BbTUUctkLuc5o2bJZA6yqPlteIgQAAPBnFCwAAADDbHkPFr7CZTcAAPwPBQsAzsNu38hc7D1jAMzjEiEAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsFBfLwAAYNZtT7/p6yVcsM05fXy9BOCK4AwWAACAYRQsAAAAw7hECADwGTtdzpS4pIkLxxmscyguLtbdd9%2Bt1NRUZWRkaPHixaqrq/P1sgAAgE1wBuscZsyYocTERG3btk1HjhzRlClT1KJFC/3sZz/z9dIAAIANULC%2BoaCgQB9%2B%2BKHWrl2rZs2aqVmzZpo4caKef/55ChYAXOXsdknz7f%2B%2B1ddLuGpRsL7B6XSqdevWioqK8owlJibqs88%2B04kTJxQREXHebZSWlqqsrMxrLDS0ieLi4oyvFwCA7xISElh3A53N4%2B%2B5KFjf4Ha7FRkZ6TV2tmy5XK4LKlgOh0PLly/3GvvFL36hGTNmmFvo/3c5352UlpbK4XBo7NixAVX%2ByGUvgZgrEDNJ5LKb0tJSLVu2LCBzPf/8b/0%2Bl3/XPx%2BxLOuynj927Fj95S9/8fozduxYQ6szp6ysTMuXL693ts3uyGUvgZgrEDNJ5LIbcvkWZ7C%2BITY2Vm6322vM7XYrKChIsbGxF7SNuLg4v27VAADgyuIM1jckJSWppKRER48e9YwVFBSoffv2atq0qQ9XBgAA7IKC9Q0JCQlKTk7Wk08%2BqRMnTqioqEhr165VVlaWr5cGAABsImT%2B/Pnzfb0If3PzzTdr48aNevTRR/W3v/1No0aN0qRJkxQUFOTrpRnXtGlT9e7dO%2BDOzpHLXgIxVyBmkshlN%2BTynSDrcu/oBgAAgBcuEQIAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsG6ShQXF2v69OlKTU1VWlqa7rvvPpWXl0uS9u3bp/Hjx6tHjx4aNGiQ1qxZ4%2BPVXrgPP/xQP/3pT9WjRw%2BlpaUpJydHZWVlkqTdu3dr1KhR6t69u4YOHar169f7eLUX77HHHlOnTp08n9s5U6dOnZSUlKTk5GTPn0cffVSSvXNJ0ooVK9S3b1917dpVEydO1MGDByXZN9d//vMfr69TcnKykpKSPMeiXXNJ0t69ezVhwgT17NlTffr00axZs3T06FFJ9s5VWFioCRMmqEePHrr55pv1u9/9zjO3adMmDRs2TN26ddPIkSP1xhtv%2BHCl3%2B31119XWlqacnNz6819V466ujotXbpUAwYMUK9evTRp0iR98cUXDbn0%2BixcFX70ox9Z9913n3XixAmrpKTEGjlypHX//fdblZWV1s0332wtW7bMqqiosAoLC63evXtbW7Zs8fWSz6u6utq66aabrOXLl1vV1dXWkSNHrPHjx1vTpk2zDh8%2BbHXt2tXKy8uzqqqqrDfffNPq0qWL9cEHH/h62Rds7969Vu/eva2OHTtalmXZPlPHjh2tL774ot643XO98MIL1q233moVFRVZx48ftx599FHr0UcftX2ub1qxYoX1y1/%2B0ta5Tp8%2BbfXp08d68sknrerqauvo0aPWz372M2vGjBm2zuVyuazU1FRryZIl1smTJ62PP/7YysjIsDZt2mTt3bvXSkpKsnbu3GlVVVVZr776qpWSkmKVlJT4etn1rF692ho0aJA1btw4Kycnx2vufDnWrVtnZWRkWPv377eOHz9uPfLII9awYcOsuro6X0SxLMuyOIN1FSgvL1dSUpJmzpyppk2bqlWrVrrjjjv09ttva%2BfOnTp9%2BrTuueceNWnSRImJiRo9erQcDoevl31elZWVys3N1ZQpUxQWFqbY2FgNHDhQn3zyiTZs2KC2bdtq1KhRCg8PV1pamjIzM5WXl%2BfrZV%2BQuro6PfTQQ5o4caJnzO6Zvo3dc61Zs0a5ubm64YYbFBERoblz52ru3Lm2z/V1//d//6e1a9fq17/%2Bta1zlZWVqaysTCNGjFBYWJhiYmI0cOBA7du3z9a59uzZo4qKCuXk5Khx48bq0KGDJk2apPz8fOXl5Sk9PV3p6ekKDw/X8OHD1bFjR788OxceHq78/Hxdd9119ebOl8PhcGjixIlq166dIiIilJubq6KiIr3//vsNHcODgnUViIyM1MKFC9WiRQvPWElJieLi4uR0OtWpUyeFhIR45hISElRYWOiLpV6UqKgojR49WqGhoZKkTz/9VH/961912223yel0KiEhwevxdsklSS%2B99JLCw8M1bNgwz5jdM0nSk08%2Bqf79%2B6tnz56aN2%2BeKioqbJ3r8OHDOnjwoI4dO6YhQ4YoNTVV2dnZOnr0qK1zfdMzzzyjO%2B%2B8U9/73vdsnSs%2BPl6dO3eWw%2BFQRUWFjhw5oq1bt6p///62ziVJQUFBXp9HRUVp375935qroKCgIZd3QSZMmKBmzZqdc%2B67clRVVWn//v1e8xEREbruuut8mpOCdRUqKCjQCy%2B8oHvuuUdut1uRkZFe89HR0XK73aqrq/PRCi9OcXGxkpKSNGTIECUnJys7O/tbc7lcLh%2Bt8sJ9%2BeWXWrZsmR566CGvcTtnkqSuXbsqLS1NW7dulcPh0J49e/Twww/bOtehQ4ckSa%2B99prWrl2rV199VYcOHdLcuXNtnevrDh48qK1bt%2BpnP/uZJHsfh8HBwVq2bJn%2B/ve/q3v37kpLS1NNTY1mzpxp61zdunVT48aN9cwzz6iyslKff/65/vjHP%2BrYsWNyu92KioryenxUVJQtcn3dd%2BU4duyYLMvyu5wUrKvMO%2B%2B8o0mTJmnmzJlKS0v71sd987shf9a6dWsVFBTotdde04EDB/TrX//a10u6LAsXLtTIkSPVvn17Xy/FKIfDodGjRyssLEzt2rXTrFmztHHjRp0%2BfdrXS7tklmVJkiZPnqz4%2BHi1atVKM2bM0Pbt2328MnNefPFFDRo0SC1btvT1Ui7bqVOnNHXqVN166616%2B%2B239Y9//EPNmjXTrFmzfL20yxIVFaXnnntOu3fvVp8%2BffSrX/1KI0aM8FyZOHuc2t35cvhbTgrWVWT79u26%2B%2B67df/992vChAmSpNjY2HoN3%2B12Kzo6WsHB9jk8goKC1LZtW%2BXm5mrjxo0KDQ2V2%2B32eozL5VJsbKyPVnhhdu/erffee0/Tp0%2BvNxcTE2PLTN/m%2B9//vmpraxUcHGzbXGcvu3/9zEfr1q1lWZZOnz5t21xft2XLFmVmZno%2Bt/NxuHv3bh08eFD33nuvmjVrpvj4eGVnZ%2Bt//ud/bH0cSlLPnj2Vl5end999Vw6HQ9HR0YqPjz/n18vtdtsm11nflePsv1fnmm/evHlDLtOLff4FxWV59913NXv2bD3zzDO6/fbbPeNJSUn66KOPVFNT4xkrKChQSkqKL5Z5UXbv3q3Bgwd7Xco8Wwq7dOlS796JwsJCv8%2B1fv16HTlyRBkZGUpNTdXIkSMlSampqerYsaMtM0lnfjR%2B0aJFXmNFRUUKCwtTenq6bXO1atVKERER2rdvn2esuLhY11xzja1znbVv3z4VFxerT58%2BnrHk5GTb5qqtrVVdXZ3XmY5Tp05JktLS0mybq7q6Wn/961914sQJz9ibb76pbt26KSkpqV4uu/wd/3XflSM8PFwdOnSQ0%2Bn0zJWXl%2Bvzzz9Xly5dGnqpX/HZzy%2BiwZw%2Bfdq67bbbrJdeeqneXHV1tZWRkWE9%2B%2Byz1smTJ609e/ZYPXv2tHbs2NHwC71I5eXlVlpamrVo0SLr5MmT1pEjR6xJkyZZP/7xj60vv/zS6tatm/Xyyy9bVVVV1s6dO60uXbpY%2B/bt8/Wyv5Pb7bZKSko8f9577z2rY8eOVklJiVVcXGzLTJZlWYcOHbK6du1qrVq1yqqurrY%2B/fRTa8iQIdajjz5q26/VWY899pg1YMAA68CBA9aXX35pjR071rrvvvtsn8uyLCs/P9/q3bu315idcx09etTq3bu39dRTT1knT560jh49ak2dOtX6r//6L1vnqq2ttTIzM60nnnjCOn36tPX6669bKSkpVmFhofXRRx9ZycnJ1o4dO6yqqiorLy/P6tatm1VaWurrZX%2Br2bNn13ubhvPl%2BOMf/2j179/f8zYN8%2BbNs%2B68805fLN%2BDgnUV%2BM9//mN17NjRSkpKqvfn4MGD1kcffWSNGzfOSkpKsvr372%2B9%2BOKLvl7yBfvwww%2Bt8ePHW126dLF%2B%2BMMfWjk5OdahQ4csy7Ksf//739bw4cOtxMREa9CgQbZ4b69v%2BuKLLzzvg2VZ9s7073//2xo7dqzVtWtXq3fv3tbChQutqqoqz5xdc1VXV1vz58%2B3evXqZXXt2tWaPXu2deLECcuy7J3Lsixr5cqV1tChQ%2BuN2zlXQUGBNX78eKtnz55WWlpawPyd8cEHH1h33HGH1aVLF2vQoEHW1q1bPXNbtmyxBg0aZCUmJlojRoyw/v3vf/twpd/u7L9LN954o3XjjTd6Pj/ru3LU1dVZzzzzjHXTTTdZXbp0se666y6fv9dXkGX52V1hAAAANsc9WAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABg2P8DXz1SNEKabxwAAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-1126928340026350280\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">31</td>\n",
              "        <td class=\"number\">1947</td>\n",
              "        <td class=\"number\">4.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">32</td>\n",
              "        <td class=\"number\">1846</td>\n",
              "        <td class=\"number\">4.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">33</td>\n",
              "        <td class=\"number\">1833</td>\n",
              "        <td class=\"number\">4.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">36</td>\n",
              "        <td class=\"number\">1780</td>\n",
              "        <td class=\"number\">4.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">35</td>\n",
              "        <td class=\"number\">1759</td>\n",
              "        <td class=\"number\">4.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">34</td>\n",
              "        <td class=\"number\">1745</td>\n",
              "        <td class=\"number\">4.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">30</td>\n",
              "        <td class=\"number\">1714</td>\n",
              "        <td class=\"number\">4.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">37</td>\n",
              "        <td class=\"number\">1475</td>\n",
              "        <td class=\"number\">3.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">29</td>\n",
              "        <td class=\"number\">1453</td>\n",
              "        <td class=\"number\">3.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">39</td>\n",
              "        <td class=\"number\">1432</td>\n",
              "        <td class=\"number\">3.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (68)</td>\n",
              "        <td class=\"number\">24204</td>\n",
              "        <td class=\"number\">58.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-1126928340026350280\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">17</td>\n",
              "        <td class=\"number\">5</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">18</td>\n",
              "        <td class=\"number\">28</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:28%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">19</td>\n",
              "        <td class=\"number\">42</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:41%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">20</td>\n",
              "        <td class=\"number\">65</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:64%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">21</td>\n",
              "        <td class=\"number\">102</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">91</td>\n",
              "        <td class=\"number\">2</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92</td>\n",
              "        <td class=\"number\">4</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">94</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">95</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">98</td>\n",
              "        <td class=\"number\">2</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_job\">job<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>12</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable2034808689726191035\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>admin.</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:70%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 25.3%\">\n",
              "            10422\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>blue-collar</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:63%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 22.5%\">\n",
              "            9254\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>technician</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:46%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 16.4%\">\n",
              "            6743\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"other\">\n",
              "    <th>Other values (9)</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 35.9%\">\n",
              "            14769\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable2034808689726191035, #minifreqtable2034808689726191035\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable2034808689726191035\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">admin.</td>\n",
              "        <td class=\"number\">10422</td>\n",
              "        <td class=\"number\">25.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">blue-collar</td>\n",
              "        <td class=\"number\">9254</td>\n",
              "        <td class=\"number\">22.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:88%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">technician</td>\n",
              "        <td class=\"number\">6743</td>\n",
              "        <td class=\"number\">16.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:65%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">services</td>\n",
              "        <td class=\"number\">3969</td>\n",
              "        <td class=\"number\">9.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:38%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">management</td>\n",
              "        <td class=\"number\">2924</td>\n",
              "        <td class=\"number\">7.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:28%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">retired</td>\n",
              "        <td class=\"number\">1720</td>\n",
              "        <td class=\"number\">4.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:17%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">entrepreneur</td>\n",
              "        <td class=\"number\">1456</td>\n",
              "        <td class=\"number\">3.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">self-employed</td>\n",
              "        <td class=\"number\">1421</td>\n",
              "        <td class=\"number\">3.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">housemaid</td>\n",
              "        <td class=\"number\">1060</td>\n",
              "        <td class=\"number\">2.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">unemployed</td>\n",
              "        <td class=\"number\">1014</td>\n",
              "        <td class=\"number\">2.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (2)</td>\n",
              "        <td class=\"number\">1205</td>\n",
              "        <td class=\"number\">2.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_marital\">marital<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>4</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable2545835316785735679\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>married</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 60.5%\">\n",
              "            24928\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>single</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:46%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 28.1%\">\n",
              "            11568\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>divorced</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:19%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 11.2%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        4612\n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable2545835316785735679, #minifreqtable2545835316785735679\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable2545835316785735679\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">married</td>\n",
              "        <td class=\"number\">24928</td>\n",
              "        <td class=\"number\">60.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">single</td>\n",
              "        <td class=\"number\">11568</td>\n",
              "        <td class=\"number\">28.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:46%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">divorced</td>\n",
              "        <td class=\"number\">4612</td>\n",
              "        <td class=\"number\">11.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:19%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">unknown</td>\n",
              "        <td class=\"number\">80</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_education\">education<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>8</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable5005924472260110573\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>university.degree</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:90%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 29.5%\">\n",
              "            12168\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>high.school</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:70%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 23.1%\">\n",
              "            9515\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>basic.9y</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:45%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 14.7%\">\n",
              "            6045\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"other\">\n",
              "    <th>Other values (5)</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 32.7%\">\n",
              "            13460\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5005924472260110573, #minifreqtable5005924472260110573\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable5005924472260110573\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">university.degree</td>\n",
              "        <td class=\"number\">12168</td>\n",
              "        <td class=\"number\">29.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">high.school</td>\n",
              "        <td class=\"number\">9515</td>\n",
              "        <td class=\"number\">23.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:78%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">basic.9y</td>\n",
              "        <td class=\"number\">6045</td>\n",
              "        <td class=\"number\">14.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">professional.course</td>\n",
              "        <td class=\"number\">5243</td>\n",
              "        <td class=\"number\">12.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:43%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">basic.4y</td>\n",
              "        <td class=\"number\">4176</td>\n",
              "        <td class=\"number\">10.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">basic.6y</td>\n",
              "        <td class=\"number\">2292</td>\n",
              "        <td class=\"number\">5.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:19%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">unknown</td>\n",
              "        <td class=\"number\">1731</td>\n",
              "        <td class=\"number\">4.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">illiterate</td>\n",
              "        <td class=\"number\">18</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_default\">default<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>3</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable4526252739045352928\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>no</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 79.1%\">\n",
              "            32588\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>unknown</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:27%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 20.9%\">\n",
              "            8597\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>yes</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:1%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 0.0%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        3\n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable4526252739045352928, #minifreqtable4526252739045352928\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable4526252739045352928\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">no</td>\n",
              "        <td class=\"number\">32588</td>\n",
              "        <td class=\"number\">79.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">unknown</td>\n",
              "        <td class=\"number\">8597</td>\n",
              "        <td class=\"number\">20.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:27%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">yes</td>\n",
              "        <td class=\"number\">3</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_housing\">housing<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>3</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-1618972988234329891\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>yes</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 52.4%\">\n",
              "            21576\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>no</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:86%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 45.2%\">\n",
              "            18622\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>unknown</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:5%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 2.4%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        990\n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-1618972988234329891, #minifreqtable-1618972988234329891\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-1618972988234329891\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">yes</td>\n",
              "        <td class=\"number\">21576</td>\n",
              "        <td class=\"number\">52.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">no</td>\n",
              "        <td class=\"number\">18622</td>\n",
              "        <td class=\"number\">45.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">unknown</td>\n",
              "        <td class=\"number\">990</td>\n",
              "        <td class=\"number\">2.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_loan\">loan<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>3</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-453960902650165002\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>no</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 82.4%\">\n",
              "            33950\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>yes</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:19%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 15.2%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        6248\n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>unknown</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:3%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 2.4%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        990\n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-453960902650165002, #minifreqtable-453960902650165002\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-453960902650165002\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">no</td>\n",
              "        <td class=\"number\">33950</td>\n",
              "        <td class=\"number\">82.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">yes</td>\n",
              "        <td class=\"number\">6248</td>\n",
              "        <td class=\"number\">15.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:19%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">unknown</td>\n",
              "        <td class=\"number\">990</td>\n",
              "        <td class=\"number\">2.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_contact\">contact<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>2</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-5042288878872222697\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>cellular</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 63.5%\">\n",
              "            26144\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>telephone</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:57%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 36.5%\">\n",
              "            15044\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-5042288878872222697, #minifreqtable-5042288878872222697\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-5042288878872222697\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">cellular</td>\n",
              "        <td class=\"number\">26144</td>\n",
              "        <td class=\"number\">63.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">telephone</td>\n",
              "        <td class=\"number\">15044</td>\n",
              "        <td class=\"number\">36.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_month\">month<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>10</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable5785334131250502880\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>may</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:97%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 33.4%\">\n",
              "            13769\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>jul</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:51%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 17.4%\">\n",
              "            7174\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>aug</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:44%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 15.0%\">\n",
              "            6178\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"other\">\n",
              "    <th>Other values (7)</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 34.2%\">\n",
              "            14067\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable5785334131250502880, #minifreqtable5785334131250502880\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable5785334131250502880\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">may</td>\n",
              "        <td class=\"number\">13769</td>\n",
              "        <td class=\"number\">33.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">jul</td>\n",
              "        <td class=\"number\">7174</td>\n",
              "        <td class=\"number\">17.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:52%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">aug</td>\n",
              "        <td class=\"number\">6178</td>\n",
              "        <td class=\"number\">15.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:45%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">jun</td>\n",
              "        <td class=\"number\">5318</td>\n",
              "        <td class=\"number\">12.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:39%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">nov</td>\n",
              "        <td class=\"number\">4101</td>\n",
              "        <td class=\"number\">10.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">apr</td>\n",
              "        <td class=\"number\">2632</td>\n",
              "        <td class=\"number\">6.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:19%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">oct</td>\n",
              "        <td class=\"number\">718</td>\n",
              "        <td class=\"number\">1.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">sep</td>\n",
              "        <td class=\"number\">570</td>\n",
              "        <td class=\"number\">1.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">mar</td>\n",
              "        <td class=\"number\">546</td>\n",
              "        <td class=\"number\">1.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">dec</td>\n",
              "        <td class=\"number\">182</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_day_of_week\">day_of_week<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>5</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-2599872795951646894\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>thu</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:54%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 20.9%\">\n",
              "            8623\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>mon</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:53%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 20.7%\">\n",
              "            8514\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>wed</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:51%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 19.7%\">\n",
              "            8134\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"other\">\n",
              "    <th>Other values (2)</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 38.6%\">\n",
              "            15917\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-2599872795951646894, #minifreqtable-2599872795951646894\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-2599872795951646894\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">thu</td>\n",
              "        <td class=\"number\">8623</td>\n",
              "        <td class=\"number\">20.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">mon</td>\n",
              "        <td class=\"number\">8514</td>\n",
              "        <td class=\"number\">20.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:98%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">wed</td>\n",
              "        <td class=\"number\">8134</td>\n",
              "        <td class=\"number\">19.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:94%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">tue</td>\n",
              "        <td class=\"number\">8090</td>\n",
              "        <td class=\"number\">19.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:93%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">fri</td>\n",
              "        <td class=\"number\">7827</td>\n",
              "        <td class=\"number\">19.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:90%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_duration\">duration<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>1544</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>3.7%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>258.29</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>4918</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram-1056769195335953366\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABEklEQVR4nO3XwQnCQABFQRVLShHpyXN6soj0tN5FHkaUBJm5L/zLY3fPY4xxAl667D0Ajuy694Bn0%2B2%2B%2Bcy6zD9YAm4QSAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAjXvQd8w3S7bz6zLvMPlvBv3CAQBAJBIBD%2B4g/yCf8W3nEeY4y9R8BReWJBEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAeADsixGT7cl2XwAAAABJRU5ErkJggg%3D%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-1056769195335953366,#minihistogram-1056769195335953366\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives-1056769195335953366\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-1056769195335953366\"\n",
              "                                                  aria-controls=\"quantiles-1056769195335953366\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram-1056769195335953366\" aria-controls=\"histogram-1056769195335953366\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common-1056769195335953366\" aria-controls=\"common-1056769195335953366\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme-1056769195335953366\" aria-controls=\"extreme-1056769195335953366\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-1056769195335953366\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>36</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>102</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>180</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>319</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>752.65</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>4918</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>4918</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>217</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>259.28</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>1.0038</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>20.248</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>258.29</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>171.67</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>3.2631</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>10638243</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>67226</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-1056769195335953366\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nO3deXhU5cH%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%2Bs6sy4guXHAgMDFBAQoMDAgKZeSrNFxt5Fvt5Hxt5Hxt7lr/lSsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMFtTLwDn5qqZ7zX1EhptfXbfpl4CAADnBVewAAAADKNgAQAAGEbBAgAAMIyCBQAAYJjPFqzdu3frjjvuUM%2BePZWamqrs7GyVlZXpr3/9q%2BLi4pSUlOTxtX79eve2y5cv17Bhw9SjRw9lZGSooKDAPXf8%2BHE9%2Buij6t%2B/v1JSUpSVlSWn0%2BmeLy4u1t13362UlBQNHDhQ8%2BbNU0NDw3k9dwAA4N98smCdOHFCd955p3r37q0dO3Zo7dq1Onz4sGbPni1JiomJkcPh8Pi67rrrJEmbNm3SwoUL9etf/1rbt2/XwIEDdc899%2BjYsWOSpAULFqiwsFD5%2BfnasGGDLMvSI4884j72fffdp3bt2mnjxo16%2BeWXtXHjRr3yyivnPQMAAOC/fLJgVVdXKycnR5MnT1aLFi0UGRmpIUOG6Msvvzzjtvn5%2BRo9erS6deumli1batKkSZKkzZs3q66uTitXrtTUqVN16aWXKjw8XNnZ2dqyZYu%2B%2BuorORwO7d69W9OmTVNoaKhiY2M1YcIE5efne/uUAQBAM%2BKTBSssLExjxoyRzfbNx3Tt3btXb7/9tvsqVVVVlTIzM5WSkqJrrrlGL7/8sizLkiQVFhYqPj7eva/AwEB17dpVDodDBw4cUEVFhRISEtzzHTt2VMuWLVVYWKjCwkLFxMQoLCzMPZ%2BQkKB9%2B/apsrLyfJw6AABoBnz6g0aLi4s1bNgw1dXVaezYscrKytLu3bvVpUsX3XHHHVqwYIE%2B%2Bugj3X///QoNDVV6erpcLpdHQZK%2BKWxOp1Mul0uSZLfbPebtdrt7/rtzJ/fldDoVEhLSqHWXlpaqrKzMY8xma6Xo6OizOv8zCQryyX78vWw2/1qv9G3G/pa1vyBf7yNj7yNj7/LXfH26YJ181uqf//ynHn30UT300EOaP3%2B%2BXn31Vfdr%2BvXrp/Hjx%2Butt95Senq6JLmvZn2fH5o/07aNkZ%2Bfr0WLFnmMZWZmKisr65z37c8iIlo39RJ%2BNLv94qZeQrNGvt5Hxt5Hxt7lb/n6dMGSpICAAMXGxionJ0fjx4/XzJkzFRkZ6fGamJgYbdiwQZIUERHhvlJ1ksvlUufOnd3buVwutW797X/sjx49qqioKNXX159224CAgFOO%2BUPGjRunQYMGeYzZbK3kdFY1eh%2BN4W9t3vT5nw9BQYGy2y9WeXm16ut5N6lp5Ot9ZOx9ZOxd55pvU/1w75MFa8eOHZo9e7bWr1%2BvwMBvSsTJ/926dauqq6t1yy23uF%2B/d%2B9etW/fXpKUmJiowsJCjRo1SpJUX1%2BvnTt3Kj09Xe3bt1dYWJj7WStJ%2BuKLL3TixAklJiaqtLRUJSUlOnLkiLtQORwOderUyaOQnUl0dPQptwPLyipUV3dhf%2BP58/nX1zf49fp9Hfl6Hxl7Hxl7l7/l65OXQBITE1VZWal58%2BapurpaR44c0cKFC3XVVVcpNDRUzz77rD788EPV1tbqz3/%2Bs1atWqWMjAxJUkZGht555x19%2Bumnqq6u1pIlS9SiRQsNGDBAQUFBGjt2rJYuXaqSkhI5nU4999xzGjJkiNq0aaP4%2BHglJSVp/vz5qqysVFFRkV5%2B%2BWX3vgEAABrDJ69ghYaG6qWXXtJTTz2lPn36qFWrVurTp4%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%2B/dXSkqKsrKy5HQ63fPFxcW6%2B%2B67lZKSooEDB2revHlqaGg4PycNAACaBZ8sWCdOnNCdd96p3r17a8eOHVq7dq0OHz6s2bNnq7S0VFOnTtX48eO1Y8cOzZw5U7NmzZLD4ZAkbdq0SQsXLtSvf/1rbd%2B%2BXQMHDtQ999yjY8eOSZIWLFigwsJC5efna8OGDbIsS4888oj72Pfdd5/atWunjRs36uWXX9bGjRv1yiuvNEkOAADAP/lkwaqurlZOTo4mT56sFi1aKDIyUkOGDNGXX36pNWvWKDY2Vunp6QoODlZqaqoGDRqkFStWSJLy8/M1evRodevWTS1bttSkSZMkSZs3b1ZdXZ1WrlypqVOn6tJLL1V4eLiys7O1ZcsWffXVV3I4HNq9e7emTZum0NBQxcbGasKECcrPz2/KOAAAgJ/xyYIVFhamMWPGyGazSZL27t2rt99%2BW9ddd50KCwsVHx/v8fr4%2BHj3bcDvzgcGBqpr165yOBw6cOCAKioqlJCQ4J7v2LGjWrZsqcLCQhUWFiomJkZhYWHu%2BYSEBO3bt0%2BVlZXePGUAANCM2Jp6AT%2BkuLhYw4YNU11dncaOHausrCzdddddateuncfrwsPD3c9RuVwuj4IkfVPYnE6nXC6XJMlut3vM2%2B129/x3507uy%2Bl0KiQkpFHrLi0tdT8vdpLN1krR0dGN2r6xgoJ8sh9/L5vNv9YrfZuxv2XtL8jX%2B8jY%2B8jYu/w1X58uWDExMXI4HPrnP/%2BpRx99VA899FCjtrMs60fPn2nbxsjPz9eiRYs8xjIzM5WVlXXO%2B/ZnERGtm3oJP5rdfnFTL6FZI1/vI2PvI2Pv8rd8fbpgSVJAQIBiY2OVk5Oj8ePHKy0tzX0l6iSn06nIyEhJUkRExCnzLpdLnTt3dr/G5XKpdetv/2N/9OhRRUVFqb6%2B/rTbBgQEuLdtjHHjxmnQoEEeYzZbKzmdVY3eR2P4W5s3ff7nQ1BQoOz2i1VeXq36et5Nahr5eh8Zex8Ze9e55ttUP9z7ZMHasWOHZs%2BerfXr1ysw8JsScfJ/k5OTtWHDBo/XFxQUqFu3bpKkxMREFRYWatSoUZKk%2Bvp67dy5U%2Bnp6Wrfvr3CwsLcz1pJ0hdffKETJ04oMTFRpaWlKikp0ZEjR9yFyuFwqFOnTh6F7Eyio6NPuR1YVlahuroL%2BxvPn8%2B/vr7Br9fv68jX%2B8jY%2B8jYu/wtX5%2B8BJKYmKjKykrNmzdP1dXVOnLkiBYuXKirrrpKGRkZKi4u1ooVK3T8%2BHFt3bpVW7du1dixYyVJGRkZeuedd/Tpp5%2BqurpaS5YsUYsWLTRgwAAFBQVp7NixWrp0qUpKSuR0OvXcc89pyJAhatOmjeLj45WUlKT58%2BersrJSRUVFevnll5WRkdHEiQAAAH/ikwUrNDRUL730kgoKCtSnTx8NHz5coaGheu655xQVFaVly5bptddeU8%2BePfXMM89o3rx5uuKKKyRJ/fv31wMPPKDs7Gz17t1b27dvV15enlq2bClJysrKUrdu3XTjjTdq8ODBat26tZ5%2B%2Bmn3sV988UWVlpaqb9%2B%2Buv3223XTTTfplltuaZIcAACAfwqwTDzVjTMqK6swvk%2BbLVBDcrcZ36%2B3rM/u29RLOGs2W6AiIlrL6azyq0vT/oJ8vY%2BMvY%2BMvetc823bNtQLqzozn7yCBQAA4M8oWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhPluwiouLlZmZqZSUFKWmpurhhx9WeXm5/vWvfykuLk5JSUkeX7/73e/c265bt04jRoxQ9%2B7dNXr0aH344YfuuYaGBi1YsECDBw9Wr169NHHiRB08eNA973K5lJ2drdTUVPXr108zZ85UTU3NeT13AADg33y2YN1zzz2y2%2B3atGmT3nrrLX355Zd69tln3fMOh8Pja%2BLEiZKkXbt2afr06Zo2bZr%2B8pe/aMKECbr33nt16NAhSdLrr7%2BuNWvWKC8vT5s3b1ZsbKwyMzNlWZYkadasWaqurtbatWu1atUqFRUVKTc39/wHAAAA/JZPFqzy8nIlJibqwQcfVOvWrXXJJZdo1KhR%2Btvf/nbGbVesWKG0tDSlpaUpODhYI0eOVJcuXbR69WpJUn5%2BviZMmKCOHTsqJCREOTk5Kioq0meffaavv/5aGzduVE5OjiIjI9WuXTtNnTpVq1atUm1trbdPGwAANBO2pl7A6djtds2ZM8djrKSkRNHR0e4/P/TQQ9q%2Bfbvq6uo0ZswYZWVl6aKLLlJhYaHS0tI8to2Pj5fD4VBNTY327Nmj%2BPh491xISIguv/xyORwOVVRUKCgoSHFxce75hIQEHTt2THv37vUY/yGlpaUqKyvzGLPZWnms34SgIJ/sx9/LZvOv9UrfZuxvWfsL8vU%2BMvY%2BMvYuf83XJwvWdzkcDr322mtasmSJWrRooe7du2vIkCF6%2BumntWvXLt13332y2Wy6//775XK5FBYW5rF9WFiY9uzZo6NHj8qyrNPOO51OhYeHKyQkRAEBAR5zkuR0Ohu93vz8fC1atMhjLDMzU1lZWWd76s1KRETrpl7Cj2a3X9zUS2jWyNf7yNj7yNi7/C1fny9YH3/8saZMmaIHH3xQqampkqQ33njDPZ%2BcnKzJkydr2bJluv/%2B%2ByXJ/TzV9/mh%2BTNt2xjjxo3ToEGDPMZstlZyOqvOed//yd/avOnzPx%2BCggJlt1%2Bs8vJq1dc3NPVymh3y9T4y9j4y9q5zzbepfrj36YK1adMm/fKXv9SsWbN00003fe/rYmJi9PXXX8uyLEVERMjlcnnMu1wuRUZGKjw8XIGBgaedj4qKUmRkpCorK1VfX6%2BgoCD3nCRFRUU1et3R0dGn3A4sK6tQXd2F/Y3nz%2BdfX9/g1%2Bv3deTrfWTsfWTsXf6Wr89eAvn73/%2Bu6dOn64UXXvAoVzt27NCSJUs8Xrt3717FxMQoICBAiYmJKigo8Jh3OBzq1q2bgoOD1blzZxUWFrrnysvLdeDAASUnJ6tr166yLEu7d%2B/22NZut6tDhw5eOlMAANDc%2BGTBqqur069%2B9StNmzZN/fr185gLDQ3V4sWL9Yc//EG1tbVyOBz63e9%2Bp4yMDEnS2LFjtX37dm3ZskXHjx/XypUrtX//fo0cOVKSlJGRoeXLl6uoqEiVlZXKzc1V165dlZSUpMjISA0bNkzPP/%2B8jhw5okOHDmnx4sVKT0%2BXzebTF/sAAIAPCbBMPHRk2N/%2B9jf913/9l1q0aHHK3HvvvaedO3dq0aJF2r9/v0JDQ3XbbbfprrvuUmDgN33x/fff1/z581VcXKxOnTpp5syZ6tWrl6RvnrFauHCh3njjDVVVVSklJUVPPPGELrnkEklSRUWFHnvsMW3evFkXXXSRbrjhBj388MOnXcvZKCurOKftT8dmC9SQ3G3G9%2Bst67P7NvUSzprNFqiIiNZyOqv86tK0vyBf7yNj7yNj7zrXfNu2DfXCqs7MJwtWc0TBomDhVOTrfWTsfWTsXf5asHzyFiEAAIA/o2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMMx4wRo0aJAWLVqkkpIS07sGAADwC8YL1s0336x169bp2muv1aRJk/T%2B%2B%2B%2Brrq7O9GEAAAB8lvGClZmZqXXr1unNN99U586d9cwzzygtLU3z5s3Tvn37TB8OAADA53jtGayEhARNnz5dmzdv1owZM/Tmm2/q%2Buuv18SJE/WPf/zDW4cFAABocl4rWLW1tVq3bp3uuusuTZ8%2BXe3atdMjjzyirl27asKECVqzZo23Dg0AANCkbKZ3WFRUpJUrV%2Bqdd95RVVWVhg0bpldeeUU9e/Z0v6ZXr16aPXu2RowYYfrwAAAATc54wRo%2BfLg6dOigyZMn66abblJ4ePgpr0lLS9ORI0dMHxoAAMAnGC9Yy5cvV%2B/evc/4us8%2B%2B8z0oQEAAHyC8Wew4uLidM8992jjxo3usf/5n//RXXfdJZfLZfpwAAAAPsd4wZozZ44qKirUqVMn99iAAQPU0NCguXPnmj4cAACAzzF%2Bi/DDDz/UmjVrFBER4R6LjY1Vbm6ubrjhBtOHAwAA8DnGr2DV1NQoODj41AMFBqq6utr04QAAAHyO8YLVq1cvzZ07V0ePHnWPffXVV3r88cc9PqoBAACguTJ%2Bi3DGjBm68847dfXVVyskJEQNDQ2qqqpS%2B/bt9eqrr5o%2BHAAAgM8xXrDat2%2Bvd999V3/605904MABBQYGqkOHDurXr5%2BCgoJMHw4AAMDnGC9YktSiRQtde%2B213tg1AACAzzNesA4ePKj58%2Bfryy%2B/VE1NzSnzH3zwgelDAgAA%2BBSvPINVWlqqfv36qVWrVqZ3DwAA4POMF6yCggJ98MEHioyMNL1rAAAAv2D8YxqioqK4cgUAAC5oxgvW5MmTtWjRIlmWdU77KS4uVmZmplJSUpSamqqHH35Y5eXlkqRdu3bp1ltvVc%2BePTV06FC99NJLHtuuW7dOI0aMUPfu3TV69Gh9%2BOGH7rmGhgYtWLBAgwcPVq9evTRx4kQdPHjQPe9yuZSdna3U1FT169dPM2fOPO2zZAAAAN/HeMH605/%2BpLffflt9%2B/bV2LFjNX78eI%2Bvxrrnnntkt9u1adMmvfXWW/ryyy/17LPPqqamRpMnT1afPn20bds2LViwQMuWLdP7778v6ZvyNX36dE2bNk1/%2BctfNGHCBN177706dOiQJOn111/XmjVrlJeXp82bNys2NlaZmZnuQjhr1ixVV1dr7dq1WrVqlYqKipSbm2s6JgAA0IwZL1ghISHq37%2B/0tLS1LFjR3Xo0MHjqzHKy8uVmJioBx98UK1bt9Yll1yiUaNG6W9/%2B5u2bNmi2tpaTZkyRa1atVJCQoLGjBmj/Px8SdKKFSuUlpamtLQ0BQcHa%2BTIkerSpYtWr14tScrPz9eECRPUsWNHhYSEKCcnR0VFRfrss8/09ddfa%2BPGjcrJyVFkZKTatWunqVOnatWqVaqtrTUdFQAAaKaMP%2BQ%2BZ86cc96H3W4/ZT8lJSWKjo5WYWGh4uLiPD60ND4%2BXitWrJAkFRYWKi0tzWPb%2BPh4ORwO1dTUaM%2BePYqPj3fPhYSE6PLLL5fD4VBFRYWCgoIUFxfnnk9ISNCxY8e0d%2B9ej/EfUlpaqrKyMo8xm62VoqOjGxdAIwUFGe/HXmWz%2Bdd6pW8z9res/QX5eh8Zex8Ze5e/5uuVDxrdu3ev3n33Xf373/92F6VPPvlE3bt3/1H7czgceu2117RkyRKtX79edrvdYz48PFwul0sNDQ1yuVwKCwvzmA8LC9OePXt09OhRWZZ12nmn06nw8HCFhIQoICDAY06SnE5no9ebn5%2BvRYsWeYxlZmYqKyur0ftojiIiWjf1En40u/3ipl5Cs0a%2B3kfG3kfG3uVv%2BRovWDt27NBdd92lDh06aP/%2B/ZozZ44OHjyo22%2B/Xc8//7wGDx58Vvv7%2BOOPNWXKFD344INKTU3V%2BvXrT/u6/yxFZ3rA/ofmz/XhfEkaN26cBg0a5DFms7WS01l1zvv%2BT/7W5k2f//kQFBQou/1ilZdXq76%2BoamX0%2ByQr/eRsfeRsXeda75N9cO98YK1YMEC/fKXv9Qdd9yh5ORkSd/8fsK5c%2Bdq8eLFZ1WwNm3apF/%2B8peaNWuWbrrpJklSZGSk9u/f7/E6l8ul8PBwBQYGKiIiQi6X65T5yMhI92tONx8VFaXIyEhVVlaqvr7efQvy5GujoqIave7o6OhTbgeWlVWoru7C/sbz5/Ovr2/w6/X7OvL1PjL2PjL2Ln/L1/glkC%2B%2B%2BEIZGRmSPK8q/fznP1dRUVGj9/P3v/9d06dP1wsvvOAuV5KUmJiozz//XHV1de4xh8Ohbt26uecLCgo89nVyPjg4WJ07d1ZhYaF7rry8XAcOHFBycrK6du0qy7K0e/duj23tdnujH9AHAAAwXrBCQ0NP%2B7lRpaWlatGiRaP2UVdXp1/96leaNm2a%2BvXr5zGXlpamkJAQLVmyRNXV1frss8%2B0cuVKd6kbO3astm/fri1btuj48eNauXKl9u/fr5EjR0qSMjIytHz5chUVFamyslK5ubnq2rWrkpKSFBkZqWHDhun555/XkSNHdOjQIS1evFjp6emy2bzyuBoAAGiGjBesHj166JlnnlFlZaV7bN%2B%2BfZo%2BfbquvvrqRu3j008/VVFRkZ566iklJSV5fJWVlWnp0qXavn27evfurezsbOXk5GjAgAGSpC5duig3N1dz5sxRz5499dprr2nZsmVq27atJGn8%2BPEaNWqUbrvtNvXt21eHDh3yeCD9iSeeUGhoqAYPHqyRI0cqOTlZOTk55gICAADNXoBl4qnu/3Do0CHdcccd%2Bte//qX6%2Bnq1atVK1dXV6ty5s5YuXaqf/OQnJg/nN8rKKozv02YL1JDcbcb36y3rs/s29RLOms0WqIiI1nI6q/zq3r%2B/IF/vI2PvI2PvOtd827YN9cKqzsz4fa9LLrlEa9eu1datW7Vv3z61bNlSHTp0UN%2B%2BfT2eyQIAAGiuvPJg0UUXXaRrr73WG7sGAADwecYL1qBBg37wStUHH3xg%2BpAAAAA%2BxXjBuv766z0KVn19vfbt2yeHw6E77rjD9OEAAAB8jvGCNW3atNOOb9iwQX/9619NHw4AAMDnnLfftXLttdfq3XffPV%2BHAwAAaDLnrWDt3LnTyO/5AwAA8HXGbxGOHz/%2BlLHq6moVFRVp6NChpg8HAADgc4wXrNjY2FPeRRgcHKz09HSNGTPG9OEAAAB8jvGCNXfuXNO7BAAA8CvGC9Y777zT6NfedNNNpg8PAADQ5IwXrJkzZ6qhoeGUB9oDAgI8xgICAihYAACgWTJesH7729/qpZde0j333KO4uDhZlqXPP/9cv/nNb3TrrbcqJSXF9CEBAAB8ileewcrLy1O7du3cY1dddZXat2%2BviRMnau3ataYPCQAA4FOMfw7W/v37FRYWdsq43W5XcXGx6cMBAAD4HOMFKyYmRnPnzpXT6XSPlZeXa/78%2BfrpT39q%2BnAAAAA%2Bx/gtwhkzZujBBx9Ufn6%2BWrdurcDAQFVWVqply5ZavHix6cMBAAD4HOMFq1%2B/ftqyZYu2bt2qQ4cOybIstWvXTtdcc41CQ0NNHw4AAMDnGC9YknTxxRdr8ODBOnTokNq3b%2B%2BNQwAAAPgs489g1dTUaPr06erevbuuu%2B46Sd88gzVp0iSVl5ebPhwAAIDPMV6w5s2bp127dik3N1eBgd/uvr6%2BXrm5uaYPBwAA4HOMF6wNGzboxRdf1M9//nP3L3222%2B2aM2eO3n//fdOHAwAA8DnGC1ZVVZViY2NPGY%2BMjNSxY8dMHw4AAMDnGC9YP/3pT/XXv/5Vkjx%2B9%2BB7772nn/zkJ6YPBwAA4HOMv4vwlltu0X333aebb75ZDQ0Nevnll1VQUKANGzZo5syZpg8HAADgc4wXrHHjxslms%2Bm1115TUFCQli5dqg4dOig3N1c///nPTR8OAADA5xgvWEeOHNHNN9%2Bsm2%2B%2B2fSuAQAA/ILxZ7AGDx7s8ewVAADAhcZ4wUpJSdH69etN7xYAAMBvGL9FeOmll%2Brpp59WXl6efvrTn%2Bqiiy7ymJ8/f77pQwIAAPgU4wVrz549%2BtnPfiZJcjqdpncPAADg84wVrJycHC1YsECvvvqqe2zx4sXKzMz80fvctm2bpk%2BfrpSUFC1YsMA9/tZbb2nGjBmnXB17/fXXlZycrIaGBr3wwgtau3atysvLlZycrNmzZ7t/8bTL5dLs2ZXC1eAAABxKSURBVLP10UcfKTAwUGlpaZo1a5ZatmwpSdq1a5eefvpp7dq1S1FRURo/frzuvPPOH30eAADgwmLsGaxNmzadMpaXl/ej9/eb3/xGTz31lC6//PLTzvfq1UsOh8PjKzk5WdI3RWvNmjXKy8vT5s2bFRsbq8zMTPfD97NmzVJ1dbXWrl2rVatWqaioyP17EmtqajR58mT16dNH27Zt04IFC7Rs2TJ%2BzQ8AAGg0YwXrdO8cPJd3EwYHB2vlypXfW7B%2BSH5%2BviZMmKCOHTsqJCREOTk5Kioq0meffaavv/5aGzduVE5OjiIjI9WuXTtNnTpVq1atUm1trbZs2aLa2lpNmTJFrVq1UkJCgsaMGaP8/PwffS4AAODCYuwW4clf7Hymsca6/fbbf3C%2BpKREv/jFL1RQUCC73a6srCzdeOONqqmp0Z49exQfH%2B9%2BbUhIiC6//HI5HA5VVFQoKChIcXFx7vmEhAQdO3ZMe/fuVWFhoeLi4hQUFOSej4%2BP14oVKxq99tLSUpWVlXmM2WytFB0d3eh9NEZQkPE3gXqVzeZf65W%2BzdjfsvYX5Ot9ZOx9ZOxd/pqv8Yfcz4fIyEjFxsbqgQceUKdOnfTHP/5RDz30kKKjo/Wzn/1MlmUpLCzMY5uwsDA5nU6Fh4crJCTEo/ydfK3T6ZTL5ZLdbvfYNjw8XC6XSw0NDQoMPPM/4Pz8fC1atMhjLDMzU1lZWT/2lJuFiIjWTb2EH81uv7ipl9Cska/3kbH3kbF3%2BVu%2BflmwBgwYoAEDBrj/PHz4cP3xj3/UW2%2B9pWnTpkn64duTP%2BbW5dlcjRs3bpwGDRrkMWaztZLTWXXWx/0h/tbmTZ//%2BRAUFCi7/WKVl1ervr6hqZfT7JCv95Gx95Gxd51rvk31w72xglVbW6sHH3zwjGPe%2BhysmJgYFRQUKDw8XIGBgXK5XB7zLpdLUVFRioyMVGVlperr6923AU%2B%2B9uT8/v37T9n25H4bIzo6%2BpTbgWVlFaqru7C/8fz5/OvrG/x6/b6OfL2PjL2PjL3L3/I1dgmkZ8%2BeKi0t9fg63ZgJv//977Vu3TqPsaKiIrVv317BwcHq3LmzCgsL3XPl5eU6cOCAkpOT1bVrV1mWpd27d7vnHQ6H7Ha7OnTooMTERH3%2B%2Beeqq6vzmO/WrZuRtQMAgObP2BWs//z8K287ceKEnnzySbVv315XXHGFNmzYoD/96U968803JUkZGRnKy8tT//791a5dO%2BXm5qpr165KSkqSJA0bNkzPP/%2B8nn32WZ04cUKLFy9Wenq6bDab0tLSFBISoiVLlmjSpEn64osvtHLlSs2bN%2B%2B8nR8AAPBvPvsM1skydPJK0saNGyV9czXp9ttvV1VVle6//36VlZXpsssu0%2BLFi5WYmChJGj9%2BvMrKynTbbbepqqpKKSkpHg%2BdP/HEE3rsscc0ePBgXXTRRbrhhhuUk5MjSWrRooWWLl2qxx57THl5eWrTpo1ycnI8nvkCAAD4IQHWuXxYFRqtrKzC%2BD5ttkANyd1mfL/esj67b1Mv4azZbIGKiGgtp7PKr%2B79%2Bwvy9T4y9j4y9q5zzbdt21AvrOrM/OttaAAAAH6AggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAM8%2BmCtW3bNqWmpionJ%2BeUuXXr1mnEiBHq3r27Ro8erQ8//NA919DQoAULFmjw4MHq1auXJk6cqIMHD7rnXS6XsrOzlZqaqn79%2BmnmzJmqqalxz%2B/atUu33nqrevbsqaFDh%2Bqll17y7okCAIBmxWcL1m9%2B8xs99dRTuvzyy0%2BZ27Vrl6ZPn65p06bpL3/5iyZMmKB7771Xhw4dkiS9/vrrWrNmjfLy8rR582bFxsYqMzNTlmVJkmbNmqXq6mqtXbtWq1atUlFRkXJzcyVJNTU1mjx5svr06aNt27ZpwYIFWrZsmd5///3zd/IAAMCv%2BWzBCg4O1sqVK09bsFasWKG0tDSlpaUpODhYI0eOVJcuXbR69WpJUn5%2BviZMmKCOHTsqJCREOTk5Kioq0meffaavv/5aGzduVE5OjiIjI9WuXTtNnTpVq1atUm1trbZs2aLa2lpNmTJFrVq1UkJCgsaMGaP8/PzzHQEAAPBTPluwbr/9doWGhp52rrCwUPHx8R5j8fHxcjgcqqmp0Z49ezzmQ0JCdPnll8vhcGjXrl0KCgpSXFycez4hIUHHjh3T3r17VVhYqLi4OAUFBXnsu6CgwPAZAgCA5srW1Av4MVwul8LCwjzGwsLCtGfPHh09elSWZZ123ul0Kjw8XCEhIQoICPCYkySn0ymXyyW73e6xbXh4uFwulxoaGhQYeOZOWlpaqrKyMo8xm62VoqOjz%2Bo8zyQoyGf78WnZbP61XunbjP0ta39Bvt5Hxt5Hxt7lr/n6ZcGS5H6e6sfMn2nb0/nPQnYm%2Bfn5WrRokcdYZmamsrKyzvq4zUlEROumXsKPZrdf3NRLaNbI1/vI2PvI2Lv8LV%2B/LFgRERFyuVweYy6XS5GRkQoPD1dgYOBp56OiohQZGanKykrV19e7bwOefO3J%2Bf3795%2By7cn9Nsa4ceM0aNAgjzGbrZWczqqzOc0z8rc2b/r8z4egoEDZ7RervLxa9fUNTb2cZod8vY%2BMvY%2BMvetc822qH%2B79smAlJiae8kyUw%2BHQ8OHDFRwcrM6dO6uwsFC9e/eWJJWXl%2BvAgQNKTk5WTEyMLMvS7t27lZCQ4N7WbrerQ4cOSkxM1O9//3vV1dXJZrO557t169bo9UVHR59yO7CsrEJ1dRf2N54/n399fYNfr9/Xka/3kbH3kbF3%2BVu%2B/nUJ5P%2BMHTtW27dv15YtW3T8%2BHGtXLlS%2B/fv18iRIyVJGRkZWr58uYqKilRZWanc3Fx17dpVSUlJioyM1LBhw/T888/ryJEjOnTokBYvXqz09HTZbDalpaUpJCRES5YsUXV1tT777DOtXLlSGRkZTXzWAADAX/jsFaykpCRJUl1dnSRp48aNkr65mtSlSxfl5uZqzpw5Ki4uVqdOnbRs2TK1bdtWkjR%2B/HiVlZXptttuU1VVlVJSUjyeiXriiSf02GOPafDgwbrooot0ww03uD/MtEWLFlq6dKkee%2Bwx5eXlqU2bNsrJydGAAQPO49kDAAB/FmD9mCe%2BcdbKyiqM79NmC9SQ3G3G9%2Bst67P7NvUSzprNFqiIiNZyOqv86tK0vyBf7yNj7yNj7zrXfNu2Pf1HPnmbX94iBAAA8GUULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGCY3xasuLg4JSYmKikpyf315JNPSpJ27Nih9PR09ejRQ8OHD9fq1as9tl2%2BfLmGDRumHj16KCMjQwUFBe6548eP69FHH1X//v2VkpKirKwsOZ3O83puAADAv9maegHn4r333tNll13mMVZaWqqpU6dq5syZGjFihD7%2B%2BGNNmTJFHTp0UFJSkjZt2qSFCxfqt7/9reLi4rR8%2BXLdc889ev/999WqVSstWLBAhYWFys/P18UXX6xZs2bpkUce0dKlS5voLAEAgL/x2ytY32fNmjWKjY1Venq6goODlZqaqkGDBmnFihWSpPz8fI0ePVrdunVTy5YtNWnSJEnS5s2bVVdXp5UrV2rq1Km69NJLFR4eruzsbG3ZskVfffVVU54WAADwI35dsObPn68BAwboqquu0qxZs1RVVaXCwkLFx8d7vC4%2BPt59G/C784GBgeratascDocOHDigiooKJSQkuOc7duyoli1bqrCw8PycFAAA8Ht%2Be4vwyiuvVGpqqp599lkdPHhQ2dnZevzxx%2BVyudSuXTuP14aHh7ufo3K5XAoLC/OYDwsLk9PplMvlkiTZ7XaPebvdflbPYZWWlqqsrMxjzGZrpejo6EbvozGCgvyrH9ts/rVe6duM/S1rf0G%2B3kfG3kfG3uWv%2BfptwcrPz3f//44dO2ratGmaMmWKevbsecZtLcs6p/nGrG3RokUeY5mZmcrKyjqn/fq7iIjWTb2EH81uv7ipl9Cska/3kbH3kbF3%2BVu%2Bfluwvuuyyy5TfX29AgMD3VeiTnI6nYqMjJQkRUREnDLvcrnUuXNn92tcLpdat/62DBw9elRRUVGNXsu4ceM0aNAgjzGbrZWczqqzOqcz8bc2b/r8z4egoEDZ7RervLxa9fUNTb2cZod8vY%2BMvY%2BMvetc822qH%2B79smDt3LlTq1ev1sMPP%2BweKyoqUosWLZSWlqa3337b4/UFBQXq1q2bJCkxMVGFhYUaNWqUJKm%2Bvl47d%2B5Uenq62rdvr7CwMBUWFiomJkaS9MUXX%2BjEiRNKTExs9Pqio6NPuR1YVlahuroL%2BxvPn8%2B/vr7Br9fv68jX%2B8jY%2B8jYu/wtX/%2B6BPJ/oqKilJ%2Bfr7y8PJ04cUL79u3TCy%2B8oHHjxunGG29UcXGxVqxYoePHj2vr1q3aunWrxo4dK0nKyMjQO%2B%2B8o08//VTV1dVasmSJWrRooQEDBigoKEhjx47V0qVLVVJSIqfTqeeee05DhgxRmzZtmvisAQCAv/DLK1jt2rVTXl6e5s%2Bf7y5Io0aNUk5OjoKDg7Vs2TI99dRTevzxxxUTE6N58%2BbpiiuukCT1799fDzzwgLKzs3X48GElJSUpLy9PLVu2lCRlZWWpqqpKN954o%2Brq6jRw4EDNnj27Cc8WAAD4mwDrXJ/oRqOUlVUY36fNFqghuduM79db1mf3beolnDWbLVAREa3ldFb51aVpf0G%2B3kfG3kfG3nWu%2BbZtG%2BqFVZ2ZX94iBAAA8GUULAAAAMP88hks%2BKfrnv9zUy/hrPjjLU0AgG/gChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDbE29AMBXXff8n5t6CWdlfXbfpl4CAOD/cAULAADAMAoWAACAYRSs0yguLtbdd9%2BtlJQUDRw4UPPmzVNDQ0NTLwsAAPgJnsE6jfvuu08JCQnauHGjDh8%2BrMmTJ6tNmzb6xS9%2B0dRLAwAAfoArWN/hcDi0e/duTZs2TaGhoYqNjdWECROUn5/f1EsDAAB%2BgitY31FYWKiYmBiFhYW5xxISErRv3z5VVlYqJCTkjPsoLS1VWVmZx5jN1krR0dFG1xoURD/Gt/zpXY9/nHaNpG//DvN32XvI2PvI2Lv8NV8K1ne4XC7Z7XaPsZNly%2Bl0Nqpg5efna9GiRR5j9957r%2B677z5zC9U3Re6OS77UuHHjjJc3fKO0tFT5%2Bflk7CWlpaV65ZXfkq8XkbH3kbF3%2BWu%2B/lUHzxPLss5p%2B3Hjxumtt97y%2BBo3bpyh1X2rrKxMixYtOuVqGcwhY%2B8iX%2B8jY%2B8jY%2B/y13y5gvUdkZGRcrlcHmMul0sBAQGKjIxs1D6io6P9qmUDAACzuIL1HYmJiSopKdGRI0fcYw6HQ506dVLr1q2bcGUAAMBfULC%2BIz4%2BXklJSZo/f74qKytVVFSkl19%2BWRkZGU29NAAA4CeCZs%2BePbupF%2BFrrrnmGq1du1ZPPvmk3n33XaWnp2vixIkKCAho6qWdonXr1urduzdX17yIjL2LfL2PjL2PjL3LH/MNsM71iW4AAAB44BYhAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULD9UXFysu%2B%2B%2BWykpKRo4cKDmzZunhoaGpl6Wz9u2bZtSU1OVk5Nzyty6des0YsQIde/eXaNHj9aHH37onmtoaNCCBQs0ePBg9erVSxMnTtTBgwfd8y6XS9nZ2UpNTVW/fv00c%2BZM1dTUnJdz8iXFxcXKzMxUSkqKUlNT9fDDD6u8vFyStGvXLt16663q2bOnhg4dqpdeeslj23PJ/0Kye/du3XHHHerZs6dSU1OVnZ2tsrIySdKOHTuUnp6uHj16aPjw4Vq9erXHtsuXL9ewYcPUo0cPZWRkqKCgwD13/PhxPfroo%2Brfv79SUlKUlZUlp9N5Xs/NFz3zzDOKi4tz/5mMzYiLi1NiYqKSkpLcX08%2B%2BaSkZpaxBb8zatQo61e/%2BpVVXl5u7du3zxo6dKj10ksvNfWyfFpeXp41dOhQa/z48VZ2drbH3M6dO63ExERry5YtVk1NjfWHP/zB6tatm1VSUmJZlmUtX77cGjhwoLVnzx6roqLCeuKJJ6wRI0ZYDQ0NlmVZ1r333mvdfffd1uHDh61Dhw5Z48aNs5588snzfo5N7YYbbrAefvhhq7Ky0iopKbFGjx5tzZgxw6qurrauueYaa%2BHChVZVVZVVUFBg9e7d29qwYYNlWeee/4Xi%2BPHj1tVXX20tWrTIOn78uHX48GHr1ltvtaZOnWp99dVX1pVXXmmtWLHCqqmpsf785z9bycnJ1j/%2B8Q/Lsizrgw8%2BsK666irr008/taqrq61ly5ZZffv2taqqqizLsqw5c%2BZYo0ePtv79739bTqfTuvfee63Jkyc35ek2uZ07d1q9e/e2unTpYlmWRcYGdenSxTp48OAp480tYwqWn/nHP/5hde3a1XK5XO6x//3f/7WGDRvWhKvyfa%2B88opVXl5uTZ8%2B/ZSC9fjjj1uZmZkeY2PGjLGWLVtmWZZlDR8%2B3HrllVfccxUVFVZ8fLz1ySefWGVlZdYVV1xh7dq1yz2/detW68orr7ROnDjhxTPyLUePHrUefvhhq6yszD326quvWkOHDrXWr19v9enTx6qrq3PPzZs3z7rzzjstyzq3/C8kLpfLevPNN63a2lr32CuvvGINGTLE%2Bu1vf2vddNNNHq/Pzs62Zs2aZVmWZd19993WM888456rr6%2B3%2Bvbta61du9aqra21evbsaW3cuNE9v2fPHisuLs46dOiQl8/KN9XX11tjxoyx/vu//9tdsMjYnO8rWM0tY24R%2BpnCwkLFxMQoLCzMPZaQkKB9%2B/apsrKyCVfm226//XaFhoaedq6wsFDx8fEeY/Hx8XI4HKqpqdGePXs85kNCQnT55ZfL4XBo165dCgoK8riNkJCQoGPHjmnv3r3eORkfZLfbNWfOHLVp08Y9VlJSoujoaBUWFiouLk5BQUHuufj4ePel/XPJ/0ISFhamMWPGyGazSZL27t2rt99%2BW9ddd933Zvh9GQcGBqpr165yOBw6cOCAKioqlJCQ4J7v2LGjWrZsqcLCwvNwZr7njTfeUHBwsEaMGOEeI2Oz5s%2BfrwEDBuiqq67SrFmzVFVV1ewypmD5GZfLJbvd7jF2smxdqPfzz5XL5fIorNI3mTqdTh09elSWZX3vvMvlUkhIiAICAjzmpAv7n4fD4dBrr72mKVOmnPbvbHh4uFwulxoaGs4p/wtRcXGxEhMTdf311yspKUlZWVnfm/HJjH4oY5fLJUmnbG%2B32y/IjL/%2B%2BmstXLhQjz32mMc4GZtz5ZVXKjU1Ve%2B//77y8/P16aef6vHHH292GVOw/JBlWU29hGbnTJn%2B0Dz/PDx9/PHHmjhxoh588EGlpqZ%2B7%2Bv%2Bs5SeS/4XmpiYGDkcDr333nvav3%2B/HnrooUZtR8aNM2fOHI0ePVqdOnU6623JuHHy8/M1ZswYtWjRQh07dtS0adO0du1a1dbWnnFbf8qYguVnIiMj3U39JJfLpYCAAEVGRjbRqvxbRETEaTONjIxUeHi4AgMDTzsfFRWlyMhIVVZWqr6%2B3mNOkqKiory/eB%2BzadMm3X333ZoxY4Zuv/12Sd/8nf3uT5Aul8ud7bnkf6EKCAhQbGyscnJytHbtWtlstlMycjqd7n8n/FDGJ1/z3fmjR49ecBnv2LFDn3zyiTIzM0%2BZO12GZGzGZZddpvr6%2BtN%2Br/tzxhQsP5OYmKiSkhIdOXLEPeZwONSpUye1bt26CVfmvxITEz3e6it9k2m3bt0UHByszp07e9zDLy8v14EDB5ScnKyuXbvKsizt3r3bY1u73a4OHTqct3PwBX//%2B981ffp0vfDCC7rpppvc44mJifr8889VV1fnHjuZ78n5H5v/hWTHjh0aNmyYx0eyBAZ%2B86/w5OTkUzIsKCjwyPg/M6yvr9fOnTvVrVs3tW/fXmFhYR7zX3zxhU6cOKHExERvnpLPWb16tQ4fPqyBAwcqJSVFo0ePliSlpKSoS5cuZGzAzp07NXfuXI%2BxoqIitWjRQmlpac0r4yZ4sB7naMyYMdaMGTOsiooKa8%2BePdagQYOs1157ramX5RdO9y7Czz//3EpKSrI2b95s1dTUWCtWrLC6d%2B9ulZaWWpb1zbs0BwwY4P6YgFmzZlk333yze/vs7Gxr0qRJ1uHDh62SkhLr5ptvtubOnXtez6up1dbWWtddd531xhtvnDJ3/Phxa%2BDAgdaLL75oHTt2zPr000%2Btq666ytq8ebNlWeee/4WivLzcSk1NtebOnWsdO3bMOnz4sDVx4kTrlltusb7%2B%2Bmure/fu1ptvvmnV1NRYW7ZssZKTk93vbt26davVs2dP65NPPrGOHTtmLVy40EpLS7Oqq6sty/rmXZ2jRo2y/v3vf1tHjhyxJk%2BebN13331NebpNwuVyWSUlJe6vTz75xOrSpYtVUlJiFRcXk7EBhw4dsq688kpr2bJl1vHjx629e/da119/vfXkk082u7/HFCw/VFJSYk2aNMlKTk62UlNTrRdffPGC%2B0ygs5WYmGglJiZaV1xxhXXFFVe4/3zShg0brKFDh1oJCQnWjTfeaH300UfuuYaGBuuFF16wrr76ais5Odm666673J/RZFnf/IcvJyfHuvLKK61evXpZjz/%2BuHX8%2BPHzen5N7f/9v/9ndenSxZ3rf37961//sj7//HNr/PjxVmJiojVgwADr9ddf99j%2BXPK/kOzevdu69dZbreTkZKtPnz5Wdna2%2By3oH330kTVy5EgrISHBGjp0qPtzxk56/fXXrbS0NCsxMdHKyMiwPv/8c/fc8ePHrdmzZ1u9evWyunfvbj3wwANWeXn5eT03X3Tw4EH3xzRYFhmb8tFHH1njxo2zrrzySqt3797WnDlzrJqaGvdcc8k4wLJ86IkwAACAZoBnsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAsP8Pm9QLCg8DnCgAAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-1056769195335953366\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">85</td>\n",
              "        <td class=\"number\">170</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">90</td>\n",
              "        <td class=\"number\">170</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">136</td>\n",
              "        <td class=\"number\">168</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">73</td>\n",
              "        <td class=\"number\">167</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">124</td>\n",
              "        <td class=\"number\">164</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">87</td>\n",
              "        <td class=\"number\">162</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">72</td>\n",
              "        <td class=\"number\">161</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">104</td>\n",
              "        <td class=\"number\">161</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">111</td>\n",
              "        <td class=\"number\">160</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">106</td>\n",
              "        <td class=\"number\">159</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (1534)</td>\n",
              "        <td class=\"number\">39546</td>\n",
              "        <td class=\"number\">96.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-1056769195335953366\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">0</td>\n",
              "        <td class=\"number\">4</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:34%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1</td>\n",
              "        <td class=\"number\">3</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">3</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:25%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">12</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">3631</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3643</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3785</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4199</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4918</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_campaign\">campaign<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>42</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.1%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>2.5676</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>1</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>56</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram-2173299089784530331\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABEElEQVR4nO3XwQnCQBRFURVLShHpyXV6soj0NO5FLkaUBDln/%2BFtLsOcxxjjBLx02XsAHNl17wHPptt98826zD9YAl4QSAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBcN17wDdMt/vmm3WZf7CEf/MXgXxCVLzjPMYYe4%2BAo/IHgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgfAAryERk/Ikp6kAAAAASUVORK5CYII%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-2173299089784530331,#minihistogram-2173299089784530331\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives-2173299089784530331\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-2173299089784530331\"\n",
              "                                                  aria-controls=\"quantiles-2173299089784530331\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram-2173299089784530331\" aria-controls=\"histogram-2173299089784530331\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common-2173299089784530331\" aria-controls=\"common-2173299089784530331\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme-2173299089784530331\" aria-controls=\"extreme-2173299089784530331\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-2173299089784530331\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>1</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>1</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>1</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>2</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>3</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>7</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>56</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>55</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>2</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>2.77</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>1.0788</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>36.98</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>2.5676</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>1.6342</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>4.7625</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>105754</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>7.673</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-2173299089784530331\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nO3de1xUdeL/8TcwgQkMMBrWl9xwvZBcdNMUIxMvq26Zli5eaLu4WXmhCJJN07x0tVbJSn2o7G5%2Bs3ps8wUrlc10XS9b6V6qzYZRK1HX4oFBOiMXQQXm94c/Z3cSE4cTM9Dr%2BXjw4OvnM%2Becz3k/pv2%2BOecwBLhcLpcAAABgmEBfLwAAAKCtoWABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMJOvF/BjUV5eecnbBAYGyGIJ1fHj1WpocP0Aq2q7yK55yM97ZOc9smse8mvcFVeE%2B%2BS4reIK1rPPPqu4uDj3v3fv3q20tDT16dNHo0aN0oYNGzxev3btWo0cOVJ9%2BvRRenq6ioqK3HOnTp3S/PnzNWjQICUnJyszM1MOh8M9X1JSogceeEDJyckaMmSIFi9erIaGhh/%2BJBsRGBiggIAABQYG%2BOT4rRnZNQ/5eY/svEd2zUN%2B/sXvC9a%2Bffu0fv1697/Lyso0Y8YMTZo0Sbt379bcuXM1b9482Ww2SdK2bdu0bNky/fa3v9WuXbs0ZMgQTZs2TSdPnpQkLV26VHa7XVarVZs3b5bL5dJjjz3m3v9DDz2kTp06aevWrVqzZo22bt2qV199tWVPGgAAtGp%2BXbAaGhq0YMECTZ482T22ceNGxcbGKi0tTSEhIUpJSdHQoUOVn58vSbJarRo3bpx69%2B6tdu3a6b777pMkbd%2B%2BXXV1dSooKNCMGTN01VVXKTIyUllZWdqxY4e%2B%2BeYb2Ww27d%2B/Xzk5OQoPD1dsbKwmT54sq9Xqi9MHAACtlF8XrDfffFMhISEaPXq0e8xutys%2BPt7jdfHx8e7bgN%2BdDwwMVM%2BePWWz2XTkyBFVVlYqISHBPd%2B1a1e1a9dOdrtddrtdMTExioiIcM8nJCTo0KFDqqqq%2BqFOEwAAtDF%2B%2B5D7t99%2Bq2XLlum1117zGHc6nerUqZPHWGRkpPs5KqfT6VGQJCkiIkIOh0NOp1OSZDabPebNZrN7/rtz5/blcDgUFhbWpLWXlZWpvLzcY8xkaq/o6OgmbX9OUFCgx3c0Hdk1D/l5j%2By8R3bNQ37%2BxW8L1qJFizRu3Dh169ZNX3/99SVt63J9/29PfN/8xbZtCqvVquXLl3uMZWRkKDMz06v9mc2XN3tNP1Zk1zzk5z2y8x7ZNQ/5%2BQe/LFi7d%2B/Wv/71LxUWFp43FxUV5b4SdY7D4ZDFYrngvNPpVPfu3d2vcTqdCg0Ndc%2BfOHFCHTp0UH19faPbBgQEuLdtiokTJ2ro0KEeYyZTezkc1U3eh3T2pxCz%2BXJVVNSovt43v8nYWpFd85Cf98jOe2TXPOTXuKio0Iu/6AfglwVrw4YNOnbsmIYMGSLpP1eVkpOTde%2B9955XvIqKitS7d29JUmJioux2u8aOHStJqq%2Bv1969e5WWlqbOnTsrIiLC/ayVJH3xxRc6ffq0EhMTVVZWptLSUh0/ftxdqGw2m7p16%2BZRyC4mOjr6vNuB5eWVqqvz7g1fX9/g9bY/dmTXPOTnPbLzHtk1D/n5B7%2B8UTt79mxt3rxZ69ev1/r165WXlydJWr9%2BvUaPHq2SkhLl5%2Bfr1KlT2rlzp3bu3KkJEyZIktLT0/XOO%2B/o008/VU1NjVauXKng4GANHjxYQUFBmjBhglatWqXS0lI5HA698MILGj58uDp27Kj4%2BHglJSUpNzdXVVVVKi4u1po1a5Senu7LOAAAQCvjl1ewIiIiPB5Ur6urkyRdeeWVkqTVq1fr6aef1hNPPKGYmBgtXrxY1157rSRp0KBBeuSRR5SVlaVjx44pKSlJeXl5ateunSQpMzNT1dXVuu2221RXV6chQ4Zo4cKF7mO9/PLLmjdvnm688UaFhYVp0qRJuuOOO1rozAEAQFsQ4DLiqW5clDd/KsdkClRUVKgcjmou914ismse8vMe2XmP7JqH/BrHn8oBAABoIyhYAAAABqNgAQAAGMwvH3JH09384oe%2BXkKTbcq60ddLAACgRXAFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMJjfFqz9%2B/frnnvuUd%2B%2BfZWSkqKsrCyVl5fr73//u%2BLi4pSUlOTxtWnTJve2a9eu1ciRI9WnTx%2Blp6erqKjIPXfq1CnNnz9fgwYNUnJysjIzM%2BVwONzzJSUleuCBB5ScnKwhQ4Zo8eLFamhoaNFzBwAArZtfFqzTp0/r3nvvVf/%2B/bV7924VFhbq2LFjWrhwoSQpJiZGNpvN4%2Bvmm2%2BWJG3btk3Lli3Tb3/7W%2B3atUtDhgzRtGnTdPLkSUnS0qVLZbfbZbVatXnzZrlcLj322GPuYz/00EPq1KmTtm7dqjVr1mjr1q169dVXWzwDAADQevllwaqpqVF2dramTp2q4OBgWSwWDR8%2BXF9%2B%2BeVFt7VarRo3bpx69%2B6tdu3a6b777pMkbd%2B%2BXXV1dSooKNCMGTN01VVXKTIyUllZWdqxY4e%2B%2BeYb2Ww27d%2B/Xzk5OQoPD1dsbKwmT54sq9X6Q58yAABoQ/yyYEVERGj8%2BPEymUySpIMHD%2Brtt992X6Wqrq5WRkaGkpOTddNNN2nNmjVyuVySJLvdrvj4ePe%2BAgMD1bNnT9lsNh05ckSVlZVKSEhwz3ft2lXt2rWT3W6X3W5XTEyMIiIi3PMJCQk6dOiQqqqqWuLUAQBAG2Dy9QK%2BT0lJiUaOHKm6ujpNmDBBmZmZ2r9/v3r06KF77rlHS5cu1T/%2B8Q89/PDDCg8PV1pampxOp0dBks4WNofDIafTKUkym80e82az2T3/3blz%2B3I4HAoLC2vSusvKylReXu4xZjK1V3R09CWdf1BQoMf31s5karnzaGvZtTTy8x7ZeY/smof8/ItfF6xzz1r9%2B9//1vz58/Xoo48qNzdXr732mvs1AwcO1KRJk/TWW28pLS1NktxXsy7k%2B%2BYvtm1TWK1WLV%2B%2B3GMsIyNDmZmZXu3PbL682WvyB1FRoS1%2BzLaSna%2BQn/fIzntk1zzk5x/8umBJUkBAgGJjY5Wdna1JkyZp7ty5slgsHq%2BJiYnR5s2bJUlRUVHuK1XnOJ1Ode/e3b2d0%2BlUaOh//p/9iRMn1KFDB9XX1ze6bUBAwHnH/D4TJ07U0KFDPcZMpvZyOKqbvA/p7E8hZvPlqqioUX196/9Nxks9/%2BZoa9m1NPLzHtl5j%2Byah/wa54sf7iU/LVi7d%2B/WwoULtWnTJgUGnr3Uee77zp07VVNTozvuuMP9%2BoMHD6pz586SpMTERNntdo0dO1aSVF9fr7179yotLU2dO3dWRESE%2B1krSfriiy90%2BvRpJSYmqqysTKWlpTp%2B/Li7UNlsNnXr1s2jkF1MdHT0ebcDy8srVVfn3Ru%2Bvr7B6239iS/Ooa1k5yvk5z2y8x7ZNQ/5%2BQe/vFGbmJioqqoqLV68WDU1NTp%2B/LiWLVum66%2B/XuHh4Xr%2B%2Bef1wQcf6MyZM/rwww%2B1bt06paenS5LS09P1zjvv6NNPP1VNTY1Wrlyp4OBgDR48WEFBQZowYYJWrVql0tJSORwOvfDCCxo%2BfLg6duyo%2BPh4JSUlKTc3V1VVVSouLtaaNWvc%2BwYAAGgKv7yCFR4erldeeUVPP/20BgwYoPbt22vAgAF65pln1KlTJ82ZM0dPPfWUSktL1bFjR82ZM0cjRoyQJA0aNEiPPPKIsrKydOzYMSUlJSkvL0/t2rWTJGVmZqq6ulq33Xab6urqNGTIEPfna0nSyy%2B/rHnz5unGG29UWFiYJk2a5HG1DAAA4GICXEY81Y2LKi%2BvvORtTKZARUWFyuGovuDl3ptf/LC5S2sxm7JubLFjNSU7XBj5eY/svEd2zUN%2BjbviinCfHNcvbxECAAC0ZhQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGB%2BW7D279%2Bve%2B65R3379lVKSoqysrJUXl4uSdq9e7fS0tLUp08fjRo1Shs2bPDYdu3atRo5cqT69Omj9PR0FRUVuedOnTql%2BfPna9CgQUpOTlZmZqYcDod7vqSkRA888ICSk5M1ZMgQLV68WA0NDS1z0gAAoE3wy4J1%2BvRp3Xvvverfv792796twsJCHTt2TAsXLlRZWZlmzJihSZMmaffu3Zo7d67mzZsnm80mSdq2bZuWLVum3/72t9q1a5eGDBmiadOm6eTJk5KkpUuXym63y2q1avPmzXK5XHrsscfcx37ooYfUqVMnbd26VWvWrNHWrVv16quv%2BiQHAADQOvllwaqpqVF2dramTp2q4OBgWSwWDR8%2BXF9%2B%2BaU2btyo2NhYpaWlKSQkRCkpKRo6dKjy8/MlSVarVePGjVPv3r3Vrl073XfffZKk7du3q66uTgUFBZoxY4auuuoqRUZGKisrSzt27NA333wjm82m/fv3KycnR%2BHh4YqNjdXkyZNltVp9GQcAAGhlTL5eQGMiIiI0fvx4978PHjyot99%2BWzfffLPsdrvi4%2BM9Xh8fH69NmzZJkux2u2655Rb3XGBgoHr27CmbzaaePXuqsrJSCQkJ7vmuXbuqXbt2stvtKisrU0xMjCIiItzzCQkJOnTokKqqqhQWFtak9ZeVlblvZ55jMrVXdHR000OQFBQU6PG9tTOZWu482lp2LY38vEd23iO75iE//%2BKXBeuckpISjRw5UnV1dZowYYIyMzN1//33q1OnTh6vi4yMdD9H5XQ6PQqSdLawORwOOZ1OSZLZbPaYN5vN7vnvzp3bl8PhaHLBslqtWr58ucdYRkaGMjMzm7T9d5nNl3u1nb%2BJigpt8WO2lex8hfy8R3beI7vmIT//4NcFKyYmRjabTf/%2B9781f/58Pfroo03azuVyeT1/sW2bYuLEiRo6dKjHmMnUXg5H9SXtJygoUGbz5aqoqFF9fet/0P5Sz7852lp2LY38vEd23iO75iG/xvnih3vJzwuWJAUEBCg2NlbZ2dmaNGmSUlNT3VeiznE4HLJYLJKkqKio8%2BadTqe6d%2B/ufo3T6VRo6H8CP3HihDp06KD6%2BvpGtw0ICHBv2xTR0dHn3Q4sL69UXZ13b/j6%2Bgavt/UnvjiHtpKdr5Cf98jOe2TXPOTnH/zyRu3u3bs1cuRIj49HCAw8u9RevXp5fOyCJBUVFal3796SpMTERNntdvdcfX299u7dq969e6tz586KiIjwmP/iiy90%2BvRpJSYmKjExUaWlpTp%2B/Lh73mazqVu3bh6FDAAA4Pv4ZcFKTExUVVWVFi9erJqaGh0/flzLli3T9ddfr/T0dJWUlCg/P1%2BnTp3Szp07tXPnTk2YMEGSlJ6ernfeeUeffvqpampqtHLlSgUHB2vw4MEKCgrShAkTtGrVKpWWlsrhcOiFF17Q8OHD1bFjR8XHxyspKUm5ubmqqqpScXGx1qxZo/T0dB8nAgAAWhO/LFjh4eF65ZVXVFRUpAEDBmjUqFEKDw/XCy%2B8oA4dOmj16tV6/fXX1bdvXz377LNavHixrr32WknSoEGD9MgjjygrK0v9%2B/fXrl27lJeXp3bt2kmSMjMz1bt3b912220aNmyYQkND9cwzz7iP/fLLL6usrEw33nij7r77bt1%2B%2B%2B264447fJIDAABonQJcRjzVjYsqL6%2B85G1MpkBFRYXK4ai%2B4P30m1/8sLlLazGbsm5ssWM1JTtcGPl5j%2By8R3bNQ36Nu%2BKKcJ8c1y%2BvYAEAALRmFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwmN8WrJKSEmVkZCg5OVkpKSmaPXu2Kioq9PXXXysuLk5JSUkeX3/4wx/c27777rsaPXq0rrvuOo0bN04ffPCBe66hoUFLly7VsGHD1K9fP02ZMkVfffWVe97pdCorK0spKSkaOHCg5s6dq9ra2hY9dwAA0Lr5bcGaNm2azGaztm3bprfeektffvmlnn/%2Befe8zWbz%2BJoyZYokad%2B%2BfZo1a5ZycnL0t7/9TZMnT9aDDz6oo0ePSpLeeOMNbdy4UXl5edq%2BfbtiY2OVkZEhl8slSZo3b55qampUWFiodevWqbi4WEuWLGn5AAAAQKvllwWroqJCiYmJmjlzpkJDQ3XllVdq7Nix%2Buijjy66bX5%2BvlJTU5WamqqQkBCNGTNGPXr00IYNGyRJVqtVkydPVteuXRUWFqbs7GwVFxdrz549%2Bvbbb7V161ZlZ2fLYrGoU6dOmjFjhtatW6czZ8780KcNAADaCL8sWGazWYsWLVLHjh3dY6WlpYqOjnb/%2B9FHH9XAgQM1YMAA5ebmuguQ3W5XfHy8x/7i4%2BNls9lUW1urAwcOeMyHhYXpmmuukc1m0759%2BxQUFKS4uDj3fEJCgk6ePKmDBw/%2BUKcLAADaGJOvF9AUNptNr7/%2BulauXKng4GBdd911Gj58uJ555hnt27dPDz30kEwmkx5%2B%2BGE5nU5FRER4bB8REaEDBw7oxIkTcrlcjc47HA5FRkYqLCxMAQEBHnOS5HA4mrzesrIylZeXe4yZTO09CmJTBAUFenxv7UymljuPtpZdSyM/75Gd98iuecjPv/h9wfr44481ffp0zZw5UykpKZKkN9980z3fq1cvTZ06VatXr9bDDz8sSe7nqS7k%2B%2BYvtm1TWK1WLV%2B%2B3GMsIyNDmZmZXu3PbL682WvyB1FRoS1%2BzLaSna%2BQn/fIzntk1zzk5x/8umBt27ZNv/nNbzRv3jzdfvvtF3xdTEyMvv32W7lcLkVFRcnpdHrMO51OWSwWRUZGKjAwsNH5Dh06yGKxqKqqSvX19QoKCnLPSVKHDh2avO6JEydq6NChHmMmU3s5HNVN3od09qcQs/lyVVTUqL6%2B4ZK29UeXev7N0daya2nk5z2y8x7ZNQ/5Nc4XP9xLflywPvnkE82aNUsvvfSSBg4c6B7fvXu3Pv30U02fPt09dvDgQcXExCggIECJiYkqKiry2JfNZtOoUaMUEhKi7t27y263q3///pLOPlB/5MgR9erVSzExMXK5XNq/f78SEhLc25rNZnXp0qXJa4%2BOjj7vdmB5eaXq6rx7w9fXN3i9rT/xxTm0lex8hfy8R3beI7vmIT//YPiN2qFDh2r58uUqLS31eh91dXV6/PHHlZOT41GuJCk8PFwrVqzQ%2BvXrdebMGdlsNv3hD39Qenq6JGnChAnatWuXduzYoVOnTqmgoECHDx/WmDFjJEnp6elau3atiouLVVVVpSVLlqhnz55KSkqSxWLRyJEj9eKLL%2Br48eM6evSoVqxYobS0NJlMfttFAQCAnwlwGfHQ0X9ZsWKF/vSnP%2Bnf//63brjhBk2YMEFDhw69pILy0Ucf6Ve/%2BpWCg4PPm3vvvfe0d%2B9eLV%2B%2BXIcPH1Z4eLjuuusu3X///QoMPNsXt2zZotzcXJWUlKhbt26aO3eu%2BvXrJ%2BnsM1bLli3Tm2%2B%2BqerqaiUnJ%2BvJJ5/UlVdeKUmqrKzUggULtH37dl122WW69dZbNXv27EbXcinKyysveRuTKVBRUaFyOKov%2BNPIzS9%2B2Kx1taRNWTe22LGakh0ujPy8R3beI7vmIb/GXXFFuE%2BOa3jBOsdut6uwsFCbNm3SmTNndPvttystLe2SbrW1JRQsClZrQn7eIzvvkV3zkF/jfFWwfrDf5UxISNCsWbO0fft2zZkzR//3f/%2BnW265RVOmTNFnn332Qx0WAADA536wgnXmzBm9%2B%2B67uv/%2B%2BzVr1ix16tRJjz32mHr27KnJkydr48aNP9ShAQAAfMrwJ7eLi4tVUFCgd955R9XV1Ro5cqReffVV9e3b1/2afv36aeHChRo9erTRhwcAAPA5wwvWqFGj1KVLF02dOlW33367IiMjz3tNamqqjh8/bvShAQAA/ILhBWvt2rXuz5j6Pnv27DH60AAAAH7B8Gew4uLiNG3aNG3dutU99r//%2B7%2B6//77z/sEdQAAgLbI8IK1aNEiVVZWqlu3bu6xwYMHq6GhQc8995zRhwMAAPA7ht8i/OCDD7Rx40ZFRUW5x2JjY7VkyRLdeuutRh8OAADA7xh%2BBau2tlYhISHnHygwUDU1NUYfDgAAwO8YXrD69eun5557TidOnHCPffPNN3riiSc8PqoBAACgrTL8FuGcOXN077336oYbblBYWJgaGhpUXV2tzp0767XXXjP6cAAAAH7H8ILVuXNn/elPf9Jf//pXHTlyRIGBgerSpYsGDhyooKAgow8HAADgdwwvWJIUHBysn//85z/ErgEAAPye4QXrq6%2B%2BUm5urr788kvV1taeN/%2BXv/zF6EMCAAD4lR/kGayysjINHDhQ7du3N3r3AAAAfs/wglVUVKS//OUvslgsRu8aAACgVTD8Yxo6dOjAlSsAAPCjZnjBmjp1qpYvXy6Xy2X0rgEAAFoFw28R/vWvf9Unn3yit956S1dffbUCAz073Jtvvmn0IQEAAPyK4QUrLCxMgwYNMnq3AAAArYbhBWvRokVG7xIAAKBVMfwZLEk6ePCgli1bpscee8w99q9//euHOBQAAIDfMbxg7d69W2PGjNGWLVtUWFgo6eyHj9599918yCgAAPhRMLxgLV26VL/5zW%2B0ceNGBQQESDr79wmfe%2B45rVixwujDAQAA%2BB3DC9YXX3yh9PR0SXIXLEn6xS9%2BoeLiYqMPBwAA4HcML1jh4eGN/g3CsrIyBQcHG304AAAAv2N4werTp4%2BeffZZVVVVuccOHTqkWbNm6YYbbjD6cAAAAH7H8I9peOyxx3TPPfcoOTlZ9fX16tOnj2pqatS9e3c999xzRh8OAADA7xhesK688koVFhZq586dOnTokNq1a6cuXbroxhtv9Hgm62JKSkr07LPP6qOPPlJQUJAGDRqkOXPmyGw2a9%2B%2BfXrmmWe0b98%2BdejQQZMmTdK9997r3vbdd9/VypUr9fXXX6tLly565JFHNHDgQElSQ0ODXnrpJRUWFqqiokK9evXSwoUL1blzZ0mS0%2BnUwoUL9Y9//EOBgYFKTU3VvHnz1K5dO2ODAgAAbdYP8jlYl112mX7%2B85/r/vvv11133aWBAwdeUrmSpGnTpslsNmvbtm1666239OWXX%2Br5559XbW2tpk6dqgEDBuj999/X0qVLtXr1am3ZskWStG/fPs2aNUs5OTn629/%2BpsmTJ%2BvBBx/U0aNHJUlvvPGGNm7cqLy8PG3fvl2xsbHKyMhw/%2B3EefPmqaamRoWFhVq3bp2Ki4u1ZMkSYwMCAABtmuFXsIYOHfq9Zaopn4VVUVGhxMREzZw5U6GhoQoNDdXYsWP12muvaceOHTpz5oymT5%2BuoKAgJSQkaPz48bJarRoxYoTy8/OVmpqq1NRUSdKYMWP0%2Buuva8OGDXrggQdktVo1efJkde3aVZKUnZ2t5ORk7dmzR1dffbW2bt2qt99%2BWxaLRZI0Y8YMPfzww5o1a5Yuu%2BwyAxICAABtneEF65ZbbvEoWPX19Tp06JBsNpvuueeeJu3DbDaf9yd3SktLFR0dLbvdrri4OAUFBbnn4uPjlZ%2BfL0my2%2B3ucvXf8zabTbW1tTpw4IDi4%2BPdc2FhYbrmmmtks9lUWVmpoKAgxcXFuecTEhJ08uRJHTx40GMcAADgQgwvWDk5OY2Ob968WX//%2B9%2B92qfNZtPrr7%2BulStXatOmTTKbzR7zkZGRcjqdamhokNPpVEREhMd8RESEDhw4oBMnTsjlcjU673A4FBkZqbCwMI%2BCeO61DoejyestKytTeXm5x5jJ1F7R0dFN3ockBQUFenxv7UymljuPtpZdSyM/75Gd98iuecjPvxhesC7k5z//uebPn6/58%2Bdf0nYff/yxpk%2BfrpkzZyolJUWbNm1q9HX/XYrOPU91Id83f7Ftm8JqtWr58uUeYxkZGcrMzPRqf2bz5c1ekz%2BIigpt8WO2lex8hfy8R3beI7vmIT//0GIFa%2B/evZdcXrZt26bf/OY3mjdvnm6//XZJksVi0eHDhz1e53Q6FRkZqcDAQEVFRcnpdJ43b7FY3K9pbL5Dhw6yWCyqqqpSfX29%2Bxbkudd26NChyeueOHGihg4d6jFmMrWXw1Hd5H1IZ38KMZsvV0VFjerrGy5pW390qeffHG0tu5ZGft4jO%2B%2BRXfOQX%2BN88cO99AMUrEmTJp03VlNTo%2BLiYo0YMaLJ%2B/nkk080a9YsvfTSS%2B6PWJCkxMRE/fGPf1RdXZ1MprPLt9ls6t27t3u%2BqKjIY182m02jRo1SSGuidyIAAB3HSURBVEiIunfvLrvdrv79%2B0s6%2B0D9kSNH1KtXL8XExMjlcmn//v1KSEhwb2s2m9WlS5cmrz06Ovq824Hl5ZWqq/PuDV9f3%2BD1tv7EF%2BfQVrLzFfLzHtl5j%2Byah/z8g%2BE3amNjY9WlSxePr%2Buuu06zZs3Ss88%2B26R91NXV6fHHH1dOTo5HuZKk1NRUhYWFaeXKlaqpqdGePXtUUFDg/vuHEyZM0K5du7Rjxw6dOnVKBQUFOnz4sMaMGSNJSk9P19q1a1VcXKyqqiotWbJEPXv2VFJSkiwWi0aOHKkXX3xRx48f19GjR7VixQqlpaW5yxwAAMDFBLiMeOjIYB999JF%2B9atfNfq3C9977z1VV1drwYIFKioqUseOHXX//ffrjjvucL9my5Ytys3NVUlJibp166a5c%2BeqX79%2Bks4%2BY7Vs2TK9%2Beabqq6uVnJysp588kldeeWVkqTKykotWLBA27dv12WXXaZbb71Vs2fPbvbfUSwvr7zkbUymQEVFhcrhqL7gTyM3v/hhs9bVkjZl3dhix2pKdrgw8vMe2XmP7JqH/Bp3xRXhPjmu4QXrnXfeafJrzz1X9WNAwaJgtSbk5z2y8x7ZNQ/5Nc5XBcvw%2B15z585VQ0PDeQ%2B0BwQEeIwFBAT8qAoWAAD48TC8YP3%2B97/XK6%2B8omnTpikuLk4ul0uff/65fve73%2BnOO%2B9UcnKy0YcEAADwK4YXrOeee055eXnq1KmTe%2Bz6669X586dNWXKFBUWFhp9SAAAAL9i%2BG8RHj58%2BLxPSpfO/vmbkpISow8HAADgdwwvWDExMXruuec8/rRMRUWFcnNz9ZOf/MTowwEAAPgdw28RzpkzRzNnzpTValVoaKgCAwNVVVWldu3aacWKFUYfDgAAwO8YXrAGDhyoHTt2aOfOnTp69KhcLpc6deqkm266SeHhvvlVSQAAgJb0g3w8%2BeWXX65hw4bp6NGj6ty58w9xCAAAAL9l%2BDNYtbW1mjVrlq677jrdfPPNks4%2Bg3XfffepoqLC6MMBAAD4HcML1uLFi7Vv3z4tWbJEgYH/2X19fb2WLFli9OEAAAD8juEFa/PmzXr55Zf1i1/8QgEBAZLOfkTDokWLtGXLFqMPBwAA4HcML1jV1dWKjY09b9xisejkyZNGHw4AAMDvGF6wfvKTn%2Bjvf/%2B7JHn87cH33ntP//M//2P04QAAAPyO4b9FeMcdd%2Bihhx7SL3/5SzU0NGjNmjUqKirS5s2bNXfuXKMPBwAA4HcML1gTJ06UyWTS66%2B/rqCgIK1atUpdunTRkiVL9Itf/MLowwEAAPgdwwvW8ePH9ctf/lK//OUvjd41AABAq2D4M1jDhg3zePYKAADgx8bwgpWcnKxNmzYZvVsAAIBWw/BbhFdddZWeeeYZ5eXl6Sc/%2BYkuu%2Bwyj/nc3FyjDwkAAOBXDC9YBw4c0E9/%2BlNJksPhMHr3AAAAfs%2BwgpWdna2lS5fqtddec4%2BtWLFCGRkZRh0CAACgVTDsGaxt27adN5aXl2fU7gEAAFoNwwpWY785yG8TAgCAHyPDCta5P%2Bx8sTEAAIC2zvCPaQAAAPixo2ABAAAYzLDfIjxz5oxmzpx50TE%2BBwsAALR1hhWsvn37qqys7KJjAAAAbZ1hBeu/P/8KAADgx8yvn8F6//33lZKSouzsbI/xt956S9dee62SkpI8vj777DNJUkNDg5YuXaphw4apX79%2BmjJlir766iv39k6nU1lZWUpJSdHAgQM1d%2B5c1dbWuuf37dunO%2B%2B8U3379tWIESP0yiuvtMwJAwCANsFvC9bvfvc7Pf3007rmmmsane/Xr59sNpvHV69evSRJb7zxhjZu3Ki8vDxt375dsbGxysjIcH8u17x581RTU6PCwkKtW7dOxcXFWrJkiSSptrZWU6dO1YABA/T%2B%2B%2B9r6dKlWr16tbZs2dIyJw4AAFo9vy1YISEhKigouGDB%2Bj5Wq1WTJ09W165dFRYWpuzsbBUXF2vPnj369ttvtXXrVmVnZ8tisahTp06aMWOG1q1bpzNnzmjHjh06c%2BaMpk%2Bfrvbt2yshIUHjx4%2BX1Wr9Ac4SAAC0RYb/sWej3H333d87X1paql//%2BtcqKiqS2WxWZmambrvtNtXW1urAgQOKj493vzYsLEzXXHONbDabKisrFRQUpLi4OPd8QkKCTp48qYMHD8putysuLk5BQUHu%2Bfj4eOXn5zd57WVlZSovL/cYM5naKzo6usn7kKSgoECP762dydRy59HWsmtp5Oc9svMe2TUP%2BfkXvy1Y38disSg2NlaPPPKIunXrpj//%2Bc969NFHFR0drZ/%2B9KdyuVyKiIjw2CYiIkIOh0ORkZEKCwvz%2BJT5c691OBxyOp0ym80e20ZGRsrpdKqhoUGBgRd/41qtVi1fvtxjLCMjQ5mZmV6dr9l8uVfb%2BZuoqNAWP2Zbyc5XyM97ZOc9smse8vMPrbJgDR48WIMHD3b/e9SoUfrzn/%2Bst956Szk5OZK%2B/%2B8gevM3Ei/lz/5MnDhRQ4cO9RgzmdrL4ai%2BpGMGBQXKbL5cFRU1qq9vuKRt/dGlnn9ztLXsWhr5eY/svEd2zUN%2BjfPFD/dSKy1YjYmJiVFRUZEiIyMVGBgop9PpMe90OtWhQwdZLBZVVVWpvr7efRvw3GvPzR8%2BfPi8bc/ttymio6PPux1YXl6pujrv3vD19Q1eb%2BtPfHEObSU7XyE/75Gd98iuecjPP7TKG7V//OMf9e6773qMFRcXq3PnzgoJCVH37t1lt9vdcxUVFTpy5Ih69eqlnj17yuVyaf/%2B/e55m80ms9msLl26KDExUZ9//rnq6uo85nv37v3DnxgAAGgTWmXBOn36tJ566inZbDadOXNGhYWF%2Butf/6pJkyZJktLT07V27VoVFxerqqpKS5YsUc%2BePZWUlCSLxaKRI0fqxRdf1PHjx3X06FGtWLFCaWlpMplMSk1NVVhYmFauXKmamhrt2bNHBQUFSk9P9/FZAwCA1sJvbxEmJSVJkvtK0tatWyWdvZp09913q7q6Wg8//LDKy8t19dVXa8WKFUpMTJQkTZo0SeXl5brrrrtUXV2t5ORkj4fOn3zySS1YsEDDhg3TZZddpltvvdX9YabBwcFatWqVFixYoLy8PHXs2FHZ2dkez3wBAAB8nwCXN09845KVl1de8jYmU6CiokLlcFRf8H76zS9%2B2NyltZhNWTe22LGakh0ujPy8R3beI7vmIb/GXXFFuE%2BO2ypvEQIAAPgzChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYzK8L1vvvv6%2BUlBRlZ2efN/fuu%2B9q9OjRuu666zRu3Dh98MEH7rmGhgYtXbpUw4YNU79%2B/TRlyhR99dVX7nmn06msrCylpKRo4MCBmjt3rmpra93z%2B/bt05133qm%2BfftqxIgReuWVV37YEwUAAG2K3xas3/3ud3r66ad1zTXXnDe3b98%2BzZo1Szk5Ofrb3/6myZMn68EHH9TRo0clSW%2B88YY2btyovLw8bd%2B%2BXbGxscrIyJDL5ZIkzZs3TzU1NSosLNS6detUXFysJUuWSJJqa2s1depUDRgwQO%2B//76WLl2q1atXa8uWLS138gAAoFXz24IVEhKigoKCRgtWfn6%2BUlNTlZqaqpCQEI0ZM0Y9evTQhg0bJElWq1WTJ09W165dFRYWpuzsbBUXF2vPnj369ttvtXXrVmVnZ8tisahTp06aMWOG1q1bpzNnzmjHjh06c%2BaMpk%2Bfrvbt2yshIUHjx4%2BX1Wpt6QgAAEArZfL1Ai7k7rvvvuCc3W5Xamqqx1h8fLxsNptqa2t14MABxcfHu%2BfCwsJ0zTXXyGazqbKyUkFBQYqLi3PPJyQk6OTJkzp48KDsdrvi4uIUFBTkse/8/Pwmr72srEzl5eUeYyZTe0VHRzd5H5IUFBTo8b21M5la7jzaWnYtjfy8R3beI7vmIT//4rcF6/s4nU5FRER4jEVEROjAgQM6ceKEXC5Xo/MOh0ORkZEKCwtTQECAx5wkORwOOZ1Omc1mj20jIyPldDrV0NCgwMCLv3GtVquWL1/uMZaRkaHMzMxLOs9zzObLvdrO30RFhbb4MdtKdr5Cft4jO%2B%2BRXfOQn39olQVLkvt5Km/mL7ZtY/67kF3MxIkTNXToUI8xk6m9HI7qSzpmUFCgzObLVVFRo/r6hkva1h9d6vk3R1vLrqWRn/fIzntk1zzk1zhf/HAvtdKCFRUVJafT6THmdDplsVgUGRmpwMDARuc7dOggi8Wiqqoq1dfXu28DnnvtufnDhw%2Bft%2B25/TZFdHT0ebcDy8srVVfn3Ru%2Bvr7B6239iS/Ooa1k5yvk5z2y8x7ZNQ/5%2BYdWeaM2MTFRRUVFHmM2m029e/dWSEiIunfvLrvd7p6rqKjQkSNH1KtXL/Xs2VMul0v79%2B/32NZsNqtLly5KTEzU559/rrq6uvP2DQAA0BStsmBNmDBBu3bt0o4dO3Tq1CkVFBTo8OHDGjNmjCQpPT1da9euVXFxsaqqqrRkyRL17NlTSUlJslgsGjlypF588UUdP35cR48e1YoVK5SWliaTyaTU1FSFhYVp5cqVqqmp0Z49e1RQUKD09HQfnzUAAGgt/PYWYVJSkiS5ryRt3bpV0tmrST169NCSJUu0aNEilZSUqFu3blq9erWuuOIKSdKkSZNUXl6uu%2B66S9XV1UpOTvZ46PzJJ5/UggULNGzYMF122WW69dZb3R9mGhwcrFWrVmnBggXKy8tTx44dlZ2drcGDB7fg2QMAgNYswOXNE9%2B4ZOXllZe8jckUqKioUDkc1Re8n37zix82d2ktZlPWjS12rKZkhwsjP%2B%2BRnffIrnnIr3FXXBHuk%2BO2yluEAAAA/oyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAZrtQUrLi5OiYmJSkpKcn899dRTkqTdu3crLS1Nffr00ahRo7RhwwaPbdeuXauRI0eqT58%2BSk9PV1FRkXvu1KlTmj9/vgYNGqTk5GRlZmbK4XC06LkBAIDWzeTrBTTHe%2B%2B9p6uvvtpjrKysTDNmzNDcuXM1evRoffzxx5o%2Bfbq6dOmipKQkbdu2TcuWLdPvf/97xcXFae3atZo2bZq2bNmi9u3ba%2BnSpbLb7bJarbr88ss1b948PfbYY1q1apWPzhIAALQ2rfYK1oVs3LhRsbGxSktLU0hIiFJSUjR06FDl5%2BdLkqxWq8aNG6fevXurXbt2uu%2B%2B%2ByRJ27dvV11dnQoKCjRjxgxdddVVioyMVFZWlnbs2KFvvvnGl6cFAABakVZdsHJzczV48GBdf/31mjdvnqqrq2W32xUfH%2B/xuvj4ePdtwO/OBwYGqmfPnrLZbDpy5IgqKyuVkJDgnu/atavatWsnu93eMicFAABavVZ7i/BnP/uZUlJS9Pzzz%2Burr75SVlaWnnjiCTmdTnXq1MnjtZGRke7nqJxOpyIiIjzmIyIi5HA45HQ6JUlms9lj3mw2X9JzWGVlZSovL/cYM5naKzo6usn7kKSgoECP762dydRy59HWsmtp5Oc9svMe2TUP%2BfmXVluwrFar%2B//u2rWrcnJyNH36dPXt2/ei27pcrmbNN2Vty5cv9xjLyMhQZmamV/szmy9v1nr8RVRUaIsfs61k5yvk5z2y8x7ZNQ/5%2BYdWW7C%2B6%2Bqrr1Z9fb0CAwPdV6LOcTgcslgskqSoqKjz5p1Op7p37%2B5%2BjdPpVGjof8rAiRMn1KFDhyavZeLEiRo6dKjHmMnUXg5H9SWdU1BQoMzmy1VRUaP6%2BoZL2tYfXer5N0dby66lkZ/3yM57ZNc85Nc4X/xwL7XSgrV3715t2LBBs2fPdo8VFxcrODhYqampevvttz1eX1RUpN69e0uSEhMTZbfbNXbsWElSfX299u7dq7S0NHXu3FkRERGy2%2B2KiYmRJH3xxRc6ffq0EhMTm7y%2B6Ojo824HlpdXqq7Ouzd8fX2D19v6E1%2BcQ1vJzlfIz3tk5z2yax7y8w%2Bt8kZthw4dZLValZeXp9OnT%2BvQoUN66aWXNHHiRN12220qKSlRfn6%2BTp06pZ07d2rnzp2aMGGCJCk9PV3vvPOOPv30U9XU1GjlypUKDg7W4MGDFRQUpAkTJmjVqlUqLS2Vw%2BHQCy%2B8oOHDh6tjx44%2BPmsAANBatMorWJ06dVJeXp5yc3PdBWns2LHKzs5WSEiIVq9eraefflpPPPGEYmJitHjxYl177bWSpEGDBumRRx5RVlaWjh07pqSkJOXl5aldu3aSpMzMTFVXV%2Bu2225TXV2dhgwZooULF/rwbAEAQGsT4GruE91okvLyykvexmQKVFRUqByO6gte7r35xQ%2Bbu7QWsynrxhY7VlOyw4WRn/fIzntk1zzk17grrgj3yXFb5S1CAAAAf0bBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDUbAAAAAMRsECAAAwGAULAADAYBQsAAAAg1GwAAAADEbBAgAAMBgFCwAAwGAULAAAAINRsAAAAAxGwQIAADAYBQsAAMBgFCwAAACDmXy9APx43Pzih75ewiXZlHWjr5cAAGiluIIFAABgMAoWAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMDxptRElJiZ544gnt2bNH7du31y233KKZM2cqMJA%2B%2BmPCB6MCALxFwWrEQw89pISEBG3dulXHjh3T1KlT1bFjR/3617/29dIAAEArwCWZ77DZbNq/f79ycnIUHh6u2NhYTZ48WVar1ddLAwAArQRXsL7DbrcrJiZGERER7rGEhAQdOnRIVVVVCgsLu%2Bg%2BysrKVF5e7jFmMrVXdHT0Ja0lKCjQ4zvwfVrbLc3W5M85NzX5tfx36z2yax7y8y8UrO9wOp0ym80eY%2BfKlsPhaFLBslqtWr58ucfYgw8%2BqIceeuiS1lJWVqZXX/29Jk6ceMFy9tEzv7ikff5YlJWVyWq1fm92uDDy815T/rtF48iuecjPv1BzG%2BFyuZq1/cSJE/XWW295fE2cOPGS91NeXq7ly5efdzUMF0d2zUN%2B3iM775Fd85Cff%2BEK1ndYLBY5nU6PMafTqYCAAFkslibtIzo6mp8eAAD4EeMK1nckJiaqtLRUx48fd4/ZbDZ169ZNoaGhPlwZAABoLShY3xEfH6%2BkpCTl5uaqqqpKxcXFWrNmjdLT0329NAAA0EoELVy4cKGvF%2BFvbrrpJhUWFuqpp57Sn/70J6WlpWnKlCkKCAho8bWEhoaqf//%2BXD3zAtk1D/l5j%2By8R3bNQ37%2BI8DV3Ce6AQAA4IFbhAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwShYAAAABqNg%2BamSkhI98MADSk5O1pAhQ7R48WI1NDT4ell%2B6/3331dKSoqys7PPm3v33Xc1evRoXXfddRo3bpw%2B%2BOADH6zQP5WUlCgjI0PJyclKSUnR7NmzVVFRIUnat2%2Bf7rzzTvXt21cjRozQK6%2B84uPV%2Bp/9%2B/frnnvuUd%2B%2BfZWSkqKsrCyVl5dLknbv3q20tDT16dNHo0aN0oYNG3y8Wv/17LPPKi4uzv1vsvt%2BcXFxSkxMVFJSkvvrqaeekkR2fsUFvzR27FjX448/7qqoqHAdOnTINWLECNcrr7zi62X5pby8PNeIESNckyZNcmVlZXnM7d2715WYmOjasWOHq7a21rV%2B/XpX7969XaWlpT5arX%2B59dZbXbNnz3ZVVVW5SktLXePGjXPNmTPHVVNT47rppptcy5Ytc1VXV7uKiopc/fv3d23evNnXS/Ybp06dct1www2u5cuXu06dOuU6duyY684773TNmDHD9c0337h%2B9rOfufLz8121tbWuDz/80NWrVy/XZ5995utl%2B529e/e6%2Bvfv7%2BrRo4fL5XKRXRP06NHD9dVXX503Tnb%2BhStYfshms2n//v3KyclReHi4YmNjNXnyZFmtVl8vzS%2BFhISooKBA11xzzXlz%2Bfn5Sk1NVWpqqkJCQjRmzBj16NGDn%2BokVVRUKDExUTNnzlRoaKiuvPJKjR07Vh999JF27NihM2fOaPr06Wrfvr0SEhI0fvx43oP/paamRtnZ2Zo6daqCg4NlsVg0fPhwffnll9q4caNiY2OVlpamkJAQpaSkaOjQocrPz/f1sv1KQ0ODFixYoMmTJ7vHyM57ZOdfKFh%2ByG63KyYmRhEREe6xhIQEHTp0SFVVVT5cmX%2B6%2B%2B67FR4e3uic3W5XfHy8x1h8fLxsNltLLM2vmc1mLVq0SB07dnSPlZaWKjo6Wna7XXFxcQoKCnLPxcfHq6ioyBdL9UsREREaP368TCaTJOngwYN6%2B%2B23dfPNN1/wfUd%2Bnt58802FhIRo9OjR7jGya5rc3FwNHjxY119/vebNm6fq6mqy8zMULD/kdDplNps9xs6VLYfD4YsltVpOp9OjqEpnsyTH89lsNr3%2B%2BuuaPn16o%2B/ByMhIOZ1OngX8jpKSEiUmJuqWW25RUlKSMjMzL5gf77v/%2BPbbb7Vs2TItWLDAY5zsLu5nP/uZUlJStGXLFlmtVn366ad64oknyM7PULD8lMvl8vUS2gyyvLiPP/5YU6ZM0cyZM5WSknLB1wUEBLTgqlqHmJgY2Ww2vffeezp8%2BLAeffRRXy%2BpVVi0aJHGjRunbt26%2BXoprY7VatX48eMVHBysrl27KicnR4WFhTpz5oyvl4b/QsHyQxaLRU6n02PM6XQqICBAFovFR6tqnaKiohrNkhz/Y9u2bXrggQc0Z84c3X333ZLOvge/%2B1Ov0%2BlUZGSkAgP5n43vCggIUGxsrLKzs1VYWCiTyXTe%2B87hcPC%2B%2B/92796tf/3rX8rIyDhvrrH/Zsnu%2B1199dWqr69XYGAg2fkR/pfSDyUmJqq0tFTHjx93j9lsNnXr1k2hoaE%2BXFnrk5iYeN7zBzabTb179/bRivzLJ598olmzZumll17S7bff7h5PTEzU559/rrq6OvcYuXnavXu3Ro4c6XHL9Fz57NWr13nvu6KiIvL7/zZs2KBjx45pyJAhSk5O1rhx4yRJycnJ6tGjB9l9j7179%2Bq5557zGCsuLlZwcLBSU1PJzo9QsPxQfHy8kpKSlJubq6qqKhUXF2vNmjVKT0/39dJanQkTJmjXrl3asWOHTp06pYKCAh0%2BfFhjxozx9dJ8rq6uTo8//rhycnI0cOBAj7nU1FSFhYVp5cqVqqmp0Z49e1RQUMB78L8kJiaqqqpKixcvVk1NjY4fP65ly5bp%2BuuvV3p6ukpKSpSfn69Tp05p586d2rlzpyZMmODrZfuF2bNna/PmzVq/fr3Wr1%2BvvLw8SdL69es1evRosvseHTp0kNVqVV5enk6fPq1Dhw7ppZde0sSJE3XbbbeRnR8JcPGAil86evSo5s2bp3/84x8KCwvTpEmT9OCDD/IMTCOSkpIkyX215dxvdZ37TcEtW7YoNzdXJSUl6tatm%2BbOnat%2B/fr5ZrF%2B5KOPPtKvfvUrBQcHnzf33nvvqbq6WgsWLFBRUZE6duyo%2B%2B%2B/X3fccYcPVuq/Pv/8cz399NP67LPP1L59ew0YMECzZ89Wp06d9M9//lNPP/20iouLFRMTo5kzZ2rEiBG%2BXrJf%2BvrrrzVs2DB9/vnnkkR2F/HPf/5Tubm5%2BvzzzxUcHKyxY8cqOztbISEhZOdHKFgAAAAG4xYhAACAwShYAAAABqNgAQAAGIyCBQAAYDAKFgAAgMEoWAAAAAajYAEAABiMggUAAGAwChYAAIDBKFgAAAAGo2ABAAAYjIIFAABgMAoWAACAwf4flvZHqkfs/3UAAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-2173299089784530331\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">1</td>\n",
              "        <td class=\"number\">17642</td>\n",
              "        <td class=\"number\">42.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">10570</td>\n",
              "        <td class=\"number\">25.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:60%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">5341</td>\n",
              "        <td class=\"number\">13.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">2651</td>\n",
              "        <td class=\"number\">6.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">5</td>\n",
              "        <td class=\"number\">1599</td>\n",
              "        <td class=\"number\">3.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">6</td>\n",
              "        <td class=\"number\">979</td>\n",
              "        <td class=\"number\">2.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">7</td>\n",
              "        <td class=\"number\">629</td>\n",
              "        <td class=\"number\">1.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">8</td>\n",
              "        <td class=\"number\">400</td>\n",
              "        <td class=\"number\">1.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">9</td>\n",
              "        <td class=\"number\">283</td>\n",
              "        <td class=\"number\">0.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">10</td>\n",
              "        <td class=\"number\">225</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (32)</td>\n",
              "        <td class=\"number\">869</td>\n",
              "        <td class=\"number\">2.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-2173299089784530331\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">1</td>\n",
              "        <td class=\"number\">17642</td>\n",
              "        <td class=\"number\">42.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">10570</td>\n",
              "        <td class=\"number\">25.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:60%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">5341</td>\n",
              "        <td class=\"number\">13.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:30%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">2651</td>\n",
              "        <td class=\"number\">6.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:15%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">5</td>\n",
              "        <td class=\"number\">1599</td>\n",
              "        <td class=\"number\">3.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">40</td>\n",
              "        <td class=\"number\">2</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">41</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">42</td>\n",
              "        <td class=\"number\">2</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">43</td>\n",
              "        <td class=\"number\">2</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">56</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:50%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_pdays\">pdays<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>27</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.1%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>962.48</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>999</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram3342543727843380110\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABCUlEQVR4nO3VwQkCURAFQRVDMghz8mxOBrE5jXeRBoXli1TdB96lmePMzAF467R6APyy8%2BoB/K/L7fHxzXa/7rDkez4IBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAOK8e8Opye3x8s92vOywBHwTScWZm9Qj4VT4IBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIhCf/Pw6RlZ2F8gAAAABJRU5ErkJggg%3D%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives3342543727843380110,#minihistogram3342543727843380110\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives3342543727843380110\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles3342543727843380110\"\n",
              "                                                  aria-controls=\"quantiles3342543727843380110\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram3342543727843380110\" aria-controls=\"histogram3342543727843380110\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common3342543727843380110\" aria-controls=\"common3342543727843380110\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme3342543727843380110\" aria-controls=\"extreme3342543727843380110\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles3342543727843380110\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>999</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>186.91</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>0.1942</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>22.229</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>962.48</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>70.362</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>-4.9222</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>39642439</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>34936</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram3342543727843380110\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nOzdeVxVdeL/8TdLYgKXRQP7khOOO4uUphiauJRWppXhQtOYk%2BZGEiSTlpm2aY2alfpVmZn8ZtbEV21yadHMpc2pscUuV61EHYsfCsq9sggqcH5/9PB%2Bu2mKeY5w8fV8PHg4fj7nfM4573ngvOecw8XHMAxDAAAAMI1vXZ8AAABAQ0PBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGT%2BdX0Cl4qiolLT1/T19VF4eKCKi8tVU2OYvv6ljGytRb7WIVvrkK21rMr3iiuCTVvrfHAHy4v5%2BvrIx8dHvr4%2BdX0qDQ7ZWot8rUO21iFbazW0fClYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJjMKwrWzJkz1a5dO/fft23bppSUFHXq1EkDBgzQmjVrPLZftmyZ%2Bvfvr06dOik1NVW5ubnuuePHj%2Bvxxx9Xz549lZiYqPT0dDmdTvd8fn6%2BxowZo8TERPXu3VuzZ89WTU2N9RcJAAAajHpfsHbt2qXVq1e7/15YWKgJEyZo%2BPDh2rZtm6ZOnapp06bJbrdLkjZt2qT58%2BfrL3/5iz799FP17t1b48aN07FjxyRJ8%2BbNk8PhUE5OjtavXy/DMPTII4%2B41584caIiIyO1ceNGLV26VBs3btQrr7xycS8aAAB4tXpdsGpqajR9%2BnSNHDnSPbZ27VpFR0crJSVFAQEBSkpKUp8%2BfbRixQpJUk5OjgYPHqyEhAQ1btxYo0ePliRt3rxZVVVVWrlypSZMmKArr7xSoaGhysjI0JYtW3To0CHZ7Xbt3r1bWVlZCg4OVnR0tEaOHKmcnJy6uHwAAOCl6nXBeuONNxQQEKCBAwe6xxwOh2JiYjy2i4mJcT8G/OW8r6%2BvOnToILvdrgMHDqi0tFSxsbHu%2BVatWqlx48ZyOBxyOByKiopSSEiIez42Nlb79u1TWVmZVZcJAAAamHr7uwgPHz6s%2BfPn69VXX/UYd7lcioyM9BgLDQ11v0flcrk8CpIkhYSEyOl0yuVySZJsNpvHvM1mc8//cu7UWk6nU0FBQbU698LCQhUVFXmM%2Bfs3UURERK32ry0/P1%2BPP2EesrUW%2BVqHbK1DttZqaPnW24I1a9YsDR48WK1bt9aPP/54Xvsaxtl/SeTZ5s%2B1b23k5ORowYIFHmNpaWlKT0%2B/4LXPxGa73JJ1QbZWI1/rkK11yNZaDSXfelmwtm3bpq%2B%2B%2Bkrr1q07bS4sLMx9J%2BoUp9Op8PDwX513uVxq06aNexuXy6XAwED3/NGjR9W0aVNVV1efcV8fHx/3vrUxbNgw9enTx2PM37%2BJnM7yWq9RG35%2BvrLZLldJSYWqq/lJRzORrbXI1zpkax2ytZZV%2BYaFBZ57IwvUy4K1Zs0aHTlyRL1795b0f3eVEhMTdd99951WvHJzc5WQkCBJiouLk8Ph0J133ilJqq6u1s6dO5WSkqIWLVooJCTE/a6VJH333Xc6ceKE4uLiVFhYqIKCAhUXF7sLld1uV%2BvWrT0K2blERESc9jiwqKhUVVXWfENWV9dYtvaljmytRb7WIVvreFO2t7zwSV2fQq29n3WDJO/K92zq5YPOKVOmaP369Vq9erVWr16t7OxsSdLq1as1cOBA5efna8WKFTp%2B/Li2bt2qrVu3aujQoZKk1NRUvfXWW/r6669VUVGhRYsWqVGjRurVq5f8/Pw0dOhQLV68WAUFBXI6nXr%2B%2Bed10003qVmzZoqJiVF8fLzmzp2rsrIy5eXlaenSpUpNTa3LOAAAgJepl3ewQkJCPF5Ur6qqkiQ1b95ckrRkyRI9/fTTeuKJJxQVFaXZs2erffv2kqSePXvqoYceUkZGho4cOaL4%2BHhlZ2ercePGkqT09HSVl5fr9ttvV1VVlXr37q0ZM2a4j/XSSy9p2rRp6t69u4KCgjR8%2BHDdfffdF%2BnKAQBAQ%2BBjmPFWN86pqKjU9DX9/X0VFhYop7O8QdxOrU/I1lrkax2ytY43ZuttjwityPeKK4JNW%2Bt81MtHhAAAAN6MggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmq7cFa/fu3br33nvVuXNnJSUlKSMjQ0VFRfrss8/Url07xcfHe3y9%2B%2B677n2XLVum/v37q1OnTkpNTVVubq577vjx43r88cfVs2dPJSYmKj09XU6n0z2fn5%2BvMWPGKDExUb1799bs2bNVU1NzUa8dAAB4t3pZsE6cOKH77rtPXbt21bZt27Ru3TodOXJEM2bMkCRFRUXJbrd7fN1yyy2SpE2bNmn%2B/Pn6y1/%2Bok8//VS9e/fWuHHjdOzYMUnSvHnz5HA4lJOTo/Xr18swDD3yyCPuY0%2BcOFGRkZHauHGjli5dqo0bN%2BqVV1656BkAAADvVS8LVkVFhTIzMzV27Fg1atRI4eHhuummm/T999%2Bfc9%2BcnBwNHjxYCQkJaty4sUaPHi1J2rx5s6qqqrRy5UpNmDBBV155pUJDQ5WRkaEtW7bo0KFDstvt2r17t7KyshQcHKzo6GiNHDlSOTk5Vl8yAABoQPzr%2BgTOJCQkREOGDHH/fe/evfrnP//pvktVXl6utLQ0bd%2B%2BXY0aNdJ9992nkSNHysfHRw6HQ7feeqt7X19fX3Xo0EF2u10dOnRQaWmpYmNj3fOtWrVS48aN5XA4VFhYqKioKIWEhLjnY2NjtW/fPpWVlSkoKKhW519YWKiioiKPMX//JoqIiPhNefwaPz9fjz9hHrK1Fvlah2ytQ7bWamj51suCdUp%2Bfr769%2B%2BvqqoqDR06VOnp6dq9e7fatm2re%2B%2B9V/PmzdPnn3%2BuBx98UMHBwUpJSZHL5fIoSNJPhc3pdMrlckmSbDabx7zNZnPP/3Lu1FpOp7PWBSsnJ0cLFizwGEtLS1N6evp5XX9t2WyXW7IuyNZq5GsdsrUO2VrjVK4NJd96XbBOvWv1n//8R48//rgefvhhzZ07V6%2B%2B%2Bqp7mx49emj48OF68803lZKSIkkyDOOs655t/lz71sawYcPUp08fjzF//yZyOssveO2f8/Pzlc12uUpKKlRdzYv4ZiJba5GvdcjWOmRrrZKSCkvyDQsLNG2t81GvC5Yk%2Bfj4KDo6WpmZmRo%2BfLimTp2q8PBwj22ioqK0fv16SVJYWJj7TtUpLpdLbdq0ce/ncrkUGPh/gR89elRNmzZVdXX1Gff18fE57ZhnExERcdrjwKKiUlVVWfMNWV1dY9nalzqytRb5WodsrUO21jhVqhpKvvXyQee2bdvUv39/j49H8PX96VS3bt2q119/3WP7vXv3qkWLFpKkuLg4ORwO91x1dbV27typhIQEtWjRQiEhIR7z3333nU6cOKG4uDjFxcWpoKBAxcXF7nm73a7WrVt7FDIAAICzqZcFKy4uTmVlZZo9e7YqKipUXFys%2BfPn67rrrlNwcLCee%2B45ffzxxzp58qQ%2B%2BeQTrVq1SqmpqZKk1NRUvfXWW/r6669VUVGhRYsWqVGjRurVq5f8/Pw0dOhQLV68WAUFBXI6nXr%2B%2Bed10003qVmzZoqJiVF8fLzmzp2rsrIy5eXlaenSpe61AQAAaqNePiIMDg7Wyy%2B/rKefflrdunVTkyZN1K1bNz3zzDOKjIzUo48%2BqqeeekoFBQVq1qyZHn30UfXr10%2BS1LNnTz300EPKyMjQkSNHFB8fr%2BzsbDVu3FiSlJ6ervLyct1%2B%2B%2B2qqqpS79693Z%2BvJUkvvfSSpk2bpu7duysoKEjDhw/X3XffXRcxAAAAL%2BVjmPFWN86pqKjU9DX9/X0VFhYop7O8QTyvrk/I1lrkax2ytY43ZnvLC5/U9SnU2vtZN1iS7xVXBJu21vmol48IAQAAvBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJPV24K1e/du3XvvvercubOSkpKUkZGhoqIiSdK2bduUkpKiTp06acCAAVqzZo3HvsuWLVP//v3VqVMnpaamKjc31z13/PhxPf744%2BrZs6cSExOVnp4up9Ppns/Pz9eYMWOUmJio3r17a/bs2aqpqbk4Fw0AABqEelmwTpw4ofvuu09du3bVtm3btG7dOh05ckQzZsxQYWGhJkyYoOHDh2vbtm2aOnWqpk2bJrvdLknatGmT5s%2Bfr7/85S/69NNP1bt3b40bN07Hjh2TJM2bN08Oh0M5OTlav369DMPQI4884j72xIkTFRkZqY0bN2rp0qXauHGjXnnllTrJAQAAeKd6WbAqKiqUmZmpsWPHqlGjRgoPD9dNN92k77//XmvXrlV0dLRSUlIUEBCgpKQk9enTRytWrJAk5eTkaPDgwUpISFDjxo01evRoSdLmzZtVVVWllStXasKECbryyisVGhqqjIwMbdmyRYcOHZLdbtfu3buVlZWl4OBgRUdHa%2BTIkcrJyanLOAAAgJeplwUrJCREQ4YMkb%2B/vyRp7969%2Buc//6lbbrlFDodDMTExHtvHxMS4HwP%2Bct7X11cdOnSQ3W7XgQMHVFpaqtjYWPd8q1at1LhxYzkcDjkcDkVFRSkkJMQ9Hxsbq3379qmsrMzKSwYAAA2If12fwNnk5%2Berf//%2Bqqqq0tChQ5Wenq77779fkZGRHtuFhoa636NyuVweBUn6qbA5nU65XC5Jks1m85i32Wzu%2BV/OnVrL6XQqKCioVuddWFjofl/sFH//JoqIiKjV/rXl5%2Bfr8SfMQ7bWIl/rkK11yNZaDS3fel2woqKiZLfb9Z///EePP/64Hn744VrtZxjGb54/1761kZOTowULFniMpaWlKT09/YLXPhOb7XJL1gXZWo18rUO21iFba5zKtaHkW68LliT5%2BPgoOjpamZmZGj58uJKTk913ok5xOp0KDw%2BXJIWFhZ0273K51KZNG/c2LpdLgYGB7vmjR4%2BqadOmqq6uPuO%2BPj4%2B7n1rY9iwYerTp4/HmL9/Ezmd5bVeozb8/Hxls12ukpIKVVfzk45mIltrka91yNY6ZGutkpIKS/INCws890YWqJcFa9u2bZoxY4beffdd%2Bfr%2BdKvw1J8dO3bU%2BvXrPbbPzc1VQkKCJCkuLk4Oh0N33nmnJKm6ulo7d%2B5USkqKWrRooZCQEPe7VpL03Xff6cSJE4qLi1NhYaEKCgpUXFzsLlR2u12tW7f2KGTnEhERcdrjwKKiUlVVWfMNWV1dY9nalzqytRb5WodsrUO21jhVqhpKvvXyQWdcXJzKyso0e/ZsVVRUqLi4WPPnz9d1112n1NRU5efna8WKFTp%2B/Li2bt2qrVu3aujQoZKk1NRUvfXWW/r6669VUVGhRYsWqVGjRurVq5f8/Pw0dOhQLV68WAUFBXI6nXr%2B%2Bed10003qVmzZoqJiVF8fLzmzp2rsrIy5eXlaenSpUpNTa3jRAAAgDeplwUrODhYL7/8snJzc9WtWzcNGDBAwcHBev7559W0aVMtWbJEy5cvV%2BfOnTVz5kzNnj1b7du3lyT17NlTDz30kDIyMtS1a1d9%2Bumnys7OVuPGjSVJ6enpSkhI0O23366%2BffsqMDBQzzzzjPvYL730kgoLC9W9e3eNGDFCd9xxh%2B6%2B%2B%2B46yQEAAHgnH8OMt7pxTkVFpaav6e/vq7CwQDmd5Q3idmp9QrbWIl/rkK11vDHbW174pK5Podbez7rBknyvuCLYtLXOR728gwUAAODNKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYrN4WrPz8fKWlpSkxMVFJSUmaMmWKSkpK9OOPP6pdu3aKj4/3%2BPr73//u3vedd97RwIEDde2112rw4MH6%2BOOP3XM1NTWaN2%2Be%2Bvbtqy5dumjUqFH64Ycf3PMul0sZGRlKSkpSjx49NHXqVFVWVl7UawcAAN6t3hascePGyWazadOmTXrzzTf1/fff67nnnnPP2%2B12j69Ro0ZJknbt2qXJkycrKytL//rXvzRy5Eg98MADOnjwoCTptdde09q1a5Wdna3NmzcrOjpaaWlpMgxDkjRt2jRVVFRo3bp1WrVqlfLy8jRnzpyLHwAAAPBa9bJglZSUKC4uTpMmTVJgYKCaN2%2BuO%2B%2B8U9u3bz/nvitWrFBycrKSk5MVEBCgQYMGqW3btlqzZo0kKScnRyNHjlSrVq0UFBSkzMxM5eXlaceOHTp8%2BLA2btyozMxMhYeHKzIyUhMmTNCqVat08uRJqy8bAAA0EPWyYNlsNs2aNUvNmjVzjxUUFCgiIsL994cfflg9evRQt27dNHfuXHcBcjgciomJ8VgvJiZGdrtdlZWV2rNnj8d8UFCQrr76atntdu3atUt%2Bfn5q166dez42NlbHjh3T3r17rbpcAADQwPibvWCfPn00ePBg3XXXXbryyitNWdNut2v58uVatGiRGjVqpGuvvVY33XSTnnnmGe3atUsTJ06Uv7%2B/HnzwQblcLoWEhHjsHxISoj179ujo0aMyDOOM806nU6GhoQoKCpKPj4/HnCQ5nc5an29hYaGKioo8xvz9m3gURDP4%2Bfl6/AnzkK21yNc6ZGsdsrVWQ8vX9IJ111136e2339aiRYt0/fXXa%2BjQoerTp4/8/X/bob744guNHz9ekyZNUlJSkiTpjTfecM937NhRY8eO1ZIlS/Tggw9Kkvt9ql9ztvlz7VsbOTk5WrBggcdYWlqa0tPTL3jtM7HZLrdkXZCt1cjXOmRrHbK1xqlcG0q%2BphestLQ0paWlyeFwaN26dZo5c6aeeOIJ3XHHHUpJSVHLli1rvdamTZv05z//WdOmTdMdd9zxq9tFRUXp8OHDMgxDYWFhcrlcHvMul0vh4eEKDQ2Vr6/vGeebNm2q8PBwlZWVqbq6Wn5%2Bfu45SWratGmtz3vYsGHq06ePx5i/fxM5neW1XqM2/Px8ZbNdrpKSClVX15i69qWObK1FvtYhW%2BuQrbVKSiosyTcsLNC0tc6H6QXrlNjYWMXGxurhhx/WO%2B%2B8oxkzZujll19WUlKSHnzwQXXs2PGs%2B3/55ZeaPHmyXnzxRfXo0cM9vm3bNn399dcaP368e2zv3r2KioqSj4%2BP4uLilJub67GW3W7XgAEDFBAQoDZt2sjhcKhr166Sfnqh/sCBA%2BrYsaOioqJkGIZ2796t2NhY9742m%2B28imFERMRpjwOLikpVVWXNN2R1dY1la1/qyNZa5GsdsrUO2VrjVKlqKPla9qDz5MmTeuedd3T//fdr8uTJioyM1COPPKIOHTpo5MiRWrt27a/uW1VVpccee0xZWVke5UqSgoODtXDhQq1evVonT56U3W7X3//%2Bd6WmpkqShg4dqk8//VRbtmzR8ePHtXLlSu3fv1%2BDBg2SJKWmpmrZsmXKy8tTWVmZ5syZow4dOig%2BPl7h4eHq37%2B/XnjhBRUXF%2BvgwYNauHChUlJSfvMjTgAAcOnxMcx46ehn8vLytHLlSr311lsqLy9X//79NXz4cHXu3Nm9zdatWzVjxgxt3rz5jGts375df/jDH9SoUaPT5t577z3t3LlTCxYs0P79%2BxUcHKw//vGPuv/%2B%2B%2BXr%2B1Nf3LBhg%2BbOnav8/Hy1bt1aU6dOVZcuXST99I7V/Pnz9cYbb6i8vFyJiYl68skn1bx5c0lSaWmppk%2Bfrs2bN%2Buyyy7TbbfdpilTppzxXM5HUVHpBe1/Jv7%2BvgoLC5TTWd4g2n59QrbWIl/rkK11vDHbW174pK5Podbez7rBknyvuCLYtLXOh%2BkFq3379mrZsqWGDRumO%2B64Q6GhoWfcLiEhQTt27DDz0PUaBcu7kK21yNc6ZGsdb8yWglV3Bcv0517Lli1zv990NpdSuQIAAJcW09/BateuncaNG6eNGze6x/7nf/5H999//2k/vQcAANAQmV6wZs2apdLSUrVu3do91qtXL9XU1OjZZ581%2B3AAAAD1jumPCD/%2B%2BGOtXbtWYWFh7rHo6GjNmTNHt912m9mHAwAAqHdMv4NVWVmpgICA0w/k66uKigqzDwcAAFDvmF6wunTpomeffVZHjx51jx06dEhPPPGEx0c1AAAANFSmPyJ89NFHdd999%2Bn6669XUFCQampqVF5erhYtWujVV181%2B3AAAAD1jukFq0WLFnr77bf14Ycf6sCBA/L19VXLli3Vo0cP9%2B/3AwAAaMgs%2Bf0vjRo10o033mjF0gAAAPWe6QXrhx9%2B0Ny5c/X999%2BrsrLytPkPPvjA7EMCAADUK5a8g1VYWKgePXqoSZMmZi8PAABQ75lesHJzc/XBBx8oPDzc7KUBAAC8gukf09C0aVPuXAEAgEua6QVr7NixWrBggQzDMHtpAAAAr2D6I8IPP/xQX375pd58801dddVV8vX17HBvvPGG2YcEAACoV0wvWEFBQerZs6fZywIAAHgN0wvWrFmzzF4SAADAq5j%2BDpYk7d27V/Pnz9cjjzziHvvqq6%2BsOBQAAEC9Y3rB2rZtmwYNGqQNGzZo3bp1kn768NERI0bwIaMAAOCSYHrBmjdvnv785z9r7dq18vHxkfTT7yd89tlntXDhQrMPBwAAUO%2BYXrC%2B%2B%2B47paamSpK7YEnSzTffrLy8PLMPBwAAUO%2BYXrCCg4PP%2BDsICwsL1ahRI7MPBwAAUO%2BYXrA6deqkmTNnqqyszD22b98%2BTZ48Wddff73ZhwMAAKh3TP%2BYhkceeUT33nuvEhMTVV1drU6dOqmiokJt2rTRs88%2Ba/bhAAAA6h3TC1bz5s21bt06bd26Vfv27VPjxo3VsmVLde/e3eOdLAAAgIbK9IIlSZdddpluvPFGK5YGAACo90wvWH369DnrnSo%2BCwsAADR0phesW2%2B91aNgVVdXa9%2B%2BfbLb7br33nvNPhwAAEC9Y3rBysrKOuP4%2BvXr9dlnn9V6nfz8fM2cOVPbt2%2BXn5%2BfevbsqUcffVQ2m027du3SM888o127dqlp06YaPny47rvvPve%2B77zzjhYtWqQff/xRLVu21EMPPaQePXpIkmpqavTiiy9q3bp1KikpUceOHTVjxgy1aNFCkuRyuTRjxgx9/vnn8vX1VXJysqZNm6bGjRtfQCoAAOBSYsnvIjyTG2%2B8UW%2B//Xattx83bpxsNps2bdqkN998U99//72ee%2B45VVZWauzYserWrZs%2B%2BugjzZs3T0uWLNGGDRskSbt27dLkyZOVlZWlf/3rXxo5cqQeeOABHTx4UJL02muvae3atcrOztbmzZsVHR2ttLQ0GYYhSZo2bZoqKiq0bt06rVq1Snl5eZozZ475gQAAgAbrohWsnTt3ukvMuZSUlCguLk6TJk1SYGCgmjdvrjvvvFPbt2/Xli1bdPLkSY0fP15NmjRRbGyshgwZopycHEnSihUrlJycrOTkZAUEBGjQoEFq27at1qxZI0nKycnRyJEj1apVKwUFBSkzM1N5eXnasWOHDh8%2BrI0bNyozM1Ph4eGKjIzUhAkTtGrVKp08edKybAAAQMNi%2BiPC4cOHnzZWUVGhvLw89evXr1Zr2Gw2zZo1y2OsoKBAERERcjgcateunfz8/NxzMTExWrFihSTJ4XAoOTnZY9%2BYmBjZ7XZVVlZqz549iomJcc8FBQXp6quvlt1uV2lpqfz8/NSuXTv3fGxsrI4dO6a9e/d6jJ9NYWGhioqKPMb8/ZsoIiKiVvvXlp%2Bfr8efMA/ZWot8rUO21iFbazW0fE0vWNHR0af9FGFAQIBSUlI0ZMiQ37Sm3W7X8uXLtWjRIr377ruy2Wwe86GhoXK5XKqpqZHL5VJISIjHfEhIiPbs2aOjR4/KMIwzzjudToWGhiooKMjj/E9t63Q6a32%2BOTk5WrBggcdYWlqa0tPTa73G%2BbDZLrdkXZCt1cjXOmRrHbK1xqlcG0q%2Bphcssz%2Bt/YsvvtD48eM1adIkJSUl6d133z3jdj8vRed6FHm2%2Bdo%2BxjybYcOGqU%2BfPh5j/v5N5HSWX/DaP%2Bfn5yub7XKVlFSourrG1LUvdWRrLfK1Dtlah2ytVVJSYUm%2BYWGBpq11PkwvWG%2B99Vatt73jjjvOOr9p0yb9%2Bc9/1rRp09zbhoeHa//%2B/R7buVwuhYaGytfXV2FhYXK5XKfNh4eHu7c503zTpk0VHh6usrIyVVdXux9Bntq2adOmtb6uiIiI0x4HFhWVqqrKmm/I6uoay9a%2B1JGttcjXOmRrHbK1xqlS1VDyNb1gTZ06VTU1NafdCfLx8fEY8/HxOdS9rSEAAB/wSURBVGvB%2BvLLLzV58mS9%2BOKL7o9YkKS4uDj94x//UFVVlfz9fzp9u92uhIQE93xubq7HWna7XQMGDFBAQIDatGkjh8Ohrl27SvrphfoDBw6oY8eOioqKkmEY2r17t2JjY9372mw2tWzZ8gJSAQAAlxLT3yT729/%2Bph49eui1117T9u3b9e9//1vLly9Xz5499de//lXffPONvvnmG%2B3YseNX16iqqtJjjz2mrKwsj3IlScnJyQoKCtKiRYtUUVGhHTt2aOXKlUpNTZUkDR06VJ9%2B%2Bqm2bNmi48ePa%2BXKldq/f78GDRokSUpNTdWyZcuUl5ensrIyzZkzRx06dFB8fLzCw8PVv39/vfDCCyouLtbBgwe1cOFCpaSkuMscAADAufgYZrx09DO33367srOzFRkZ6TF%2B6NAhjRo1SuvWrTvnGtu3b9cf/vAHNWrU6LS59957T%2BXl5Zo%2Bfbpyc3PVrFkz3X///br77rvd22zYsEFz585Vfn6%2BWrduralTp6pLly6SfnrHav78%2BXrjjTdUXl6uxMREPfnkk2revLkkqbS0VNOnT9fmzZt12WWX6bbbbtOUKVPOeC7no6io9IL2PxN/f1%2BFhQXK6SxvELdT6xOytRb5WodsreON2d7ywid1fQq19n7WDZbke8UVwaatdT5ML1gJCQn67LPPTvvk84qKCiUlJemrr74y83Beg4LlXcjWWuRrHbK1jjdmS8Gqu4Jl%2BiPCqKgoPfvssx4fa1BSUqK5c%2Bfqd7/7ndmHAwAAqHdMf7Ho0Ucf1aRJk5STk6PAwED5%2BvqqrKxMjRs31sKFC80%2BHAAAQL1jesHq0aOHtmzZoq1bt%2BrgwYMyDEORkZG64YYbFBxcN7fpAAAALiZLfjTu8ssvV9%2B%2BfXXw4EG1aNHCikMAAADUW6a/g1VZWanJkyfr2muv1S233CLpp3ewRo8erZKSErMPBwAAUO%2BYXrBmz56tXbt2ac6cOfL1/b/lq6urNWfOHLMPBwAAUO%2BYXrDWr1%2Bvl156STfffLP79wPabDbNmjVLGzZsMPtwAAAA9Y7pBau8vFzR0dGnjYeHh%2BvYsWNmHw4AAKDeMb1g/e53v9Nnn30mSR6/e/C9997Tf/3Xf5l9OAAAgHrH9J8ivPvuuzVx4kTdddddqqmp0dKlS5Wbm6v169dr6tSpZh8OAACg3jG9YA0bNkz%2B/v5avny5/Pz8tHjxYrVs2VJz5szRzTffbPbhAAAA6h3TC1ZxcbHuuusu3XXXXWYvDQAA4BVMfwerb9%2B%2BMvn3RwMAAHgV0wtWYmKi3n33XbOXBQAA8BqmPyK88sor9cwzzyg7O1u/%2B93vdNlll3nMz5071%2BxDAgAA1CumF6w9e/bo97//vSTJ6XSavTwAAEC9Z1rByszM1Lx58/Tqq6%2B6xxYuXKi0tDSzDgEAAOAVTHsHa9OmTaeNZWdnm7U8AACA1zCtYJ3pJwf5aUIAAHApMq1gnfrFzucaAwAAaOhM/5gGAACASx0FCwAAwGSm/RThyZMnNWnSpHOO8TlYAACgoTOtYHXu3FmFhYXnHAMAAGjoTCtYP//8KwAAgEsZ72ABAACYjIIFAABgsnpdsD766CMlJSUpMzPTY/zNN99U%2B/btFR8f7/H1zTffSJJqamo0b9489e3bV126dNGoUaP0ww8/uPd3uVzKyMhQUlKSevTooalTp6qystI9v2vXLt1zzz3q3Lmz%2BvXrp5dffvniXDAAAGgQ6m3B%2Butf/6qnn35aV1999Rnnu3TpIrvd7vHVsWNHSdJrr72mtWvXKjs7W5s3b1Z0dLTS0tLcnyw/bdo0VVRUaN26dVq1apXy8vI0Z84cSVJlZaXGjh2rbt266aOPPtK8efO0ZMkSbdiw4eJcOAAA8Hr1tmAFBARo5cqVv1qwziYnJ0cjR45Uq1atFBQUpMzMTOXl5WnHjh06fPiwNm7cqMzMTIWHhysyMlITJkzQqlWrdPLkSW3ZskUnT57U%2BPHj1aRJE8XGxmrIkCHKycmx4CoBAEBDVG8L1ogRIxQcHPyr8wUFBfrTn/6kLl26qG/fvlq9erWkn%2B5A7dmzRzExMe5tg4KCdPXVV8tut2vXrl3y8/NTu3bt3POxsbE6duyY9u7dK4fDoXbt2snPz889HxMTo9zcXAuuEgAANESmfUzDxRQeHq7o6Gg99NBDat26td5//309/PDDioiI0O9//3sZhqGQkBCPfUJCQuR0OhUaGqqgoCCP35N4alun0ymXyyWbzeaxb2hoqFwul2pqauTre%2B5OWlhYqKKiIo8xf/8mioiI%2BK2XfEZ%2Bfr4ef8I8ZGst8rUO2VqHbK3V0PL1yoLVq1cv9erVy/33AQMG6P3339ebb76prKwsSXK/b3UmZ5v7Nefzi6tzcnK0YMECj7G0tDSlp6ef93Frw2a73JJ1QbZWI1/rkK11yNYap3JtKPl6ZcE6k6ioKOXm5io0NFS%2Bvr5yuVwe8y6XS02bNlV4eLjKyspUXV3tfgx4attT8/v37z9t31Pr1sawYcPUp08fjzF//yZyOst/49WdmZ%2Bfr2y2y1VSUqHq6hpT177Uka21yNc6ZGsdsrVWSUmFJfmGhQWattb58MqC9Y9//EMhISG69dZb3WN5eXlq0aKFAgIC1KZNGzkcDnXt2lWSVFJSogMHDqhjx46KioqSYRjavXu3YmNjJUl2u102m00tW7ZUXFyc/vGPf6iqqkr%2B/v7u%2BYSEhFqfX0RExGmPA4uKSlVVZc03ZHV1jWVrX%2BrI1lrkax2ytQ7ZWuNUqWoo%2BXrlg84TJ07oqaeekt1u18mTJ7Vu3Tp9%2BOGHGj58uCQpNTVVy5YtU15ensrKyjRnzhx16NBB8fHxCg8PV//%2B/fXCCy%2BouLhYBw8e1MKFC5WSkiJ/f38lJycrKChIixYtUkVFhXbs2KGVK1cqNTW1jq8aAAB4i3p7Bys%2BPl6SVFVVJUnauHGjpJ/uJo0YMULl5eV68MEHVVRUpKuuukoLFy5UXFycJGn48OEqKirSH//4R5WXlysxMdHjnagnn3xS06dPV9%2B%2BfXXZZZfptttuc3%2BYaaNGjbR48WJNnz5d2dnZatasmTIzMz3e%2BQIAADgbH%2BO3vPGN81ZUVGr6mv7%2BvgoLC5TTWd4gbqfWJ2RrLfK1DtlaxxuzveWFT%2Br6FGrt/awbLMn3iit%2B/SOfrOSVjwgBAADqMwoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmKxeF6yPPvpISUlJyszMPG3unXfe0cCBA3Xttddq8ODB%2Bvjjj91zNTU1mjdvnvr27asuXbpo1KhR%2BuGHH9zzLpdLGRkZSkpKUo8ePTR16lRVVla653ft2qV77rlHnTt3Vr9%2B/fTyyy9be6EAAKBBqbcF669//auefvppXX311afN7dq1S5MnT1ZWVpb%2B9a9/aeTIkXrggQd08OBBSdJrr72mtWvXKjs7W5s3b1Z0dLTS0tJkGIYkadq0aaqoqNC6deu0atUq5eXlac6cOZKkyspKjR07Vt26ddNHH32kefPmacmSJdqwYcPFu3gAAODV6m3BCggI0MqVK89YsFasWKHk5GQlJycrICBAgwYNUtu2bbVmzRpJUk5OjkaOHKlWrVopKChImZmZysvL044dO3T48GFt3LhRmZmZCg8PV2RkpCZMmKBVq1bp5MmT2rJli06ePKnx48erSZMmio2N1ZAhQ5STk3OxIwAAAF7Kv65P4NeMGDHiV%2BccDoeSk5M9xmJiYmS321VZWak9e/YoJibGPRcUFKSrr75adrtdpaWl8vPzU7t27dzzsbGxOnbsmPbu3SuHw6F27drJz8/PY%2B0VK1bU%2BtwLCwtVVFTkMebv30QRERG1XqM2/Px8Pf6EecjWWuRrHbK1Dtlaq6HlW28L1tm4XC6FhIR4jIWEhGjPnj06evSoDMM447zT6VRoaKiCgoLk4%2BPjMSdJTqdTLpdLNpvNY9/Q0FC5XC7V1NTI1/fc/8Xn5ORowYIFHmNpaWlKT08/r%2BusLZvtckvWBdlajXytQ7bWIVtrnMq1oeTrlQVLkvt9qt8yf659z%2BTnhexchg0bpj59%2BniM%2Bfs3kdNZft7HPRs/P1/ZbJerpKRC1dU1pq59qSNba5GvdcjWOmRrrZKSCkvyDQsLNG2t8%2BGVBSssLEwul8tjzOVyKTw8XKGhofL19T3jfNOmTRUeHq6ysjJVV1e7HwOe2vbU/P79%2B0/b99S6tREREXHa48CiolJVVVnzDVldXWPZ2pc6srUW%2BVqHbK1DttY4VaoaSr5e%2BaAzLi5Oubm5HmN2u10JCQkKCAhQmzZt5HA43HMlJSU6cOCAOnbsqA4dOsgwDO3evdtjX5vNppYtWyouLk7ffvutqqqqTlsbAACgNryyYA0dOlSffvqptmzZouPHj2vlypXav3%2B/Bg0aJElKTU3VsmXLlJeXp7KyMs2ZM0cdOnRQfHy8wsPD1b9/f73wwgsqLi7WwYMHtXDhQqWkpMjf31/JyckKCgrSokWLVFFRoR07dmjlypVKTU2t46sGAADeot4%2BIoyPj5ck952kjRs3SvrpblLbtm01Z84czZo1S/n5%2BWrdurWWLFmiK664QpI0fPhwFRUV6Y9//KPKy8uVmJjo8dL5k08%2BqenTp6tv37667LLLdNttt7k/zLRRo0ZavHixpk%2BfruzsbDVr1kyZmZnq1avXRbx6AADgzXyM3/LGN85bUVGp6Wv6%2B/sqLCxQTmd5g3heXZ%2BQrbXI1zpkax1vzPaWFz6p61OotfezbrAk3yuuCDZtrfPhlY8IAQAA6jMKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMggUAAGAyChYAAIDJKFgAAAAmo2ABAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACbz2oLVrl07xcXFKT4%2B3v311FNPSZK2bdumlJQUderUSQMGDNCaNWs89l22bJn69%2B%2BvTp06KTU1Vbm5ue6548eP6/HHH1fPnj2VmJio9PR0OZ3Oi3ptAADAu/nX9QlciPfee09XXXWVx1hhYaEmTJigqVOnauDAgfriiy80fvx4tWzZUvHx8dq0aZPmz5%2Bvv/3tb2rXrp2WLVumcePGacOGDWrSpInmzZsnh8OhnJwcXX755Zo2bZoeeeQRLV68uI6uEgAAeBuvvYP1a9auXavo6GilpKQoICBASUlJ6tOnj1asWCFJysnJ0eDBg5WQkKDGjRtr9OjRkqTNmzerqqpKK1eu1IQJE3TllVcqNDRUGRkZ2rJliw4dOlSXlwUAALyIVxesuXPnqlevXrruuus0bdo0lZeXy%2BFwKCYmxmO7mJgY92PAX877%2BvqqQ4cOstvtOnDggEpLSxUbG%2Bueb9WqlRo3biyHw3FxLgoAAHg9r31EeM011ygpKUnPPfecfvjhB2VkZOiJJ56Qy%2BVSZGSkx7ahoaHu96hcLpdCQkI85kNCQuR0OuVyuSRJNpvNY95ms53Xe1iFhYUqKiryGPP3b6KIiIhar1Ebfn6%2BHn/CPGRrLfK1Dtlah2yt1dDy9dqClZOT4/7PrVq1UlZWlsaPH6/OnTufc1/DMC5ovjbntmDBAo%2BxtLQ0paenX9C6v8Zmu9ySdUG2ViNf65CtdcjWGqdybSj5em3B%2BqWrrrpK1dXV8vX1dd%2BJOsXpdCo8PFySFBYWdtq8y%2BVSmzZt3Nu4XC4FBga6548ePaqmTZvW%2BlyGDRumPn36eIz5%2BzeR01l%2BXtd0Ln5%2BvrLZLldJSYWqq2tMXftSR7bWIl/rkK11yNZaJSUVluQbFhZ47o0s4JUFa%2BfOnVqzZo2mTJniHsvLy1OjRo2UnJysf/7znx7b5%2BbmKiEhQZIUFxcnh8OhO%2B%2B8U5JUXV2tnTt3KiUlRS1atFBISIgcDoeioqIkSd99951OnDihuLi4Wp9fRETEaY8Di4pKVVVlzTdkdXWNZWtf6sjWWuRrHbK1Dtla41Spaij5euWDzqZNmyonJ0fZ2dk6ceKE9u3bpxdffFHDhg3T7bffrvz8fK1YsULHjx/X1q1btXXrVg0dOlSSlJqaqrfeektff/21KioqtGjRIjVq1Ei9evWSn5%2Bfhg4dqsWLF6ugoEBOp1PPP/%2B8brrpJjVr1qyOrxoAAHgLr7yDFRkZqezsbM2dO9ddkO68805lZmYqICBAS5Ys0dNPP60nnnhCUVFRmj17ttq3by9J6tmzpx566CFlZGToyJEjio%2BPV3Z2tho3bixJSk9PV3l5uW6//XZVVVWpd%2B/emjFjRh1eLQAA8DY%2BxoW%2B0Y1aKSoqNX1Nf39fhYUFyuksbxC3U%2BsTsrUW%2BVqHbK3jjdne8sIndX0KtfZ%2B1g2W5HvFFcGmrXU%2BvPIRIQAAQH1GwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk1GwAAAATEbBAgAAMBkFCwAAwGQULAAAAJNRsAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBQsAAAAk/nX9Qngwlw39b26PoVaezeje12fAgAAFwV3sAAAAExGwQIAADAZBQsAAMBkFCwAAACTUbAAAABMRsECAAAwGQULAADAZBSsM8jPz9eYMWOUmJio3r17a/bs2aqpqanr0wIAAF6CDxo9g4kTJyo2NlYbN27UkSNHNHbsWDVr1kx/%2BtOf6vrUAACAF%2BAO1i/Y7Xbt3r1bWVlZCg4OVnR0tEaOHKmcnJy6PjUAAOAluIP1Cw6HQ1FRUQoJCXGPxcbGat%2B%2BfSorK1NQUNA51ygsLFRRUZHHmL9/E0VERJh6rn5%2B3tWP/f2953xPZettGXsL8rUO2VqHbK3V0PKlYP2Cy%2BWSzWbzGDtVtpxOZ60KVk5OjhYsWOAx9sADD2jixInmnah%2BKnL3Nv9ew4YNM728XeoKCwv1yit/I1uLkK91yNY63pjt9mdurutTqDVvzPdsGkZNNJlhGBe0/7Bhw/Tmm296fA0bNsyks/s/RUVFWrBgwWl3y3DhyNZa5GsdsrUO2VqroeXLHaxfCA8Pl8vl8hhzuVzy8fFReHh4rdaIiIhoEO0bAAD8NtzB%2BoW4uDgVFBSouLjYPWa329W6dWsFBgbW4ZkBAABvQcH6hZiYGMXHx2vu3LkqKytTXl6eli5dqtTU1Lo%2BNQAA4CX8ZsyYMaOuT6K%2BueGGG7Ru3To99dRTevvtt5WSkqJRo0bJx8enrk/tNIGBgeratSt31yxAttYiX%2BuQrXXI1loNKV8f40Lf6AYAAIAHHhECAACYjIIFAABgMgoWAACAyShYAAAAJqNgAQAAmIyCBQAAYDIKFgAAgMkoWAAAACajYAEAAJiMguWF8vPzNWbMGCUmJqp3796aPXu2ampq6vq0vEZ%2Bfr7S0tKUmJiopKQkTZkyRSUlJZKkXbt26Z577lHnzp3Vr18/vfzyyx77vvPOOxo4cKCuvfZaDR48WB9//HFdXIJXmDlzptq1a%2Bf%2B%2B7Zt25SSkqJOnTppwIABWrNmjcf2y5YtU//%2B/dWpUyelpqYqNzf3Yp%2ByV1i0aJF69Oiha665RiNHjtSPP/4oiXwv1M6dOzVixAhdd9116t69u7KyslRcXCyJbH%2BLjz76SElJScrMzDxt7mz/jtbU1GjevHnq27evunTpolGjRumHH35wz7tcLmVkZCgpKUk9evTQ1KlTVVlZeVGu6bwZ8Dp33nmn8dhjjxklJSXGvn37jH79%2Bhkvv/xyXZ%2BW17jtttuMKVOmGGVlZUZBQYExePBg49FHHzUqKiqMG264wZg/f75RXl5u5ObmGl27djXWr19vGIZh7Ny504iLizO2bNliVFZWGqtXrzYSEhKMgoKCOr6i%2Bmfnzp1G165djbZt2xqGYRiHDh0yrrnmGmPFihVGZWWl8cknnxgdO3Y0vvnmG8MwDOODDz4wrrvuOuPrr782KioqjCVLlhjdu3c3ysvL6/Iy6p3ly5cbN998s5GXl2eUlpYaTz31lPHUU0%2BR7wU6efKk0b17d2Pu3LnG8ePHjeLiYuNPf/qTMXHiRLL9DbKzs41%2B/foZw4cPNzIyMjzmzvXv6LJly4zevXsbe/bsMUpLS40nn3zSGDhwoFFTU2MYhmE88MADxpgxY4wjR44YBw8eNIYNG2Y89dRTF/0aa4OC5WW%2B%2BeYbo0OHDobL5XKPvf7660b//v3r8Ky8x9GjR40pU6YYRUVF7rFXX33V6Nevn/Huu%2B8a3bp1M6qqqtxzs2fPNu677z7DMAzjiSeeMNLS0jzWGzJkiLFkyZKLc/Jeorq62hgyZIjx3//93%2B6C9be//c244447PLbLyMgwpk2bZhiGYYwZM8aYOXOmxxrdu3c31q1bd/FO3Av06dPHXfh/jnwvzP/7f//PaNu2rbFnzx732Ouvv27ceOONZPsbvPLKK0ZJSYkxefLk0wrWuf4dHTBggPHKK6%2B450pLS42YmBjjq6%2B%2BMoqKioz27dsbu3btcs9v3brVuOaaa4wTJ05YeEW/DY8IvYzD4VBUVJRCQkLcY7Gxsdq3b5/Kysrq8My8g81m06xZs9SsWTP3WEFBgSIiIuRwONSuXTv5%2Bfm552JiYty3%2Bx0Oh2JiYjzWi4mJkd1uvzgn7yXeeOMNBQQEaODAge6xX8vu17L19fVVhw4dyPZnDh06pB9//FFHjx7VrbfeqsTERKWnp6u4uJh8L1BkZKQ6dOignJwclZeX68iRI9qwYYN69epFtr/BiBEjFBwcfMa5s/07WllZqT179njMBwUF6eqrr5bdbteuXbvk5%2Bfn8epBbGysjh07pr1791pzMReAguVlXC6XbDabx9ipsuV0OuvilLya3W7X8uXLNX78%2BDNmGxoaKpfLpZqaGrlcLo9iK/2UPbn/n8OHD2v%2B/PmaPn26x/ivZXsqO7I9t4MHD0qS3nvvPS1dulSrV6/WwYMH9dhjj5HvBfL19dX8%2BfP1wQcfqFOnTkpKSlJVVZUmTZpEtiY7W15Hjx6VYRi/Ou9yuRQUFCQfHx%2BPOal%2B/u8fBcsLGYZR16fQIHzxxRcaNWqUJk2apKSkpF/d7uffzGR/drNmzdLgwYPVunXr896XbM/uVD6jR49WZGSkmjdvrokTJ2rTpk3ntT9Od%2BLECY0bN04333yztm/frg8//FDBwcHKysqq1f5ke37OldfZ5r0pawqWlwkPD5fL5fIYc7lc8vHxUXh4eB2dlffZtGmTxowZo0cffVQjRoyQ9FO2v/x/QS6XS6GhofL19VVYWNgZsyf3n2zbtk1fffWV0tLSTps7U3ZOp9OdHdme26nH2j%2B/mxIVFSXDMHTy5EnyvQDbtm3Tjz/%2BqIceekjBwcGKjIxUenq63n//ffn6%2BpKtic6W16l/a88037RpU4WHh6usrEzV1dUec5LUtGlT60/%2BPFGwvExcXJwKCgrcPz4s/fSYq3Xr1goMDKzDM/MeX375pSZPnqwXX3xRd9xxh3s8Li5O3377raqqqtxjdrtdCQkJ7vlf/vj1z%2BcvdWvWrNGRI0fUu3dvJSYmavDgwZKkxMREtW3b9rTscnNzPbJ1OBzuuerqau3cuZNsf6Z58%2BYKCgrSrl273GP5%2Bfm67LLLlJycTL4XoLq6WjU1NR53R06cOCFJSkpKIlsTne3f0YCAALVp08Yjz5KSEh04cEAdO3ZUhw4dZBiGdu/e7bGvzWZTy5YtL9o11FqdvFqPCzJkyBDj0UcfNUpLS409e/YYffr0MZYvX17Xp%2BUVTp48adxyyy3GG2%2B8cdrc8ePHjd69exsvvfSScezYMePrr782rrvuOmPz5s2GYRjGt99%2Ba8THxxubN282KisrjRUrVhjXXnutUVhYeJGvon5yuVxGQUGB%2B%2Burr74y2rZtaxQUFBj5%2BfnGtddea/zv//6vUVlZaWzZssXo2LGj%2B6eBtm7danTu3Nn46quvjGPHjhnz5883kpOTjYqKijq%2Bqvpl5syZRt%2B%2BfY39%2B/cbhw8fNoYNG2ZMmTLFOHz4MPlegOLiYqNr167G888/bxw7dswoLi42xo0bZ/zhD38g2wtwpp8iPNe/o6%2B//rrRq1cv98c0TJs2zbjrrrvc%2B2dkZBijR482jhw5YhQUFBh33XWX8eyzz17U66otCpYXKigoMEaPHm107NjRSEpKMl566SX3Z4Tg7P79738bbdu2NeLi4k77%2BvHHH41vv/3WGD58uBEXF2f06tXLeO211zz2X79%2BvdGvXz8jNjbWuP32243PP/%2B8jq6k/vvhhx/cH9NgGIbx%2BeefG4MGDTJiY2ONfv36nfZxA6%2B99pqRnJxsxMXFGampqca33357sU%2B53jt%2B/LgxY8YMo0uXLsY111xjTJ482SgrKzMMg3wvlN1uN%2B655x7juuuuM5KSkoyMjAzj4MGDhmGQ7fk69W9q%2B/btjfbt27v/fsrZ/h2tqakxXnzxReP66683OnbsaNx///0enzVYUlJiZGZmGtdcc83/b9eOTQCGgSAI4qJUoMpW9G7BwWIQzHRw2QY3a63Ze88559d9Xz0zFz3GAAAu4IMFABATWAAAMYEFABATWAAAMYEFABATWAAAMYEFABATWAAAMYEFABATWAAAMYEFABATWAAAMYEFABB7AYLGmsVD8tRxAAAAAElFTkSuQmCC\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common3342543727843380110\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">999</td>\n",
              "        <td class=\"number\">39673</td>\n",
              "        <td class=\"number\">96.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">439</td>\n",
              "        <td class=\"number\">1.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">6</td>\n",
              "        <td class=\"number\">412</td>\n",
              "        <td class=\"number\">1.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">118</td>\n",
              "        <td class=\"number\">0.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">9</td>\n",
              "        <td class=\"number\">64</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">61</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">7</td>\n",
              "        <td class=\"number\">60</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">12</td>\n",
              "        <td class=\"number\">58</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">10</td>\n",
              "        <td class=\"number\">52</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">5</td>\n",
              "        <td class=\"number\">46</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (17)</td>\n",
              "        <td class=\"number\">205</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme3342543727843380110\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">0</td>\n",
              "        <td class=\"number\">15</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1</td>\n",
              "        <td class=\"number\">26</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">61</td>\n",
              "        <td class=\"number\">0.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:14%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">439</td>\n",
              "        <td class=\"number\">1.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">118</td>\n",
              "        <td class=\"number\">0.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:27%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">22</td>\n",
              "        <td class=\"number\">3</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">25</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">26</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">27</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">999</td>\n",
              "        <td class=\"number\">39673</td>\n",
              "        <td class=\"number\">96.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_previous\">previous<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>8</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>0.17296</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>7</td>\n",
              "                </tr>\n",
              "                <tr class=\"alert\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>86.3%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram-3699115766663516678\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABFklEQVR4nO3YwQnCQABFQRVLShH25Dk9WYQ9rXeRBxFjNMzcF/7lsdkcxxjjALx02noA/LLz1gOeTdfb4jP3%2BbLCEnCDQBIIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAiE89YDPmG63hafuc%2BXFZawN24QCAKBIBAIAoGwi0f6O77xsPfz4P8dxxhj6xHwq3xiQRAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQHgAaOYUk74WUgkAAAAASUVORK5CYII%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives-3699115766663516678,#minihistogram-3699115766663516678\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives-3699115766663516678\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles-3699115766663516678\"\n",
              "                                                  aria-controls=\"quantiles-3699115766663516678\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram-3699115766663516678\" aria-controls=\"histogram-3699115766663516678\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common-3699115766663516678\" aria-controls=\"common-3699115766663516678\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme-3699115766663516678\" aria-controls=\"extreme-3699115766663516678\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles-3699115766663516678\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>1</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>7</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>7</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>0</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>0.4949</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>2.8613</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>20.109</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>0.17296</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>0.29868</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>3.832</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>7124</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>0.24493</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram-3699115766663516678\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nO3de3RU1cH%2B8SfJkCBJJskEEmygQgFTcgEVJTRQwkWkiqIit/RXJUUql0hIhIqCEVQQLUGkwAJiK5XK207BCxIFLEWsvtJatdIhgkqAqimYCDOExHDJ5feHL9OOUI2yk5nJ%2BX7Wymrde84%2B%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%2BbKuVv9vYUm25Lf399bAACgRQTsFax9%2B/ZpwoQJ6tOnjzIzM5Wfn6/Kykr99a9/VXJystLT032%2BtmzZ4j123bp1Gj58uK644gplZ2drz5493rlTp07p/vvv18CBA5WRkaG8vDy53W7vfHl5ue644w5lZGRo8ODBWrx4sRoaGlo0OwAACG4BWbBOnz6tiRMnqm/fvtq1a5dKSkp09OhRzZ8/X5KUlJQkl8vl83XttddKknbs2KHly5frF7/4hd544w0NHjxYU6ZM0eeffy5JWrp0qUpLS%2BV0OrVt2zY1Njbq3nvv9Z57%2BvTpSkxM1Pbt27V27Vpt375dTz31VIu/BwAAIHgFZMGqra1VQUGBJk%2BerPDwcDkcDg0bNkwffvjh1x7rdDo1atQo9e7dW23bttWkSZMkSa%2B88orq6uq0ceNGTZs2TRdffLFiY2OVn5%2BvnTt36tNPP5XL5dK%2Bffs0a9YsRUdHq0uXLsrJyZHT6WzuyAAAoBUJyGewYmJiNGbMGO8/HzhwQM8995z3KlVNTY1yc3P11ltvKTw8XBMnTlROTo5CQkJUWlqq6667zntsaGioevbsKZfLpZ49e%2BrEiRNKTU31znfr1k1t27ZVaWmpKioqlJSUpJiYGO98amqqDh48qOrqakVFRTVp/xUVFaqsrPQZs9naKSEh4Vu9H/9NWFhA9uP/ymYzs9%2BzuYMt/4Wyam7JutmtmluybnZyt57cAVmwziovL9fw4cNVV1ensWPHKi8vT/v27dOll16qCRMmaOnSpXrzzTc1Y8YMRUdHa/To0fJ4PD4FSfqisLndbnk8HkmS3W73mbfb7d75L8%2BdXcvtdje5YDmdTq1YscJnLDc3V3l5ed8of2sTFxdpdD27/SKj6wULq%2BaWrJvdqrkl62Ynd/AL6IJ19lmrf/7zn7r//vt19913a8mSJfrtb3/rfc2AAQM0fvx4Pfvssxo9erQkqbGx8SvX/ar5rzu2KcaNG6chQ4b4jNls7eR211zw2v8p2Jq%2BqfxhYaGy2y9SVVWt6uut8wMIVs0tWTe7VXNL1s1ObvO5TX9z31QBXbAkKSQkRF26dFFBQYHGjx%2BvuXPnyuFw%2BLwmKSlJ27ZtkyTFxcV5r1Sd5fF41KNHD%2B9xHo9HkZH/fsOPHz%2Bu%2BPh41dfXn/fYkJCQc875VRISEs65HVhZeUJ1ddb5w3I%2BpvPX1zdY8j21am7JutmtmluybnZyB7%2BAvASya9cuDR8%2B3OfjEUJDv9jqq6%2B%2Bqv/5n//xef2BAwfUuXNnSVJaWppKS0u9c/X19XrvvffUu3dvde7cWTExMT7zH3zwgU6fPq20tDSlpaXp8OHDOnbsmHfe5XKpe/fuPoUMAADgqwRkwUpLS1N1dbUWL16s2tpaHTt2TMuXL9eVV16p6OhoPfroo3r99dd15swZ/e///q%2BeeeYZZWdnS5Kys7P1/PPP691331Vtba1WrVql8PBwDRo0SGFhYRo7dqxWr16tw4cPy%2B1267HHHtOwYcPUvn17paSkKD09XUuWLFF1dbXKysq0du1a79oAAABNEZC3CKOjo/Xkk09qwYIF6tevn9q1a6d%2B/fpp4cKFSkxM1Jw5c/TQQw/p8OHDat%2B%2BvebMmaNrrrlGkjRw4EDdddddys/P19GjR5Wenq7i4mK1bdtWkpSXl6eamhrdeOONqqur0%2BDBg72fryVJv/zlL1VYWKj%2B/fsrKipK48eP149//GN/vA0AACBIhTSaeKobX6uy8oTxNW22UA0res34us3F1K/KsdlCFRcXKbe7ptXcq28Kq%2BaWrJvdqrkl62Ynt/ncHTpEG12vqQLyFiEAAEAwo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwwK2YO3bt08TJkxQnz59lJmZqfz8fFVWVkqSdu3apdGjR%2BuKK67QiBEj9MILL/gcu27dOg0fPlxXXHGFsrOztWfPHu/cqVOndP/992vgwIHKyMhQXl6e3G63d768vFx33HGHMjIyNHjwYC1evFgNDQ0tExoAALQKAVmwTp8%2BrYkTJ6pv377atWuXSkpKdPToUc2fP18VFRWaNm2axo8fr127dmnu3LkqLCyUy%2BWSJO3YsUPLly/XL37xC73xxhsaPHiwpkyZos8//1yStHTpUpWWlsrpdGrbtm1qbGzUvffe6z339OnTlZiYqO3bt2vt2rXavn27nnrqKb%2B8DwAAIDgFZMGqra1VQUGBJk%2BerPDwcDkcDg0bNkwffvihNm/erC5dumj06NGKiIhQZmamhgwZog0bNkiSnE6nRo0apd69e6tt27aaNGmSJOmVV15RXV2dNm7cqGnTpuniiy9WbGys8vPztXPnTn366adyuVzat2%2BfZs2apejoaHXp0kU5OTlyOp3%2BfDsAAECQCciCFRMTozFjxshms0mSDhw4oOeee07XXnutSktLlZKS4vP6lJQU723AL8%2BHhoaqZ8%2Becrlc%2Buijj3TixAmlpqZ657t166a2bduqtLRUpaWlSkpKUkxMjHc%2BNTVVBw8eVHV1dXNGBgAArYjN3xv4KuXl5Ro%2BfLjq6uo0duxY5eXl6Wc/%2B5kSExN9XhcbG%2Bt9jsrj8fgUJOmLwuZ2u%2BXxeCRJdrvdZ95ut3vnvzx3di23262oqKgm7buiosL7vNhZNls7JSQkNOn4pgoLC8h%2B/F/ZbGb2ezZ3sOW/UFbNLVk3u1VzS9bNTu7WkzugC1ZSUpJcLpf%2B%2Bc9/6v7779fdd9/dpOMaGxu/9fzXHdsUTqdTK1as8BnLzc1VXl7eBa8dzOLiIo2uZ7dfZHS9YGHV3JJ1s1s1t2Td7OQOfgFdsCQpJCREXbp0UUFBgcaPH6%2BsrCzvlaiz3G63HA6HJCkuLu6ceY/Hox49enhf4/F4FBn57/%2BzP378uOLj41VfX3/eY0NCQrzHNsW4ceM0ZMgQnzGbrZ3c7pomr9EUwdb0TeUPCwuV3X6RqqpqVV9vnZ/wtGpuybrZrZpbsm52cpvPbfqb%2B6YKyIK1a9cuzZ8/X1u2bFFo6Bcl4ux/9urVS9u2bfN5/Z49e9S7d29JUlpamkpLS3XzzTdLkurr6/Xee%2B9p9OjR6ty5s2JiYrzPWknSBx98oNOnTystLU0VFRU6fPiwjh075i1ULpdL3bt39ylkXychIeGc24GVlSdUV2edPyznYzp/fX2DJd9Tq%2BaWrJvdqrkl62Ynd/ALyEsgaWlpqq6u1uLFi1VbW6tjx45p%2BfLluvLKK5Wdna3y8nJt2LBBp06d0quvvqpXX31VY8eOlSRlZ2fr%2Beef17vvvqva2lqtWrVK4eHhGjRokMLCwjR27FitXr1ahw8fltvt1mOPPaZhw4apffv2SklJUXp6upYsWaLq6mqVlZVp7dq1ys7O9vM7AgAAgklAFqzo6Gg9%2BeST2rNnj/r166cRI0YoOjpajz32mOLj47VmzRo9/fTT6tOnjx5%2B%2BGEtXrxY3//%2B9yVJAwcO1F133aX8/Hz17dtXb7zxhoqLi9W2bVtJUl5ennr37q0bb7xRQ4cOVWRkpBYuXOg99y9/%2BUtVVFSof//%2Buu2223TTTTfpxz/%2BsV/eBwAAEJxCGk081Y2vVVl5wviaNluohhW9Znzd5rIlv7%2BRdWy2UMXFRcrtrmk1l5Kbwqq5Jetmt2puybrZyW0%2Bd4cO0UbXa6qAvIIFAAAQzChYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEBW7DKy8uVm5urjIwMZWZm6p577lFVVZU%2B%2BeQTJScnKz093efr17/%2BtffYl156STfccIMuv/xyjRo1Sq%2B//rp3rqGhQUuXLtXQoUN11VVX6fbbb9fHH3/snfd4PMrPz1dmZqYGDBiguXPn6uTJky2aHQAABLeALVhTpkyR3W7Xjh079Oyzz%2BrDDz/Uo48%2B6p13uVw%2BX7fffrskae/evZo9e7ZmzZqlv/zlL8rJydGdd96pI0eOSJLWr1%2BvzZs3q7i4WK%2B88oq6dOmi3NxcNTY2SpIKCwtVW1urkpISPfPMMyorK1NRUVHLvwEAACBoBWTBqqqqUlpammbOnKnIyEh17NhRN998s956662vPXbDhg3KyspSVlaWIiIiNHLkSF166aV64YUXJElOp1M5OTnq1q2boqKiVFBQoLKyMu3evVufffaZtm/froKCAjkcDiUmJmratGl65plndObMmeaODQAAWomALFh2u12LFi1S%2B/btvWOHDx9WQkKC95/vvvtuDRgwQP369dOSJUu8Bai0tFQpKSk%2B66WkpMjlcunkyZPav3%2B/z3xUVJQuueQSuVwu7d27V2FhYUpOTvbOp6am6vPPP9eBAweaKy4AAGhlbP7eQFO4XC49/fTTWrVqlcLDw3X55Zdr2LBhWrhwofbu3avp06fLZrNpxowZ8ng8iomJ8Tk%2BJiZG%2B/fv1/Hjx9XY2HjeebfbrdjYWEVFRSkkJMRnTpLcbneT91tRUaHKykqfMZutnU9BNCEsLCD78X9ls5nZ79ncwZb/Qlk1t2Td7FbNLVk3O7lbT%2B6AL1hvv/22pk6dqpkzZyozM1OS9Pvf/94736tXL02ePFlr1qzRjBkzJMn7PNV/81XzX3dsUzidTq1YscJnLDc3V3l5eRe8djCLi4s0up7dfpHR9YKFVXNL1s1u1dySdbOTO/gFdMHasWOHfv7zn6uwsFA33XTTf31dUlKSPvvsMzU2NiouLk4ej8dn3uPxyOFwKDY2VqGhoeedj4%2BPl8PhUHV1terr6xUWFuadk6T4%2BPgm73vcuHEaMmSIz5jN1k5ud02T12iKYGv6pvKHhYXKbr9IVVW1qq9vMLJmMLBqbsm62a2aW7JudnKbz236m/umCtiC9c4772j27NlatmyZBgwY4B3ftWuX3n33XU2dOtU7duDAASUlJSkkJERpaWnas2ePz1oul0sjRoxQRESEevToodLSUvXt21fSFw/Uf/TRR%2BrVq5eSkpLU2Nioffv2KTU11Xus3W5X165dm7z3hISEc24HVlaeUF2ddf6wnI/p/PX1DZZ8T62aW7JudqvmlqybndzBLyAvgdTV1em%2B%2B%2B7TrFmzfMqVJEVHR2vlypXatGmTzpw5I5fLpV//%2BtfKzs6WJI0dO1ZvvPGGdu7cqVOnTmnjxo06dOiQRo4cKUnKzs7WunXrVFZWpurqahUVFalnz55KT0%2BXw%2BHQ8OHD9fjjj%2BvYsWM6cuSIVq5cqdGjR8tmC9guCgAAAkxAtoZ3331XZWVlWrBggRYsWOAzt3XrVi1dulQrVqzQ/fffr%2BjoaN16662aMGGCJOnSSy9VUVGRFi1apPLycnXv3l1r1qxRhw4dJEnjx49XZWWlbr31VtXU1CgjI8PneakHH3xQ8%2BbN09ChQ9WmTRtdf/31KigoaLnwAAAg6IU0mniqG1%2BrsvKE8TVttlANK3rN%2BLrNZUt%2BfyPr2GyhiouLlNtd02ouJTeFVXNL1s1u1dySdbOT23zuDh2ija7XVAF5ixAAACCYUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGHGC9aQIUO0YsUKHT582PTSAAAAQcF4wbrlllv00ksv6eqrr9akSZP08ssvq66uzvRpAAAAApbxgpWbm6uXXnpJf/jDH9SjRw89/PDDysrK0uLFi3Xw4EHTpwMAAAg4zfYMVmpqqmbPnq1XXnlFc%2BbM0R/%2B8Addd911uv322/WPf/yjuU4LAADgd81WsM6cOaOXXnpJP/vZzzR79mwlJibq3nvvVc%2BePZWTk6PNmzc316kBAAD8ymZ6wbKyMm3cuFHPP/%2B8ampqNHz4cD311FPq06eP9zVXXXWV5s%2BfrxtuuMH06QEAAPzOeMEaMWKEunbtqsmTJ%2Bumm25SbGzsOa/JysrSsWPHTJ8aAAAgIBgvWOvWrVPfvn2/9nW7d%2B82fWoAAICAYPwZrOTkZE2ZMkXbt2/3jv3mN7/Rz372M3k8HtOnAwAACDjGC9aiRYt04sQJde/e3Ts2aNAgNTQ06JFHHjF9OgAAgIBj/Bbh66%2B/rs2bNysuLs471qVLFxUVFen66683fToAAICAY/wK1smTJxUREXHuiUJDVVtba/p0AAAAAcd4wbrqqqv0yCOP6Pjx496xTz/9VA888IDPRzUAAAC0VsZvEc6ZM0cTJ07UD37wA0VFRamhoUE1NTXq3Lmzfvvb35o%2BHQAAQMAxXrA6d%2B6sF198UX/%2B85/10UcfKTQ0VF27dtWAAQMUFhZm%2BnQAAAABx3jBkqTw8HBdffXVzbE0AABAwDNesD7%2B%2BGMtWbJEH374oU6ePHnO/J/%2B9CfTpwQAAAgozfIMVkVFhQYMGKB27dp963XKy8v18MMP66233lJYWJgGDhyoOXPmyG63a%2B/evVq4cKH27t2r%2BPh4jR8/XhMnTvQe%2B9JLL2nVqlX65JNP1LVrV911110aMGCAJKmhoUHLli1TSUmJqqqq1KtXL82fP1%2BdO3eWJHk8Hs2fP19vvvmmQkNDlZWVpcLCQrVt2/bC3hgAAGAZxgvWnj179Kc//UkOh%2BOC1pkyZYrS0tK0Y8cOnThxQrm5uXr00UdVWFioyZMna%2BzYsSouLtbBgwc1ceJEderUSddcc4327t2r2bNna8WKFerXr5%2B2bdumO%2B%2B8U1u3blXHjh21fv16bd68WU888YQSExO1dOlS5ebmatOmTQoJCVFhYaFOnz6tkpISnTlzRjNmzFBRUZHuu%2B8%2BQ%2B8QAABo7Yx/TEN8fPwFXbmSpKqqKqWlpWnmzJmKjIxUx44ddfPNN%2Butt97Szp07debMGU2dOlXt2rVTamqqxowZI6fTKUnasGGDsrKylJWVpYiICI0cOVKXXnqpXnjhBUmS0%2BlUTk6OunXrpqioKBUUFKisrEy7d%2B/WZ599pu3bt6ugoEAOh0OJiYmaNm2annnmGZ05c%2BaC3xsAAGANxgvW5MmTtWLFCjU2Nn7rNex2uxYtWqT27dt7xw4fPqyEhASVlpYqOTnZ5ycSU1JStGfPHklSaWmpUlJSfNZLSUmRy%2BXSyZMntX//fp/5qKgoXXLJJXK5XNq7d6/CwsKUnJzsnU9NTdXnn3%2BuAwcOfOs8AADAWozfIvzzn/%2Bsd955R88%2B%2B6w6deqk0FDfDvf73//%2BG6/pcrn09NNPa9WqVdqyZYvsdrvPfGxsrDwejxoaGuTxeBQTE%2BMzHxMTo/379%2Bv48eNqbGw877zb7VZsbKyioqIUEhLiMydJbre7yfutqKhQZWWlz5jN1k4JCQlNXqMpwsKM9%2BNmZbOZ2e/Z3MGW/0JZNbdk3exWzS1ZNzu5W09u4wUrKipKAwcONLbe22%2B/ralTp2rmzJnKzMzUli1bzvu6/yxFX3f17KvmL%2BTK21lOp1MrVqzwGcvNzVVeXt4Frx3M4uIija5nt19kdL1gYdXcknWzWzW3ZN3s5A5%2BxgvWokWLjK21Y8cO/fznP1dhYaFuuukmSZLD4dChQ4d8XufxeBQbG6vQ0FDFxcXJ4/GcM%2B9wOLyvOd98fHy8HA6HqqurVV9f770Fefa18fHxTd73uHHjNGTIEJ8xm62d3O6aJq/RFMHW9E3lDwsLld1%2BkaqqalVf32BkzWBg1dySdbNbNbdk3ezkNp/b9Df3TdUsHzR64MABvfjii/rXv/7lLVx///vfdfnllzd5jXfeeUezZ8/WsmXLvB%2BxIElpaWn63e9%2Bp7q6OtlsX2zf5XKpd%2B/e3vmzz2Od5XK5NGLECEVERKhHjx4qLS1V3759JX3xQP1HH32kXr16KSkpSY2Njdq3b59SU1O9x9rtdnXt2rXJe09ISDjndmBl5QnV1VnnD8v5mM5fX99gyffUqrkl62a3am7JutnJHfyMXwLZtWuXRo4cqZdfflklJSWSvvjw0dtuu63JHzJaV1en%2B%2B67T7NmzfIpV5KUlZWlqKgorVq1SrW1tdq9e7c2btyo7OxsSdLYsWP1xhtvaOfOnTp16pQ2btyoQ4cOaeTIkZKk7OxsrVu3TmVlZaqurlZRUZF69uyp9PR0ORwODR8%2BXI8//riOHTumI0eOaOXKlRo9erS3zAEAAHwd4wVr6dKl%2BvnPf67Nmzd7n4vq3LmzHnnkEa1cubJJa7z77rsqKyvTggULlJ6e7vNVWVmp1atX64033lDfvn2Vn5%2BvgoICDRo0SJJ06aWXqqioSIsWLVKfPn309NNPa82aNerQoYMkafz48br55pt16623qn///jpy5IjP81IPPvigoqOjNXToUI0cOVK9evVSQUGB2TcJAAC0aiGNJp7q/g%2BXXXaZ3nzzTYWHh6t3797avXu3JKm%2Bvl5XXHGF95%2BtprLyhPE1bbZQDSt6zfi6zWVLfn8j69hsoYqLi5TbXdNqLiU3hVVzS9bNbtXcknWzk9t87g4doo2u11TGr2BFR0ef93cQVlRUKDw83PTpAAAAAo7xgnXFFVfo4YcfVnV1tXfs4MGDmj17tn7wgx%2BYPh0AAEDAMf7k9r333qsJEyYoIyPDe1uwtrZWPXr00COPPGL6dAAAAAHHeMHq2LGjSkpK9Oqrr%2BrgwYNq27atunbtqv79%2B/t8GCgAAEBr1SyfPdCmTRtdffXVzbE0AABAwDNesIYMGfKVV6qa%2BllYAAAAwcp4wbruuut8ClZ9fb0OHjwol8ulCRMmmD4dAABAwDFesGbNmnXe8W3btumvf/2r6dMBAAAEnBb7bcFXX321XnzxxZY6HQAAgN%2B0WMF67733ZPhD4wEAAAKS8VuE48ePP2estrZWZWVluuaaa0yfDgAAIOAYL1hdunQ556cIIyIiNHr0aI0ZM8b06QAAAAKO8YLFp7UDAACrM16wnn/%2B%2BSa/9qabbjJ9egAAAL8zXrDmzp2rhoaGcx5oDwkJ8RkLCQmhYAEAgFbJeMH61a9%2BpSeffFJTpkxRcnKyGhsb9f777%2BuJJ57QT37yE2VkZJg%2BJQAAQEBplmewiouLlZiY6B278sor1blzZ91%2B%2B%2B0qKSkxfUoAAICAYvxzsA4dOqSYmJhzxu12u8rLy02fDgAAIOAYL1hJSUl65JFH5Ha7vWNVVVVasmSJvvvd75o%2BHQAAQMAxfotwzpw5mjlzppxOpyIjIxUaGqrq6mq1bdtWK1euNH06AACAgGO8YA0YMEA7d%2B7Uq6%2B%2BqiNHjqixsVGJiYn64Q9/qOjoaNOnAwAACDjGC5YkXXTRRRo6dKiOHDmizp07N8cpAAAAApbxZ7BOnjyp2bNn6/LLL9e1114r6YtnsCZNmqSqqirTpwMAAAg4xgvW4sWLtXfvXhUVFSk09N/L19fXq6ioyPTpAAAAAo7xgrVt2zb98pe/1I9%2B9CPvL3222%2B1atGiRXn75ZdOnAwAACDjGC1ZNTY26dOlyzrjD4dDnn39u%2BnQAAAABx3jB%2Bu53v6u//vWvkuTzuwe3bt2q73znO6ZPBwAAEHCM/xThj3/8Y02fPl233HKLGhoatHbtWu3Zs0fbtm3T3LlzTZ8OAAAg4BgvWOPGjZPNZtPTTz%2BtsLAwrV69Wl27dlVRUZF%2B9KMfmT4dAABAwDFesI4dO6ZbbrlFt9xyi%2BmlAQAAgoLxZ7CGDh3q8%2BwVAACA1RgvWBkZGdqyZYvpZQEAAIKG8VuEF198sRYuXKji4mJ997vfVZs2bXzmlyxZ0uS1XnvtNc2ePVsZGRlaunSpd/zZZ5/VnDlzzll7/fr16tWrlxoaGrRs2TKVlJSoqqpKvXr10vz5872/tsfj8Wj%2B/LsojXwAABkgSURBVPl68803FRoaqqysLBUWFqpt27aSpL1792rhwoXau3ev4uPjNX78eE2cOPHbviUAAMBijBes/fv363vf%2B54kye12f%2Bt1nnjiCW3cuFGXXHLJeeevuuoq/fa3vz3v3Pr167V582Y98cQTSkxM1NKlS5Wbm6tNmzYpJCREhYWFOn36tEpKSnTmzBnNmDFDRUVFuu%2B%2B%2B3Ty5ElNnjxZY8eOVXFxsQ4ePKiJEyeqU6dOuuaaa751HgAAYB3GClZBQYGWLl3qU3pWrlyp3Nzcb7VeRESENm7cqIULF%2BrUqVPf6Fin06mcnBx169bNu7eMjAzt3r1bnTp10vbt2/Xcc8/J4XBIkqZNm6YZM2Zo9uzZ2rlzp86cOaOpU6cqLCxMqampGjNmjJxOJwULAAA0ibGCtWPHjnPGiouLv3XBuu22275y/vDhw/rpT3%2BqPXv2yG63Ky8vTzfeeKNOnjyp/fv3KyUlxfvaqKgoXXLJJXK5XDpx4oTCwsKUnJzsnU9NTdXnn3%2BuAwcOqLS0VMnJyQoLC/POp6SkaMOGDU3ee0VFhSorK33GbLZ2SkhIaPIaTREWZvwRumZls5nZ79ncwZb/Qlk1t2Td7FbNLVk3O7lbT25jBet8PznYXD9N6HA41KVLF911113q3r27/vjHP%2Bruu%2B9WQkKCvve976mxsVExMTE%2Bx8TExMjtdis2NlZRUVHe35N4dk764pamx%2BOR3W73OTY2NlYej0cNDQ0%2Bv8D6v3E6nVqxYoXPWG5urvLy8r5t5FYhLi7S6Hp2%2B0VG1wsWVs0tWTe7VXNL1s1O7uBnrGD9Z2H5qjETBg0apEGDBnn/ecSIEfrjH/%2BoZ599VrNmzZL01eXu2xS/b5Jl3LhxGjJkiM%2BYzdZObnfNNz7vVwm2pm8qf1hYqOz2i1RVVav6%2BgYjawYDq%2BaWrJvdqrkl62Ynt/ncpr%2B5byrjD7n7S1JSkvbs2aPY2FiFhobK4/H4zHs8HsXHx8vhcKi6ulr19fXe24BnX3t2/tChQ%2Bcce3bdpkhISDjndmBl5QnV1VnnD8v5mM5fX99gyffUqrkl62a3am7JutnJHfyC6xLI//nd736nl156yWesrKxMnTt3VkREhHr06KHS0lLvXFVVlT766CP16tVLPXv2VGNjo/bt2%2Bedd7lcstvt6tq1q9LS0vT%2B%2B%2B%2Brrq7OZ753797NHwwAALQKxq5gnTlzRjNnzvzasW/yOVj/zenTp/XQQw%2Bpc%2BfO%2Bv73v69t27bpz3/%2Bs/7whz9IkrKzs1VcXKyBAwcqMTFRRUVF6tmzp9LT0yVJw4cP1%2BOPP65HH31Up0%2Bf1sqVKzV69GjZbDZlZWUpKipKq1at0qRJk/TBBx9o48aNWrx48QXvGwAAWIOxgtWnTx9VVFR87VhTnS1DZ68kbd%2B%2BXdIXV5Nuu%2B021dTUaMaMGaqsrFSnTp20cuVKpaWlSZLGjx%2BvyspK3XrrraqpqVFGRobPQ%2BcPPvig5s2bp6FDh6pNmza6/vrrVVBQIEkKDw/X6tWrNW/ePBUXF6t9%2B/YqKCjweeYLAADgq4Q08osDW0Rl5Qnja9psoRpW9JrxdZvLlvz%2BRtax2UIVFxcpt7um1dyrbwqr5pasm92quSXrZie3%2BdwdOkQbXa%2BpgvIZLAAAgEBGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMC%2BiC9dprrykzM1MFBQXnzL300ku64YYbdPnll2vUqFF6/fXXvXMNDQ1aunSphg4dqquuukq33367Pv74Y%2B%2B8x%2BNRfn6%2BMjMzNWDAAM2dO1cnT570zu/du1c/%2BclP1KdPH11zzTV68sknmzcoAABoVQK2YD3xxBNasGCBLrnkknPm9u7dq9mzZ2vWrFn6y1/%2BopycHN155506cuSIJGn9%2BvXavHmziouL9corr6hLly7Kzc1VY2OjJKmwsFC1tbUqKSnRM888o7KyMhUVFUmSTp48qcmTJ6tfv3567bXXtHTpUq1Zs0Yvv/xyy4UHAABBLWALVkREhDZu3HjegrVhwwZlZWUpKytLERERGjlypC699FK98MILkiSn06mcnBx169ZNUVFRKigoUFlZmXbv3q3PPvtM27dvV0FBgRwOhxITEzVt2jQ988wzOnPmjHbu3KkzZ85o6tSpateunVJTUzVmzBg5nc6WfgsAAECQsvl7A//Nbbfd9l/nSktLlZWV5TOWkpIil8ulkydPav/%2B/UpJSfHORUVF6ZJLLpHL5dKJEycUFham5ORk73xqaqo%2B//xzHThwQKWlpUpOTlZYWJjP2hs2bGjy3isqKlRZWekzZrO1U0JCQpPXaIqwsIDtx%2Bdls5nZ79ncwZb/Qlk1t2Td7FbNLVk3O7lbT%2B6ALVhfxePxKCYmxmcsJiZG%2B/fv1/Hjx9XY2HjeebfbrdjYWEVFRSkkJMRnTpLcbrc8Ho/sdrvPsbGxsfJ4PGpoaFBo6Nf/y3c6nVqxYoXPWG5urvLy8r5RztYmLi7S6Hp2%2B0VG1wsWVs0tWTe7VXNL1s1O7uAXlAVLkvd5qm8z/3XHns9/FrKvM27cOA0ZMsRnzGZrJ7e75huf96sEW9M3lT8sLFR2%2B0WqqqpVfX2DkTWDgVVzS9bNbtXcknWzk9t8btPf3DdVUBasuLg4eTwenzGPxyOHw6HY2FiFhoaedz4%2BPl4Oh0PV1dWqr6/33gY8%2B9qz84cOHTrn2LPrNkVCQsI5twMrK0%2Bors46f1jOx3T%2B%2BvoGS76nVs0tWTe7VXNL1s1O7uAXXJdA/k9aWpr27NnjM%2BZyudS7d29FRESoR48eKi0t9c5VVVXpo48%2BUq9evdSzZ081NjZq3759Psfa7XZ17dpVaWlpev/991VXV3fO2gAAAE0RlAVr7NixeuONN7Rz506dOnVKGzdu1KFDhzRy5EhJUnZ2ttatW6eysjJVV1erqKhIPXv2VHp6uhwOh4YPH67HH39cx44d05EjR7Ry5UqNHj1aNptNWVlZioqK0qpVq1RbW6vdu3dr48aNys7O9nNqAAAQLAL2FmF6erokea8kbd%2B%2BXdIXV5MuvfRSFRUVadGiRSovL1f37t21Zs0adejQQZI0fvx4VVZW6tZbb1VNTY0yMjJ8Hjp/8MEHNW/ePA0dOlRt2rTR9ddf7/0w0/DwcK1evVrz5s1TcXGx2rdvr4KCAg0aNKgF0wMAgGAW0vhtnvjGN1ZZecL4mjZbqIYVvWZ83eayJb%2B/kXVstlDFxUXK7a5pNffqm8KquSXrZrdqbsm62cltPneHDtFG12uqoLxFCAAAEMgoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhQVuwkpOTlZaWpvT0dO/XQw89JEnatWuXRo8erSuuuEIjRozQCy%2B84HPsunXrNHz4cF1xxRXKzs7Wnj17vHOnTp3S/fffr4EDByojI0N5eXlyu90tmg0AAAQ3m783cCG2bt2qTp06%2BYxVVFRo2rRpmjt3rm644Qa9/fbbmjp1qrp27ar09HTt2LFDy5cv169%2B9SslJydr3bp1mjJlil5%2B%2BWW1a9dOS5cuVWlpqZxOpy666CIVFhbq3nvv1erVq/2UEgAABJugvYL132zevFldunTR6NGjFRERoczMTA0ZMkQbNmyQJDmdTo0aNUq9e/dW27ZtNWnSJEnSK6%2B8orq6Om3cuFHTpk3TxRdfrNjYWOXn52vnzp369NNP/RkLAAAEkaC%2BgrVkyRL9/e9/V3V1ta699lrdc889Ki0tVUpKis/rUlJStGXLFklSaWmprrvuOu9caGioevbsKZfLpZ49e%2BrEiRNKTU31znfr1k1t27ZVaWmpEhMTm7SviooKVVZW%2BozZbO2UkJDwbaOeV1hYcPVjm83Mfs/mDrb8F8qquSXrZrdqbsm62cndenIHbcG67LLLlJmZqUcffVQff/yx8vPz9cADD8jj8ZxThGJjY73PUXk8HsXExPjMx8TEyO12y%2BPxSJLsdrvPvN1u/0bPYTmdTq1YscJnLDc3V3l5eU1eozWKi4s0up7dfpHR9YKFVXNL1s1u1dySdbOTO/gFbcFyOp3e/96tWzfNmjVLU6dOVZ8%2Bfb722MbGxgua/zrjxo3TkCFDfMZstnZyu2suaN0vC7ambyp/WFio7PaLVFVVq/r6BiNrBgOr5pasm92quSXrZie3%2Bdymv7lvqqAtWF/WqVMn1dfXKzQ01Hsl6iy32y2HwyFJiouLO2fe4/GoR48e3td4PB5FRv77X8jx48cVHx/f5L0kJCScczuwsvKE6uqs84flfEznr69vsOR7atXcknWzWzW3ZN3s5A5%2BwXUJ5P%2B89957euSRR3zGysrKFB4erqysLJ%2BPXZCkPXv2qHfv3pKktLQ0lZaWeufq6%2Bv13nvvqXfv3urcubNiYmJ85j/44AOdPn1aaWlpzZgIAAC0JkF5BSs%2BPl5Op1MOh0M5OTkqLy/XsmXLNG7cON14441asWKFNmzYoJEjR%2Bovf/mLXn31Ve8txezsbN111126/vrrlZycrF//%2BtcKDw/XoEGDFBYWprFjx2r16tVKT09X27Zt9dhjj2nYsGFq3769n1MHv2sf/19/b%2BEb2ZLf399bAAAEqaAsWImJiSouLtaSJUu0atUqhYeH6%2Babb1ZBQYEiIiK0Zs0aLViwQA888ICSkpK0ePFiff/735ckDRw4UHfddZfy8/N19OhRpaenq7i4WG3btpUk5eXlqaamRjfeeKPq6uo0ePBgzZ8/349pAQBAsAlpvNAnutEklZUnjK9ps4VqWNFrxtfFFwLtCpbNFqq4uEi53TWt5hmFprJqdqvmlqybndzmc3foEG10vaYKymewAAAAAhkFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyjYAEAABhGwQIAADCMggUAAGAYBQsAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsAAAAAyz%2BXsDQKC69vH/9fcWvpEt%2Bf39vQUAwP/hChYAAIBhFKzzKC8v1x133KGMjAwNHjxYixcvVkNDg7%2B3BQAAggS3CM9j%2BvTpSk1N1fbt23X06FFNnjxZ7du3109/%2BlN/bw0AAAQBCtaXuFwu7du3T2vXrlV0dLSio6OVk5Ojp556ioIFGMLzbQBaOwrWl5SWliopKUkxMTHesdTUVB08eFDV1dWKior62jUqKipUWVnpM2aztVNCQoLRvYaFcYcX/2az8b%2BH5hJshfCPs37o7y1csLN/v1nt7zlyt57cFKwv8Xg8stvtPmNny5bb7W5SwXI6nVqxYoXP2J133qnp06eb26i%2BKHITOn6ocePGGS9vgayiokJOp5PcQeythT/6Rq9vTdm/Cavmlr7I/tRTv7JcdnK3ntytpyoa1NjYeEHHjxs3Ts8%2B%2B6zP17hx4wzt7t8qKyu1YsWKc66WtXbktlZuybrZrZpbsm52cree3FzB%2BhKHwyGPx%2BMz5vF4FBISIofD0aQ1EhISWk0DBwAA3xxXsL4kLS1Nhw8f1rFjx7xjLpdL3bt3V2RkpB93BgAAggUF60tSUlKUnp6uJUuWqLq6WmVlZVq7dq2ys7P9vTUAABAkwubPnz/f35sIND/84Q9VUlKihx56SC%2B%2B%2BKJGjx6t22%2B/XSEhIf7e2jkiIyPVt29fy11dI7e1ckvWzW7V3JJ1s5O7deQOabzQJ7oBAADgg1uEAAAAhlGwAAAADKNgAQAAGEbBAgAAMIyCBQAAYBgFCwAAwDAKFgAAgGEULAAAAMMoWAAAAIZRsIJQeXm57rjjDmVkZGjw4MFavHixGhoa/L2tFvHaa68pMzNTBQUF/t5KiyovL1dubq4yMjKUmZmpe%2B65R1VVVf7eVovYt2%2BfJkyYoD59%2BigzM1P5%2BfmqrKz097Za1MMPP6zk5GR/b6NFJCcnKy0tTenp6d6vhx56yN/bajGrVq3SgAEDdNlllyknJ0effPKJv7fUrP72t7/5/LtOT09XWlpaq/jfOwUrCE2fPl2JiYnavn271q5dq%2B3bt%2Bupp57y97aa3RNPPKEFCxbokksu8fdWWtyUKVNkt9u1Y8cOPfvss/rwww/16KOP%2Bntbze706dOaOHGi%2Bvbtq127dqmkpERHjx6VlX6F6t69e7Vp0yZ/b6NFbd26VS6Xy/tVWFjo7y21iPXr1%2BuFF17QunXr9Prrr6t79%2B76zW9%2B4%2B9tNaurrrrK59%2B1y%2BXSnXfeqWuvvdbfW7tgFKwg43K5tG/fPs2aNUvR0dHq0qWLcnJy5HQ6/b21ZhcREaGNGzdarmBVVVUpLS1NM2fOVGRkpDp27Kibb75Zb731lr%2B31uxqa2tVUFCgyZMnKzw8XA6HQ8OGDdOHH37o7621iIaGBs2bN085OTn%2B3gpawJNPPqmCggJ973vfU1RUlO677z7dd999/t5Wi/rXv/6ltWvX6u677/b3Vi4YBSvIlJaWKikpSTExMd6x1NRUHTx4UNXV1X7cWfO77bbbFB0d7e9ttDi73a5Fixapffv23rHDhw8rISHBj7tqGTExMRozZoxsNpsk6cCBA3ruuedaxXe3TfH73/9eERERuuGGG/y9lRa1ZMkSDRo0SFdeeaUKCwtVU1Pj7y01u08//VSffPKJjh8/ruuuu04ZGRnKy8vTsWPH/L21FrVs2TLdcsst%2Bs53vuPvrVwwClaQ8Xg8stvtPmNny5bb7fbHltDCXC6Xnn76aU2dOtXfW2kx5eXlSktL03XXXaf09HTl5eX5e0vN7rPPPtPy5cs1b948f2%2BlRV122WXKzMzUyy%2B/LKfTqXfffVcPPPCAv7fV7I4cOSLpi9uja9eu1aZNm3TkyBFLXcH65JNP9PLLL%2BunP/2pv7diBAUrCDU2Nvp7C/CTt99%2BW7fffrtmzpypzMxMf2%2BnxSQlJcnlcmnr1q06dOhQq7h98HUWLVqkUaNGqXv37v7eSotyOp0aM2aMwsPD1a1bN82aNUslJSU6ffq0v7fWrM7%2BvT5p0iQlJiaqY8eOmj59unbs2KFTp075eXctY/369brmmmvUoUMHf2/FCApWkHE4HPJ4PD5jHo9HISEhcjgcftoVWsKOHTt0xx13aM6cObrtttv8vZ0WFxISoi5duqigoEAlJSWt%2BtbJrl279Pe//125ubn%2B3orfderUSfX19Tp69Ki/t9Kszj4C8J93KJKSktTY2Njqs5%2B1bds2DRkyxN/bMIaCFWTS0tJ0%2BPBhn/9zcblc6t69uyIjI/24MzSnd955R7Nnz9ayZct00003%2BXs7LWbXrl0aPny4z8eQhIZ%2B8ddWmzZt/LWtZvfCCy/o6NGjGjx4sDIyMjRq1ChJUkZGhl588UU/7675vPfee3rkkUd8xsrKyhQeHt7qnzns2LGjoqKitHfvXu9YeXm52rRp0%2BqzS1/8tGx5ebn69%2B/v760YQ8EKMikpKUpPT9eSJUtUXV2tsrIyrV27VtnZ2f7eGppJXV2d7rvvPs2aNUsDBgzw93ZaVFpamqqrq7V48WLV1tbq2LFjWr58ua688spW/QMP99xzj7Zt26ZNmzZp06ZNKi4uliRt2rSpVX2H/2Xx8fFyOp0qLi7W6dOndfDgQS1btkzjxo1TWFiYv7fXrGw2m0aPHq3Vq1frn//8p44ePaqVK1fqhhtu8P6QR2v23nvvKTY2VlFRUf7eijEhjTzQE3SOHDmiwsJCvfnmm4qKitL48eN15513KiQkxN9ba1bp6emSvigckrx/6bhcLr/tqSW89dZb%2Bn//7/8pPDz8nLmtW7cqKSnJD7tqOe%2B//74WLFigf/zjH2rXrp369eune%2B65R4mJif7eWov55JNPNHToUL3//vv%2B3kqz%2B9vf/qYlS5bo/fffV3h4uG6%2B%2BWYVFBQoIiLC31trdqdPn9aiRYv04osv6syZMxo%2BfLgKCwstcXdizZo12rx5s0pKSvy9FWMoWAAAAIZxixAAAMAwChYAAIBhFCwAAADDKFgAAACGUbAAAAAMo2ABAAAYRsECAAAwjIIFAABgGAULAADAMAoWAACAYRQsAAAAwyhYAAAAhlGwAAAADPv/LuPswEQIsFsAAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common-3699115766663516678\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">0</td>\n",
              "        <td class=\"number\">35563</td>\n",
              "        <td class=\"number\">86.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1</td>\n",
              "        <td class=\"number\">4561</td>\n",
              "        <td class=\"number\">11.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">754</td>\n",
              "        <td class=\"number\">1.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">216</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">70</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">5</td>\n",
              "        <td class=\"number\">18</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">6</td>\n",
              "        <td class=\"number\">5</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">7</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme-3699115766663516678\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">0</td>\n",
              "        <td class=\"number\">35563</td>\n",
              "        <td class=\"number\">86.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1</td>\n",
              "        <td class=\"number\">4561</td>\n",
              "        <td class=\"number\">11.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">2</td>\n",
              "        <td class=\"number\">754</td>\n",
              "        <td class=\"number\">1.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">216</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">70</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">3</td>\n",
              "        <td class=\"number\">216</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">4</td>\n",
              "        <td class=\"number\">70</td>\n",
              "        <td class=\"number\">0.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:33%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">5</td>\n",
              "        <td class=\"number\">18</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">6</td>\n",
              "        <td class=\"number\">5</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">7</td>\n",
              "        <td class=\"number\">1</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_poutcome\">poutcome<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>3</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable8686060162630297692\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>nonexistent</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 86.3%\">\n",
              "            35563\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>failure</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:12%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 10.3%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        4252\n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>success</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:4%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 3.3%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        1373\n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable8686060162630297692, #minifreqtable8686060162630297692\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable8686060162630297692\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">nonexistent</td>\n",
              "        <td class=\"number\">35563</td>\n",
              "        <td class=\"number\">86.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">failure</td>\n",
              "        <td class=\"number\">4252</td>\n",
              "        <td class=\"number\">10.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">success</td>\n",
              "        <td class=\"number\">1373</td>\n",
              "        <td class=\"number\">3.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_emp.var.rate\">emp.var.rate<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>10</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>0.081886</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>-3.4</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>1.4</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram2807817225322593550\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABIUlEQVR4nO3cwQkCMRBAURVLsgh78mxPFmFPsQH5mAXZsLx3X5jLZ7IQch5jjBPw1WXvAWBl170H4Lhuj9f0N%2B/n/Q%2BTbGeDQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBE%2BPTph9SnO1ZzSZZ4NAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBJcVD8Alyv%2BxQSAIBIJAICz3DzJ7nt7KOZxf2CAQltsgR7JlG9psazmPMcbeQ8CqHLEgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgfAACixeXEX3SyQAAAABJRU5ErkJggg%3D%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2807817225322593550,#minihistogram2807817225322593550\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives2807817225322593550\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2807817225322593550\"\n",
              "                                                  aria-controls=\"quantiles2807817225322593550\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram2807817225322593550\" aria-controls=\"histogram2807817225322593550\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common2807817225322593550\" aria-controls=\"common2807817225322593550\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme2807817225322593550\" aria-controls=\"extreme2807817225322593550\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2807817225322593550\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>-3.4</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>-2.9</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>-1.8</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>1.1</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>1.4</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>1.4</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>1.4</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>4.8</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>3.2</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>1.571</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>19.185</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>-1.0626</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>0.081886</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>1.4228</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>-0.7241</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>3372.7</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>2.4679</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2807817225322593550\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%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%2B4H3u%2B%2B%2B/r/vuu0/9%2B/fXQw89pI8//tg719TUpBUrVmjYsGEaOHCgJk6cqKNHj3rn3W63Zs6cqaysLA0ePFjz5s1TXV2dX/cdAACEtqANWE8%2B%2BaRsNpt27typDRs26JtvvtHzzz/vnXc6nT5fEydOlCTt27dP%2Bfn5mj17tj7//HNNmDBBU6dO1fHjxyVJb775pjZt2qSioiLt2rVLycnJys3NlcfjkSTNnz9ftbW12rx5s9avX6%2BysjIVFBT4vwEAACBkWQJdwMVUVVUpPT1ds2bNUkxMjGJiYvTggw/q9ddfv%2BRz161bp%2BzsbGVnZ0uSRo4cqTfeeEMbN27UE088IYfDoQkTJqhnz56SpLy8PGVmZmrv3r3q2rWrduzYof/6r/%2BS3W6XJE2ZMkUzZsxQfn6%2BIiMjr3ifKitPX/FzQ0V4eJjs9hidPFmjpiZPoMtp1ei1/9Br/6HX/tOWet2pU2xA1g3KgGWz2bRkyRKfsfLyciUmJnr//%2Bmnn9ann36qhoYGjR49WtOnT1dkZKRKS0u94eq81NRUOZ1O1dXV6cCBA0pNTfXOWa1Wde/eXU6nU6dPn1ZERIRSUlK882lpaTpz5owOHjzoM/5jKioqVFlZ6TNmsbT3qb81iogIV1hYmCIjI9TY2BToclo1eu0/9Np/6LX/0OuWF5QB6/ucTqfeeOMNrV69WlFRUerfv7/uuOMO/fa3v9W%2Bffs0bdo0WSwWzZgxQ263W3FxcT7Pj4uL04EDB3Tq1Cl5PJ6LzrtcLsXHx8tqtSosLMxnTpJcLlez63U4HCosLPQZy83N1fTp0y9310OSzdYu0CW0GfTaf%2Bi1/9Br/6HXLSfoA9aXX36pyZMna9asWcrKypIkvf322975vn37atKkSXr55Zc1Y8YMSfJeT/VDfmz%2BUs9tjrFjx2ro0KE%2BYxZLe7lcNVe97WAWEREum62dqqpq%2BRdRC6PX/kOv/Yde%2B09b6nVCQkxA1g3qgLVz50499dRTmj9/vh544IEffFxSUpJOnDghj8ejhIQEud1un3m32y273a74%2BHiFh4dfdL5Dhw6y2%2B2qrq5WY2OjIiIivHOS1KFDh2bXnZiYeMHpwMrK02poaN0v4vMaG5vazL4GGr32H3rtP/Taf%2Bh1ywna3yL86quvlJ%2BfrxdeeMEnXH322WdavXq1z2MPHjyopKQkhYWFKT09XSUlJT7zTqdT/fr1U3R0tHr37q3S0lLvXFVVlY4cOaK%2BffuqT58%2B8ng82r9/v89zbTabevTo0UJ7CgAAWpugDFgNDQ165plnNHv2bA0ePNhnLjY2VqtWrdK7776r%2Bvp6OZ1O/eEPf1BOTo4kacyYMfr000%2B1e/dunT17VsXFxTp8%2BLBGjhwpScrJydHatWtVVlam6upqFRQUqE%2BfPsrIyJDdbtfw4cO1cuVKnTx5UsePH9eqVas0atQoWSxBfbAPAAAEkTCPiYuODPviiy/085//XFFRURfMbd26VV9//bUKCwt1%2BPBhxcbG6pFHHtHjjz%2Bu8PDv8uL27du1fPlyHTt2TL169dK8efM0cOBASd9dY/Xiiy/q7bffVk1NjTIzM7Vo0SJ16dJFknT69GktWLBAu3btUmRkpEaMGKE5c%2BZctJbL0RZu02CxhCshIUYuVw2HnFsYvfYfeu0/9Np/2lKvA3WbhqAMWK0RAQsm0Wv/odf%2BQ6/9py31OlABKyhPEQIAAIQyAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYxt0zAQBope5e%2BUmgS2i2LTNvDnQJRnEECwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADAvagHXs2DHl5uYqMzNTWVlZmjNnjqqqqiRJ%2B/bt0/jx4zVgwADdeeedWrNmjc9z33//fd13333q37%2B/HnroIX388cfeuaamJq1YsULDhg3TwIEDNXHiRB09etQ773a7NXPmTGVlZWnw4MGaN2%2Be6urq/LPTAACgVQjagPXkk0/KZrNp586d2rBhg7755hs9//zzqqur06RJk3TjjTdqz549WrFihV5%2B%2BWVt375d0nfhKz8/X7Nnz9bnn3%2BuCRMmaOrUqTp%2B/Lgk6c0339SmTZtUVFSkXbt2KTk5Wbm5ufJ4PJKk%2BfPnq7a2Vps3b9b69etVVlamgoKCgPUBAACEnqAMWFVVVUpPT9esWbMUExOjLl266MEHH9QXX3yh3bt3q76%2BXpMnT1b79u2Vlpam0aNHy%2BFwSJLWrVun7OxsZWdnKzo6WiNHjtT111%2BvjRs3SpIcDocmTJignj17ymq1Ki8vT2VlZdq7d69OnDihHTt2KC8vT3a7XZ07d9aUKVO0fv161dfXB7IlAAAghARlwLLZbFqyZIk6duzoHSsvL1diYqJKS0uVkpKiiIgI71xqaqpKSkokSaWlpUpNTfXZXmpqqpxOp%2Brq6nTgwAGfeavVqu7du8vpdGrfvn2KiIhQSkqKdz4tLU1nzpzRwYMHW2p3AQBAK2MJdAHN4XQ69cYbb2j16tXasmWLbDabz3x8fLzcbreamprkdrsVFxfnMx8XF6cDBw7o1KlT8ng8F513uVyKj4%2BX1WpVWFiYz5wkuVyuZtdbUVGhyspKnzGLpb0SExObvY1QFBER7vMdLYde%2Bw%2B99h963bZZLK3r5x70AevLL7/U5MmTNWvWLGVlZWnLli0Xfdw/hqLz11P9kB%2Bbv9Rzm8PhcKiwsNBnLDc3V9OnT7/qbYcCm61doEtoM%2Bi1/9Br/6HXbVNCQkygSzAqqAPWzp079dRTT2n%2B/Pl64IEHJEl2u12HDx/2eZzb7VZ8fLzCw8OVkJAgt9t9wbzdbvc%2B5mLzHTp0kN1uV3V1tRobG72nIM8/tkOHDs2ue%2BzYsRo6dKjPmMXSXi5XTbO3EYoiIsJls7VTVVWtGhubAl1Oq0av/Yde%2Bw%2B9btta6u/IQAW3oA1YX331lfLz8/XCCy9o8ODB3vH09HS99dZbamhokMXyXflOp1P9%2BvXzzp%2B/Hus8p9Ope%2B%2B9V9HR0erdu7dKS0s1aNAgSd9dUH/kyBH17dtXSUlJ8ng82r9/v9LS0rzPtdls6tGjR7NrT0xMvOB0YGXlaTU0tI03jMbGpjazr4FGr/2HXvsPvW6bWtvPPChPeDY0NOiZZ57R7NmzfcKVJGVnZ8tqtWr16tWqra3V3r17VVxcrJycHEnSmDFj9Omnn2r37t06e/asiouLdfjwYY0cOVKSlJOTo7Vr16qsrEzV1dUqKChQnz59lJGRIbvdruHDh2vlypU6efKkjh8/rlWrVmnUqFHeMAcAAHApYR4TFx0Z9sUXX%2BjnP/%2B5oqKiLpjbunWrampqtGDBApWUlKhjx456/PHH9bOf/cz7mO3bt2v58uU6duyYevXqpXnz5mngwIGSvrvG6sUXX9Tbb7%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%2Bsvf/mLJKmpqUkrVqzQsGHDNHDgQE2cOFFHjx71Pt/tdmvmzJnKysrS4MGDNW/ePNXV1Xnn9%2B3bp/Hjx2vAgAG68847tWbNGv/sMAAAaBWCNmC98sorWrx4sbp3737R%2BYEDB8rpdPp89e3bV5L05ptvatOmTSoqKtKuXbuUnJys3NxceTweSdL8%2BfNVW1urzZs3a/369SorK1NBQYEkqa6uTpMmTdKNN96oPXv2aMWKFXr55Ze1fft2/%2Bw4AAAIecYD1tChQ1VYWKjy8vKr2k50dLSKi4t/MGD9GIfDoQkTJqhnz56yWq3Ky8tTWVmZ9u7dqxMnTmjHjh3Ky8uT3W5X586dNWXKFK1fv1719fXavXu36uvrNXnyZLVv315paWkaPXq0HA7HVe0PAABoOyymN/jwww/rvffe0%2BrVq3XTTTdpzJgxGjp0qCyWy1vq0Ucf/dH58vJy/fKXv1RJSYlsNpumT5%2Bu%2B%2B%2B/X3V1dTpw4IBSU1O9j7VarerevbucTqdOnz6tiIgIpaSkeOfT0tJ05swZHTx4UKWlpUpJSVFERIR3PjU1VevWrWt27RUVFaqsrPQZs1jaKzExsdnbCEUREeE%2B39Fy6LX/0Gv/oddtm8XSun7uxgNWbm6ucnNzVVpaqs2bN%2Bt3v/udFi5cqAceeECjRo1Sjx49rnoNu92u5ORk/frXv1avXr30xz/%2BUU8//bQSExN13XXXyePxKC4uzuc5cXFxcrlcio%2BPl9VqVVhYmM%2BcJLlcLrndbtlsNp/nxsfHy%2B12q6mpSeHhl34BOBwOFRYW%2Bozl5uZq%2BvTpV7rLIcVmaxfoEtoMeu0/9Np/6HXblJAQE%2BgSjDIesM5LS0tTWlqann76ab3//vt69tlntWbNGmVlZWnGjBne66WuxJAhQzRkyBDv/99777364x//qA0bNmj27NmS5L3e6mJ%2BbO6H/GMgu5SxY8dq6NChPmMWS3u5XDWXvW4oiYgIl83WTlVVtWpsbAp0Oa0avfYfeu0/9Lpta6m/IwMV3FosYNXX13tDz%2Beff67k5GRNmzZNFRUVmjBhghYuXKj77rvP2HpJSUkqKSlRfHy8wsPD5Xa7febdbrc6dOggu92u6upqNTY2ek8Dnn/s%2BfnDhw9f8Nzz222OxMTEC04HVlaeVkND23jDaGxsajP7Gmj02n/otf/Q67aptf3MjQessrIyFRcX65133lFNTY2GDx%2Bu1157TQMGDPA%2BZuDAgXr22WevOGC99dZbiouL0z333OOzbrdu3RQdHa3evXurtLRUgwYNkiRVVVXpyJEj6tu3r5KSkuTxeLR//36lpaVJkpxOp2w2m3r06KH09HS99dZbamho8F435nQ61a9fvyttCQAAaGOMX1F27733avfu3Zo0aZI%2B%2BugjLVu2zCdcSVJ2drZOnjx5xWucO3dOzz33nJxOp%2Brr67V582Z99NFHGjdunCQpJydHa9euVVlZmaqrq1VQUKA%2BffooIyNDdrtdw4cP18qVK3Xy5EkdP35cq1at0qhRo2SxWJSdnS2r1arVq1ertrZWe/fuVXFxsXJycq6qLwAAoO0wfgRr7dq13iNHP2bv3r0/Op%2BRkSFJamhokCTt2LFD0ndHkx599FHV1NRoxowZqqysVNeuXbVq1Sqlp6dLksaNG6fKyko98sgjqqmpUWZmps9F54sWLdKCBQs0bNgwRUZGasSIEd6bmUZFRemll17SggULVFRUpI4dOyovL8/nmi8AAIAfE%2Ba5kiu%2Bf8SpU6eUn5%2BvUaNG6fbbb5ck/cd//Ic%2B%2BeQTLVu2TPHx8SaXCxmVlacDXUKLs1jClZAQI5erptWdSw829Np/6LX/0Gvz7l75SaBLaLYtM29uke126hTbItu9FOOnCJcsWaLTp0%2BrV69e3rEhQ4aoqalJS5cuNb0cAABA0DF%2BivDjjz/Wpk2blJCQ4B1LTk5WQUGBRowYYXo5AACAoGP8CFZdXZ2io6MvXCg8XLW1taaXAwAACDrGA9bAgQO1dOlSnTp1yjv2t7/9TQsXLrzgtwkBAABaI%2BOnCH/zm9/oscce00033SSr1aqmpibV1NSoW7duev31100vBwAAEHSMB6xu3brpvffe00cffaQjR44oPDxcPXr00ODBg30%2BQBkAAKC1apGPyomKivLeogEAAKCtMR6wjh49quXLl%2Bubb75RXV3dBfMffPCB6SUBAACCSotcg1VRUaHBgwerffv2pjcPAAAQ9IwHrJKSEn3wwQey2%2B2mNw0AABASjN%2BmoUOHDhy5AgAAbZrxgDVp0iQVFhbK8EccAgAAhAzjpwg/%2BugjffXVV9qwYYO6du2q8HDfDPf222%2BbXhIAACCoGA9YVqtVt956q%2BnNAgAAhAzjAWvJkiWmNwkAABBSjF%2BDJUkHDx7Uiy%2B%2BqLlz53rH/vznP7fEUgAAAEHHeMD67LPPNHLkSG3fvl2bN2%2BW9N3NRx999FFuMgoAANoE4wFrxYoVeuqpp7Rp0yaFhYVJ%2Bu7zCZcuXapVq1aZXg4AACDoGA9Yf/3rX5WTkyNJ3oAlSXfddZfKyspMLwcAABB0jAes2NjYi34GYUVFhaKiokwvBwAAEHSMB6wbbrhBv/vd71RdXe0dO3TokPLz83XTTTeZXg4AACDoGL9Nw9y5c/WLX/xCmZmZamxs1A033KDa2lr17t1bS5cuNb0cAABA0DEesLp06aLNmzfrww8/1KFDh3TNNdeoR48euvnmm32uyQIAAGitjAcsSYqMjNTtt9/eEpsGAAAIesYD1tChQ3/0SBX3wgIAAK2d8YB1zz33%2BASsxsZGHTp0SE6nU7/4xS9MLwcAABB0jAes2bNnX3R827Zt%2BtOf/mR6OQAAgKDTIp9FeDG333673nvvPX8tBwAAEDB%2BC1hff/21PB6Pv5YDAAAIGOOnCMeNG3fBWG1trcrKynTnnXeaXg4AACDoGA9YycnJF/wWYXR0tEaNGqXRo0ebXg4AACDoGA9Y3K0dAAC0dcYD1jvvvNPsxz7wwAOmlwcAAAg44wFr3rx5ampquuCC9rCwMJ%2BxsLAwAhYAAGiVjAes3//%2B91qzZo2efPJJpaSkyOPx6H//93/1yiuvaPz48crMzDS9JAAAQFBpkWuwioqK1LlzZ%2B/Yv/zLv6hbt26aOHGiNm/ebHpJAACAoGL8PliHDx9WXFzcBeM2m03Hjh0zvRwAAEDQMR6wkpKStHTpUrlcLu9YVVWVli9frmuvvdb0cgAAAEHH%2BCnC3/zmN5o1a5YcDodiYmIUHh6u6upqXXPNNVq1apXp5QAAAIKO8YA1ePBg7d69Wx9%2B%2BKGOHz8uj8ejzp0765ZbblFsbKzp5QAAAIKO8YAlSe3atdOwYcN0/PhxdevWrSWWAAAACFrGr8Gqq6tTfn6%2B%2Bvfvr7vvvlvSd9dg/epXv1JVVZXp5QAAAIKO8YC1bNky7du3TwUFBQoP/3%2Bbb2xsVEFBgenlAAAAgo7xgLVt2zb9%2B7//u%2B666y7vhz7bbDYtWbJE27dvN70cAABA0DEesGpqapScnHzBuN1u15kzZ0wvBwAAEHSMB6xrr71Wf/rTnyTJ57MHt27dqn/6p38yvRwAAEDQMf5bhD/72c80bdo0Pfzww2pqatKrr76qkpISbdu2TfPmzTO9HAAAQNAxHrDGjh0ri8WiN954QxEREXrppZfUo0cPFRQU6K677jK9HAAAQNAxHrBOnjyphx9%2BWA8//LDpTQMAAIQE49dgDRs2zOfaKwAAgLbGeMDKzMzUli1bTG8WAAAgZBg/RfiTn/xEv/3tb1VUVKRrr71WkZGRPvPLly83vSQAAEBQMX4E68CBA7ruuusUGxsrl8uliooKn6/LsWfPHmVlZSkvL%2B%2BCuffff1/33Xef%2Bvfvr4ceekgff/yxd66pqUkrVqzQsGHDNHDgQE2cOFFHjx71zrvdbs2cOVNZWVkaPHiw5s2bp7q6Ou/8vn37NH78eA0YMEB33nmn1qxZcwWdAAAAbZWxI1h5eXlasWKFXn/9de/YqlWrlJube0Xbe%2BWVV1RcXKzu3btfMLdv3z7l5%2BersLBQN954o7Zt26apU6dq69at6tKli958801t2rRJr7zyijp37qwVK1YoNzdX7777rsLCwjR//nydO3dOmzdvVn19vWbMmKGCggI988wzqqur06RJkzRmzBgVFRXp0KFDeuyxx9S1a1fdeeedV9wfAADQdhg7grVz584LxoqKiq54e9HR0T8YsNatW6fs7GxlZ2crOjpaI0eO1PXXX6%2BNGzdKkhwOhyZMmKCePXvKarUqLy9PZWVl2rt3r06cOKEdO3YoLy9PdrtdnTt31pQpU7R%2B/XrV19dr9%2B7dqq%2Bv1%2BTJk9W%2BfXulpaVp9OjRcjgcV7wvAACgbTEWsC72m4NX89uEjz76qGJjYy86V1paqtTUVJ%2Bx1NRUOZ1O1dXV6cCBAz7zVqtV3bt3l9Pp1L59%2BxQREaGUlBTvfFpams6cOaODBw%2BqtLRUKSkpioiI8Nl2SUnJFe8LAABoW4ydIjz/wc6XGjPB7XYrLi7OZywuLk4HDhzQqVOn5PF4LjrvcrkUHx8vq9XqU9v5x7pcLrndbtlsNp/nxsfHy%2B12q6mpSeHhl86kFRUVqqys9BmzWNorMTHxsvYz1EREhPt8R8uh1/5Dr/2HXrdtFkvr%2Brkb/y1Cf7nU0bEfm7%2BSI2uXExYdDocKCwt9xnJzczV9%2BvTLXjcU2WztAl1Cm0Gv/Yde%2Bw%2B9bpsSEmICXYJRIRmwEhIS5Ha7fcbcbrfsdrvi4%2BMVHh5%2B0fkOHTrIbrerurpajY2N3tOA5x97fv7w4cMXPPf8dptj7NixGjp0qM%2BYxdJeLlfN5exmyImICJfN1k5VVbVqbGwKdDmt2sV6fUfBngBX1Xx/nH1LoEtoNl7X/kOv27aW%2BjsyUMHNWMCqr6/XrFmzLjlm4j5Y6enpF1wT5XQ6de%2B99yo6Olq9e/dWaWmpBg0aJEmqqqrSkSNH1LdvXyUlJcnj8Wj//v1KS0vzPtdms6lHjx5KT0/XW2%2B9pYaGBlksFu98v379ml1fYmLiBacDKytPq6GhbbxhNDY2tZl9DbRQ7XUo1hyqvQ5F9Lptam0/c2MnPAcMGHDBPa8uNmbCmDFj9Omnn2r37t06e/asiouLdfjwYY0cOVKSlJOTo7Vr16qsrEzV1dUqKChQnz59lJGRIbvdruHDh2vlypU6efKkjh8/rlWrVmnUqFGyWCzKzs6W1WrV6tWrVVtbq71796q4uFg5OTlGagcAAK2fsSNY/3j/KxMyMjIkSQ0NDZKkHTt2SPruaNL111%2BvgoICLVmyRMeOHVOvXr308ssvq1OnTpKkcePGqbKyUo888ohqamqUmZnpc03UokWLtGDBAg0bNkyRkZEaMWKE92amUVFReumll7RgwQIVFRWpY8eOysvL05AhQ4zuHwAAaL3CPHwys19UVp4OdAktzmIJV0JCjFyumlZ3qDfYXKzXd6/8JMBVNd%2BWmTcHuoRm43XtP/TaPN4XpE6dLn7Lp5bWun4nEgAAIAgQsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYyAaslJQUpaenKyMjw/v13HPPSZI%2B%2B%2BwzjRo1SjfccIPuvfdebdy40ee5a9eu1QvUGgYAAAvHSURBVPDhw3XDDTcoJydHJSUl3rmzZ8/qX//1X3XrrbcqMzNT06dPl8vl8uu%2BAQCA0GYJdAFXY%2BvWreratavPWEVFhaZMmaJ58%2Bbpvvvu05dffqnJkyerR48eysjI0M6dO/Xiiy/q97//vVJSUrR27Vo9%2BeST2r59u9q3b68VK1aotLRUDodD7dq10/z58zV37ly99NJLAdpLAAAQakL2CNYP2bRpk5KTkzVq1ChFR0crKytLQ4cO1bp16yRJDodDDz30kPr166drrrlGv/rVryRJu3btUkNDg4qLizVlyhT95Cc/UXx8vGbOnKndu3frb3/7WyB3CwAAhJCQPoK1fPly/fnPf1Z1dbXuvvtuzZkzR6WlpUpNTfV5XGpqqrZs2SJJKi0t1T333OOdCw8PV58%2BfeR0OtWnTx%2BdPn1aaWlp3vmePXvqmmuuUWlpqTp37tysuioqKlRZWekzZrG0V2Ji4pXuakiIiAj3%2BY6WE%2Bq9tlhCp%2B5Q73UooddtWyi9LzRHyAasf/7nf1ZWVpaef/55HT16VDNnztTChQvldrsvCELx8fHe66jcbrfi4uJ85uPi4uRyueR2uyVJNpvNZ95ms13WdVgOh0OFhYU%2BY7m5uZo%2BfXqztxHKbLZ2gS6hzQjVXickxAS6hMsWqr0ORfS6bQrF94UfE7IBy%2BFweP%2B7Z8%2Bemj17tiZPnqwBAwZc8rkej%2Beq5i9l7NixGjp0qM%2BYxdJeLlfNVW032EVEhMtma6eqqlo1NjYFupxWLdR7HUp/FkK916GEXrdtLfW%2BEKjgFrIB6/u6du2qxsZGhYeHe49EnedyuWS32yVJCQkJF8y73W717t3b%2Bxi3262YmP/3Azl16pQ6dOjQ7FoSExMvOB1YWXlaDQ1t4w2jsbGpzexroIVqr0Ox5lDtdSii121Ta/uZh%2BQJz6%2B//lpLly71GSsrK1NUVJSys7N9brsgSSUlJerXr58kKT09XaWlpd65xsZGff311%2BrXr5%2B6deumuLg4n/m//vWvOnfunNLT01twjwAAQGsSkgGrQ4cOcjgcKioq0rlz53To0CG98MILGjt2rO6//34dO3ZM69at09mzZ/Xhhx/qww8/1JgxYyRJOTk5euedd/Q///M/qq2t1erVqxUVFaUhQ4YoIiJCY8aM0UsvvaTy8nK5XC7927/9m%2B644w517NgxwHsNAABCRUieIuzcubOKioq0fPlyb0B68MEHlZeXp%2BjoaL388stavHixFi5cqKSkJC1btkw//elPJUm33nqrfv3rX2vmzJn6%2B9//royMDBUVFemaa66RJE2fPl01NTW6//771dDQoNtuu03PPvtsAPcWAACEmjDP1V7RjWaprDwd6BJanMUSroSEGLlcNa3uXHqwuViv7175SYCrar4tM28OdAnNxuvaf%2Bi1ebwvSJ06xbbIdi8lJE8RAgAABDMCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhIflZhACAH8bHowCBxxEsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwyyBLgBtx90rPwl0CZdly8ybA10CACBEcQQLAADAMAIWAACAYQQsAAAAwwhYAAAAhnGRe4gLtQvHAQBoCziCBQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABjGfbAA%2BF2o3b/ti9/eFegSAIQYjmABAAAYRsACAAAwjIAFAABgGAHrIo4dO6YnnnhCmZmZuu2227Rs2TI1NTUFuiwAABAiuMj9IqZNm6a0tDTt2LFDf//73zVp0iR17NhRv/zlLwNdGgAACAEcwfoep9Op/fv3a/bs2YqNjVVycrImTJggh8MR6NIAAECI4AjW95SWliopKUlxcXHesbS0NB06dEjV1dWyWq2X3EZFRYUqKyt9xiyW9kpMTDReL1qOxRK8//6IiAj3%2BY6WR69bxj/%2BOeN13bYF83vulSBgfY/b7ZbNZvMZOx%2B2XC5XswKWw%2BFQYWGhz9jUqVM1bdo0c4X%2B/4Lp/jwVFRVyOBwaO3YsYbKFVVRU6LXXfu/T62B6LbQmFRUVevHFF0PqdR2qr4WLva5xdX7otcD7dctrXXHREI/Hc1XPHzt2rDZs2ODzNXbsWEPVBa/KykoVFhZecPQO5tFr/6HX/kOv/YdetzyOYH2P3W6X2%2B32GXO73QoLC5Pdbm/WNhITE/kXAQAAbRhHsL4nPT1d5eXlOnnypHfM6XSqV69eiomJCWBlAAAgVBCwvic1NVUZGRlavny5qqurVVZWpldffVU5OTmBLg0AAISIiGefffbZQBcRbG655RZt3rxZzz33nN577z2NGjVKEydOVFhYWKBLC3oxMTEaNGgQR/v8gF77D732H3rtP/S6ZYV5rvaKbgAAAPjgFCEAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsGPftt99qypQpGjRokDIzM/X444/r0KFDgS6rVXK5XMrPz9fNN9%2BszMxMTZ06VeXl5YEuq9VyOp264447NGbMmECX0uocO3ZMTzzxhDIzM3Xbbbdp2bJlampqCnRZrdKePXuUlZWlvLy8QJfSqhGwYFxubq46duyoXbt26YMPPpDVauUPcguZO3euTpw4oU2bNmnbtm2qr6/X3LlzA11Wq7Rx40ZNmzZN3bt3D3QprdK0adPUuXNn7dixQ6%2B%2B%2Bqp27Nih1157LdBltTqvvPKKFi9ezOvYDwhYMOrcuXMaP368Zs2apZiYGFmtVo0YMUIHDhwQH3tplsfjUefOnZWfny%2B73a74%2BHiNGzdOX375Jb1uAWfPnpXD4VC/fv0CXUqr43Q6tX//fs2ePVuxsbFKTk7WhAkT5HA4Al1aqxMdHa3i4mIClh9YAl0AWpeoqCiNHj3a%2B//l5eX6z//8T911110KCwsLYGWtT1hYmBYuXOgzVl5erk6dOtHrFvCPr2uYVVpaqqSkJMXFxXnH0tLSdOjQIVVXV8tqtQawutbl0UcfDXQJbQZHsNBi0tPTNWTIELVr106LFi0KdDmt3rfffqsXXnhBkydPDnQpwGVxu92y2Ww%2BY%2BfDlsvlCkRJwFUjYOGyvfvuu0pJSbno14YNG7yPKykp0YcffqjIyEhNnDiRC1avQHN7XVZWpvHjx%2BvBBx/kSMsVam6v0TI4rY3WhlOEuGz333%2B/7r///mY9tkuXLpo7d65uueUWlZaWKiMjo4Wra12a0%2Bu//OUvevzxx/XYY49p0qRJfqqs9bmc1zXMstvtcrvdPmNut1thYWGy2%2B0Bqgq4OhzBglEHDx5Udna2z2H98PDvXmaRkZGBKqvVOnz4sJ544gnl5%2BcTrhCy0tPTVV5erpMnT3rHnE6nevXqpZiYmABWBlw5AhaM6t69u2JjY7V48WJVVVWpurpay5cv17XXXqvrrrsu0OW1OosWLdKYMWP00EMPBboU4IqlpqYqIyNDy5cvV3V1tcrKyvTqq68qJycn0KUBVyzMw4lvGHbs2DEtXrxYn3/%2BuaKiotS3b1/NmTNHPXv2DHRprUp5ebmGDBmiyMjIC35rcM2aNRo4cGCAKmudhg8frv/7v/9TY2OjmpqavEdkt27dqqSkpABXF/qOHz%2Bu%2BfPn67//%2B79ltVo1btw4TZ06ld%2BINez8ZRoNDQ2SJIvluyuFnE5nwGpqrQhYAAAAhnGKEAAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAM%2B/8Afu/l0meDfYcAAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2807817225322593550\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">1.4</td>\n",
              "        <td class=\"number\">16234</td>\n",
              "        <td class=\"number\">39.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-1.8</td>\n",
              "        <td class=\"number\">9184</td>\n",
              "        <td class=\"number\">22.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:57%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1.1</td>\n",
              "        <td class=\"number\">7763</td>\n",
              "        <td class=\"number\">18.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:48%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-0.1</td>\n",
              "        <td class=\"number\">3683</td>\n",
              "        <td class=\"number\">8.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-2.9</td>\n",
              "        <td class=\"number\">1663</td>\n",
              "        <td class=\"number\">4.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:11%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-3.4</td>\n",
              "        <td class=\"number\">1071</td>\n",
              "        <td class=\"number\">2.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-1.7</td>\n",
              "        <td class=\"number\">773</td>\n",
              "        <td class=\"number\">1.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-1.1</td>\n",
              "        <td class=\"number\">635</td>\n",
              "        <td class=\"number\">1.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-3.0</td>\n",
              "        <td class=\"number\">172</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-0.2</td>\n",
              "        <td class=\"number\">10</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2807817225322593550\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">-3.4</td>\n",
              "        <td class=\"number\">1071</td>\n",
              "        <td class=\"number\">2.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:12%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-3.0</td>\n",
              "        <td class=\"number\">172</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:2%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-2.9</td>\n",
              "        <td class=\"number\">1663</td>\n",
              "        <td class=\"number\">4.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:18%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-1.8</td>\n",
              "        <td class=\"number\">9184</td>\n",
              "        <td class=\"number\">22.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-1.7</td>\n",
              "        <td class=\"number\">773</td>\n",
              "        <td class=\"number\">1.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:9%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">-1.1</td>\n",
              "        <td class=\"number\">635</td>\n",
              "        <td class=\"number\">1.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-0.2</td>\n",
              "        <td class=\"number\">10</td>\n",
              "        <td class=\"number\">0.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:1%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-0.1</td>\n",
              "        <td class=\"number\">3683</td>\n",
              "        <td class=\"number\">8.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1.1</td>\n",
              "        <td class=\"number\">7763</td>\n",
              "        <td class=\"number\">18.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:48%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">1.4</td>\n",
              "        <td class=\"number\">16234</td>\n",
              "        <td class=\"number\">39.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_cons.price.idx\">cons.price.idx<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>26</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.1%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>93.576</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>92.201</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>94.767</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram74061896390647916\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABNElEQVR4nO3csW3DMBRAQTnISBkiO6X2Th4iO9ELGA%2BxDUGMeFerYPP0SRDgZYwxNuChj6MXADP7PHoBvO/r5/bU97/X751Wcj4mCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCIRlX1Z89jXCbfMi4YpMEAjLTpBXeAN3PSYIBBNkR845/58JAkEgEGyx%2BJNVt4smCASBQBAIBIFAEAgEgUAQCASBQJjuonDVCynmNF0g7O%2BVn9CqbLEgnGKC%2BCOyl1MEwpzOcJ68jDHG0YuAWTmDQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQBAIBIFAEAgEgUAQCASBQLgD5U0gbeRqV3kAAAAASUVORK5CYII%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives74061896390647916,#minihistogram74061896390647916\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives74061896390647916\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles74061896390647916\"\n",
              "                                                  aria-controls=\"quantiles74061896390647916\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram74061896390647916\" aria-controls=\"histogram74061896390647916\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common74061896390647916\" aria-controls=\"common74061896390647916\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme74061896390647916\" aria-controls=\"extreme74061896390647916\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles74061896390647916\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>92.201</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>92.713</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>93.075</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>93.749</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>93.994</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>94.465</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>94.767</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>2.566</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>0.919</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>0.57884</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>0.0061858</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>-0.82981</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>93.576</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>0.50981</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>-0.23089</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>3854200</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>0.33506</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram74061896390647916\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nO3dfXRU1b3/8U%2BSEQKEyQOQ6OWicHkyTyAgxEZISHiIooJgBNKrNr1QQaJponQhIhIsV1Ci2IIXiFUrrdqRYBUQCqJAUbBL24rJCCpRq7KARJghEBLIw/n94S9jx4CA7GRmwvu1Fitm75mz9/6ywQ/nnDkJsizLEgAAAIwJ9vUEAAAAWhsCFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwzObrCVwsKiqO%2BXoKfi04OEhRUR105EiVGhosX0%2BnVaCm5lFT86ipedTUW5cuHX0yLmew4BeCg4MUFBSk4OAgX0%2Bl1aCm5lFT86ipedTUPxCwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwm68nAAAw6/on3/H1FM7ZGzOH%2BXoKQLPgDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObXAWvHjh1KTk5Wfn7%2BGV9TVVWl4cOH6/777/e0NTQ0aMmSJRoxYoQGDx6sKVOm6KuvvvL0u91u5eXlKTk5WUOHDtWcOXNUU1Pj6d%2BzZ49uu%2B02DRo0SKNHj9azzz7bPAsEAACtkt8GrKeffloLFizQFVdc8YOvW7p0qY4fP%2B7V9sILL2jdunUqKirS1q1b1b17d%2BXk5MiyLEnS3LlzVV1drfXr12vNmjUqKytTYWGhJKmmpkbTpk3TNddcox07dmjJkiVauXKlNm/e3DwLBQAArY7fBqy2bduquLj4BwPW3r17tX79eo0fP96r3eFwKDs7Wz179lRYWJjy8/NVVlam3bt365tvvtGWLVuUn5%2BvqKgoxcTEaMaMGVqzZo1qa2u1bds21dbW6q677lL79u0VHx%2BvW2%2B9VQ6Ho7mXDAAAWgm/DVh33HGHOnbseMZ%2By7JUUFCg/Px82e12T3tNTY327dunuLg4T1tYWJiuuOIKlZSUaM%2BePQoJCVHfvn09/fHx8Tpx4oQ%2B%2B%2BwzOZ1O9e3bVyEhIZ7%2BuLg4lZaWGl4hAABorWy%2BnsCP5XA4FBQUpAkTJmjZsmWe9qNHj8qyLIWHh3u9Pjw8XC6XSxEREQoLC1NQUJBXnyS5XC653W6vwCZJERERcrvdamhoUHDw2TNpeXm5KioqvNpstvaKjo4%2B73VeLEJCgr2%2B4sJRU/OoqXnU1Dxq6h8CMmAdPnxYv/nNb/T73//eKyj9u8b7rc6370zONM7pOBwOr9AnSTk5OcrNzT3vcS82dns7X0%2Bh1aGm5lFTcxprSU3No6a%2BFZABa9GiRbr55pu9LvM1ioiIUHBwsNxut1e72%2B1Wp06dFBUVpePHj6u%2Bvt5zGbDxtY39X3zxRZP3Nh73XEyaNEnp6elebTZbe7lcVee6xItOSEiw7PZ2qqysVn19g6%2Bn0ypQU/OoqXmVldXU1DD2qbfIyA4%2BGTcgA9batWtlt9v1yiuvSPr2vquGhgZt3bpVf/vb39S7d285nU4NGTJEklRZWakvv/xS/fr1U9euXWVZlvbu3av4%2BHhJUklJiex2u3r06KGEhAS99NJLqqurk81m8/T379//nOcXHR3d5HJgRcUx1dWx0c%2Bmvr6BOhlGTc2jpuY0BgBqah419a2ADFjbt2/3%2Bv65557TwYMHNXv2bElSVlaWioqKlJKSopiYGBUWFio2NlaJiYmSpIyMDD355JN69NFHderUKT311FPKzMyUzWZTamqqwsLCtHz5ck2dOlWffPKJiouLtXjx4hZfJwAACEx%2BG7Aaw1BdXZ0kacuWLZK%2BPZt06aWXer02LCxM7dq187RPnjxZFRUVuv3221VVVaWkpCSve6IefvhhzZs3TyNGjNAll1yiG2%2B80fMw0zZt2mjFihWaN2%2BeioqK1LlzZ%2BXn52v48OHNvWQAANBKBFk/5o5vnLeKimO%2BnoJfs9mCFRnZQS5XFae0DaGm5gVKTa9/8h1fT%2BGcvTFzWEDUNJAEyj5tKV26nPmRT82Jz3ACAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAw/w6YO3YsUPJycnKz89v0rd582aNHTtWAwYMUEZGhl5%2B%2BWWv/lWrVikjI0MDBw5UVlaWSktLPX0nT57UQw89pJSUFCUlJSk3N1cul8vTv3//ft15551KSkpSWlqaFi9erIaGhuZbKAAAaFX8NmA9/fTTWrBgga644oomfR9%2B%2BKFmzpyp3Nxcvffee3rggQf08MMP6/3335ckvfXWW1q6dKkee%2Bwx7dy5U2lpaZo%2BfbpOnDghSVqyZImcTqccDoc2bdoky7I0e/Zsz/HvuecexcTEaMuWLXruuee0ZcsWPf/88y2zcAAAEPD8NmC1bdtWxcXFpw1Ybrdb06ZN08iRI2Wz2ZSamqo%2Bffp4ApbD4dCECRPUv39/hYaGaurUqZKkrVu3qq6uTsXFxZoxY4Yuu%2BwyRUREKC8vT9u2bdOhQ4dUUlKivXv3aubMmerYsaO6d%2B%2Bu7OxsORyOFl0/AAAIXDZfT%2BBM7rjjjjP2paSkKCUlxfN9XV2dKioqFBMTI0lyOp0aM2aMpz84OFixsbEqKSlRbGysjh07pvj4eE9/z549FRoaKqfTqfLycnXt2lXh4eGe/vj4eH3%2B%2Bec6fvy4wsLCTC4TAAC0Qn4bsM5HYWGh2rdv7wlVbrfbKyBJUnh4uFwul9xutyTJbrd79dvtdk//9/saj%2BVyuc4pYJWXl6uiosKrzWZrr%2Bjo6PNb2EUkJCTY6ysuHDU1j5qaR03No6b%2BIaADlmVZKiws1Pr167Vq1Sq1bdvWq%2B9s7/0xfefC4XBo2bJlXm05OTnKzc29oONeDOz2dr6eQqtDTc2jpuY01pKamkdNfStgA1ZDQ4Nmz56tDz/8UC%2B99JK6devm6YuMjPScqWrkdrvVu3dvRUVFeb7v0KGDp//o0aPq1KmT6uvrT/veoKAgz3vPZtKkSUpPT/dqs9nay%2BWqOq81XkxCQoJlt7dTZWW16uv5xKYJ1NQ8ampeZWU1NTWMfeotMrLD2V/UDAI2YD3yyCP69NNP9dJLLykiIsKrLyEhQU6nU%2BPHj5ck1dfX66OPPlJmZqa6deum8PBwOZ1Ode3aVZL0ySef6NSpU0pISFB5ebkOHDigI0eOeAJVSUmJevXq5RXIfkh0dHSTy4EVFcdUV8dGP5v6%2BgbqZBg1NY%2BamtMYAKipedTUtwLyAu3f//53rV27VkVFRU3ClSRlZWXp1Vdf1QcffKDq6motX75cbdq00fDhwxUSEqKJEydqxYoVOnDggFwul5544gmNGjVKnTt3VlxcnBITE/X444/r%2BPHjKisr03PPPaesrCwfrBQAAAQivz2DlZiYKOnbTwhK0pYtWyR9ezZpzZo1OnbsmNLS0rzeM3jwYD377LNKSUnRvffeq7y8PB0%2BfFiJiYkqKipSaGioJCk3N1dVVVUaN26c6urqlJaWpoKCAs9xfvvb32ru3Lm69tprFRYWpsmTJ%2BunP/1pC6waAAC0BkHWhd7RjXNSUXHM11PwazZbsCIjO8jlquKUtiHU1LxAqen1T77j6ymcszdmDguImgaSQNmnLaVLl44%2BGTcgLxECAAD4MwIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGObXAWvHjh1KTk5Wfn5%2Bk74NGzbopptu0oABAzRhwgS9/fbbnr6GhgYtWbJEI0aM0ODBgzVlyhR99dVXnn632628vDwlJydr6NChmjNnjmpqajz9e/bs0W233aZBgwZp9OjRevbZZ5t3oQAAoFXx24D19NNPa8GCBbriiiua9O3Zs0ezZs3SzJkz9e677yo7O1t33323Dh48KEl64YUXtG7dOhUVFWnr1q3q3r27cnJyZFmWJGnu3Lmqrq7W%2BvXrtWbNGpWVlamwsFCSVFNTo2nTpumaa67Rjh07tGTJEq1cuVKbN29uucUDAICA5rcBq23btiouLj5twFq9erVSU1OVmpqqtm3bauzYserTp4/Wrl0rSXI4HMrOzlbPnj0VFham/Px8lZWVaffu3frmm2%2B0ZcsW5efnKyoqSjExMZoxY4bWrFmj2tpabdu2TbW1tbrrrrvUvn17xcfH69Zbb5XD4WjpEgAAgABl8/UEzuSOO%2B44Y5/T6VRqaqpXW1xcnEpKSlRTU6N9%2B/YpLi7O0xcWFqYrrrhCJSUlOnbsmEJCQtS3b19Pf3x8vE6cOKHPPvtMTqdTffv2VUhIiNexV69efc5zLy8vV0VFhVebzdZe0dHR53yMi01ISLDXV1w4amoeNTWPmppHTf2D3wasH%2BJ2uxUeHu7VFh4ern379uno0aOyLOu0/S6XSxEREQoLC1NQUJBXnyS5XC653W7Z7Xav90ZERMjtdquhoUHBwWffsA6HQ8uWLfNqy8nJUW5u7nmt82Jkt7fz9RRaHWpqHjU1p7GW1NQ8aupbARmwJHnup/ox/Wd77%2Bn8eyA7m0mTJik9Pd2rzWZrL5er6rzHvViEhATLbm%2Bnyspq1dc3%2BHo6rQI1NY%2BamldZWU1NDWOfeouM7OCTcQMyYEVGRsrtdnu1ud1uRUVFKSIiQsHBwaft79Spk6KionT8%2BHHV19d7LgM2vrax/4svvmjy3sbjnovo6OgmlwMrKo6pro6Nfjb19Q3UyTBqah41NacxAFBT86ipbwXkBdqEhASVlpZ6tZWUlKh///5q27atevfuLafT6emrrKzUl19%2BqX79%2Bik2NlaWZWnv3r1e77Xb7erRo4cSEhL08ccfq66ursmxAQAAzkVABqyJEydq586d2rZtm06ePKni4mJ98cUXGjt2rCQpKytLq1atUllZmY4fP67CwkLFxsYqMTFRUVFRysjI0JNPPqkjR47o4MGDeuqpp5SZmSmbzabU1FSFhYVp%2BfLlqq6u1u7du1VcXKysrCwfrxoAAAQKv71EmJiYKEmeM0lbtmyR9O3ZpD59%2BqiwsFALFy7U/v371atXL61cuVJdunSRJE2ePFkVFRW6/fbbVVVVpaSkJK%2Bbzh9%2B%2BGHNmzdPI0aM0CWXXKIbb7zR8zDTNm3aaMWKFZo3b56KiorUuXNn5efna/jw4S24egAAEMiCrB9zxzfOW0XFMV9Pwa/ZbMGKjOwgl6uKewYMoabmBUpNr3/yHV9P4Zy9MXNYQNQ0kATKPm0pXbp09Mm4AXmJEAAAwJ8RsAAAAAwjYAEAABhGwAIAADCMgAUAAGCY8YCVnp6uZcuW6cCBA6YPDQAAEBCMB6xbbrlFGzZs0MiRIzV16lRt3rzZ66noAAAArZ3xgJWTk6MNGzbo5ZdfVu/evfXII48oNTVVixcv1ueff256OAAAAL/TbPdgxcfHa9asWdq6daseeOABvfzyyxozZoymTJmiDz/8sLmGBQAA8LlmC1i1tbXasGGDfvGLX2jWrFmKiYnR7NmzFRsbq%2BzsbK1bt665hgYAAPAp4z%2BLsKysTMXFxXr11VdVVVWljIwMPf/88xo0aJDnNYMHD1ZBQYFuuukm08MDAAD4nPGAdcMNN6hHjx6aNm2abr75ZkVERDR5TWpqqo4cOWJ6aAAAAL9gPGCtWrVKQ4YMOevrdu/ebXpoAAAAv2D8Hqy%2Bfftq%2BvTp2rJli6ft97//vX7xi1/I7XabHg4AAMDvGA9YCxcu1LFjx9SrVy9P2/Dhw9XQ0KBFixaZHg4AAMDvGL9E%2BPbbb2vdunWKjIz0tHXv3l2FhYW68cYbTQ8HAADgd4yfwaqpqVHbtm2bDhQcrOrqatPDAQAA%2BB3jAWvw4MFatGiRjh496mk7dOiQ5s%2Bf7/WoBgAAgNbK%2BCXCBx54QP/zP/%2Bjn/zkJwoLC1NDQ4OqqqrUrVs3/eEPfzA9HAAAgN8xHrC6deum119/XX/961/15ZdfKjg4WD169NDQoUMVEhJiejgAAAC/YzxgSVKbNm00cuTI5jg0AACA3zMesL766is9/vjj%2BvTTT1VTU9Ok/8033zQ9JAAAgF9plnuwysvLNXToULVv39704QEAAPye8YBVWlqqN998U1FRUaYPDQAAEBCMP6ahU6dOnLkCAAAXNeMBa9q0aVq2bJksyzJ9aAAAgIBg/BLhX//6V/3jH//QK6%2B8ov/8z/9UcLB3hvvTn/5kekgAAAC/YjxghYWFKSUlxfRhAQAAAobxgLVw4ULThwQAAAgoxu/BkqTPPvtMS5cu1ezZsz1t//znP5tjKAAAAL9jPGDt2rVLY8eO1ebNm7V%2B/XpJ3z589I477jD6kNGPPvpId9xxh66%2B%2Bmpde%2B21mjlzpo4cOeKZQ2ZmpgYOHKgbbrhBa9eu9XrvqlWrlJGRoYEDByorK0ulpaWevpMnT%2Bqhhx5SSkqKkpKSlJubK5fLZWzeAACg9TMesJYsWaJf/epXWrdunYKCgiR9%2B/MJFy1apKeeesrIGHV1dbrzzjt11VVXaefOnVq/fr2OHDmigoIClZeXa8aMGZo8ebJ27dqlOXPmaO7cuSopKZEkvfXWW1q6dKkee%2Bwx7dy5U2lpaZo%2BfbpOnDjhmb/T6ZTD4dCmTZtkWZbXmTgAAICzMR6wPvnkE2VlZUmSJ2BJ0nXXXaeysjIjY1RUVKiiokLjxo1TmzZtFBkZqVGjRmnPnj1at26dunfvrszMTLVt21bJyclKT0/X6tWrJUkOh0MTJkxQ//79FRoaqqlTp0qStm7dqrq6OhUXF2vGjBm67LLLFBERoby8PG3btk2HDh0yMncAAND6Gb/JvWPHjqqpqVGbNm282svLy5u0/VgxMTGKjY2Vw%2BHQL3/5S9XU1Gjz5s0aPny4nE6n4uLivF4fFxenjRs3SpKcTqfGjBnj6QsODlZsbKxKSkoUGxurY8eOKT4%2B3tPfs2dPhYaGyul0KiYm5pzmV15eroqKCq82m629oqOjf%2BySW72QkGCvr7hw1NQ8amoeNTWPmvoH4wFr4MCBeuSRR/Tggw962j7//HPNmzdPP/nJT4yMERwcrKVLlyo7O1vPP/%2B8JGnIkCG67777NGPGjCZBKCIiwnMfldvtVnh4uFd/eHi4XC6X3G63JMlut3v12%2B3287oPy%2BFwaNmyZV5tOTk5ys3NPedjXKzs9na%2BnkKrQ03No6bmNNaSmppHTX3LeMCaPXu2fvaznykpKUn19fUaOHCgqqur1bt3by1atMjIGKdOndL06dN13XXXee6fmj9/vmbOnHlO7z/bU%2BYv9Cn0kyZNUnp6ulebzdZeLlfVBR23NQsJCZbd3k6VldWqr2/w9XRaBWpqHjU1r7Kympoaxj71FhnZwSfjGg9Yl156qdavX6/t27fr888/V2hoqHr06KFrr73W656sC7Fr1y59/fXXuvfeexUSEqKOHTsqNzdX48aN07Bhwzxnohq5XC7PD5%2BOjIxs0u92u9W7d2/Pa9xutzp0%2BO435OjRo%2BrUqdM5zy86OrrJ5cCKimOqq2Ojn019fQN1MoyamkdNzWkMANTUPGrqW8YDliRdcsklGjlyZHMcWpJUX1%2BvhoYGrzNNp06dkiQlJyfrz3/%2Bs9frS0tL1b9/f0lSQkKCnE6nxo8f7znWRx99pMzMTHXr1k3h4eFyOp3q2rWrpG9v2j916pQSEhKabT0AAKB1MR6w0tPTf/BMlYlnYQ0YMEDt27fX0qVLNX36dNXU1Gj58uUaPHiwxo0bp2XLlmn16tUaO3as3n33XW3fvl0Oh0OSlJWVpXvvvVc33nij%2Bvbtq2eeeUZt2rTR8OHDFRISookTJ2rFihVKTExUaGionnjiCY0aNUqdO3e%2B4HkDAICLg/GANWbMGK%2BAVV9fr88//1wlJSX62c9%2BZmSMyMhIPfPMM3r00UeVkpKiNm3aaMiQISooKFCnTp20cuVKLViwQPPnz1fXrl21ePFiXXnllZKklJQU3XvvvcrLy9Phw4eVmJiooqIihYaGSpJyc3NVVVWlcePGqa6uTmlpaSooKDAybwAAcHEIsi70ju5ztGnTJv3tb3/TQw891BLD%2BZ2KimO%2BnoJfs9mCFRnZQS5XFfcMGEJNzQuUml7/5Du%2BnsI5e2PmsICoaSAJlH3aUrp06eiTcVvsIRkjR47U66%2B/3lLDAQAA%2BEyLBayPPvrogh9/AAAAEAiM34M1efLkJm3V1dUqKyvT6NGjTQ8HAADgd4wHrO7duzf5FGHbtm2VmZmpW2%2B91fRwAAAAfsd4wDL1tHYAAIBAZTxgvfrqq%2Bf82ptvvtn08AAAAD5nPGDNmTOnyVPWJSkoKMirLSgoiIAFAABaJeMB63e/%2B52effZZTZ8%2BXX379pVlWfr444/19NNP67bbblNSUpLpIQEAAPxKs9yDVVRUpJiYGE/b1VdfrW7dumnKlClav3696SEBAAD8ivHnYH3xxRcKDw9v0m6327V//37TwwEAAPgd4wGra9euWrRokVwul6etsrJSjz/%2BuC6//HLTwwEAAPgd45cIH3jgAd13331yOBzq0KGDgoODdfz4cYWGhuqpp54yPRwAAIDfMR6whg4dqm3btmn79u06ePCgLMtSTEyMhg0bpo4dffMDFwEAAFqS8YAlSe3atdOIESN08OBBdevWrTmGAAAA8FvG78GqqanRrFmzNGDAAF1//fWSvr0Ha%2BrUqaqsrDQ9HAAAgN8xHrAWL16sPXv2qLCwUMHB3x2%2Bvr5ehYWFpocDAADwO8YD1qZNm/Tb3/5W1113neeHPtvtdi1cuFCbN282PRwAAIDfMR6wqqqq1L179ybtUVFROnHihOnhAAAA/I7xm9wvv/xy/e1vf1NSUpLXzx78y1/%2Bov/4j/8wPRzQbK5/8h1fT%2BG8bMy71tdTAAD8f8YD1k9/%2BlPdc889uuWWW9TQ0KDnnntOpaWl2rRpk%2BbMmWN6OAAAAL9jPGBNmjRJNptNf/zjHxUSEqIVK1aoR48eKiws1HXXXWd6OAAAAL9jPGAdOXJEt9xyi2655RbThwYAAAgIxm9yHzFihNe9VwAAABcb4wErKSlJGzduNH1YAACAgGH8EuFll12m//3f/1VRUZEuv/xyXXLJJV79jz/%2BuOkhAQAA/IrxgLVv3z7913/9lyTJ5XKZPjwAAIDfMxaw8vPztWTJEv3hD3/wtD311FPKyckxNQQAAEBAMHYP1ltvvdWkraioyNThAQAAAoaxgHW6Tw7yaUIAAHAxMhawGn%2Bw89naAAAAWjvjj2loScuXL9fQoUN11VVXKTs7W19//bUkadeuXcrMzNTAgQN1ww03aO3atV7vW7VqlTIyMjRw4EBlZWWptLTU03fy5Ek99NBDSklJUVJSknJzc7lZHwAAnJeADVgvvPCC1q5dq1WrVuntt99Wr1699Pvf/17l5eWaMWOGJk%2BerF27dmnOnDmaO3euSkpKJH17r9jSpUv12GOPaefOnUpLS9P06dN14sQJSdKSJUvkdDrlcDi0adMmWZal2bNn%2B3KpAAAgwBj7FGFtba3uu%2B%2B%2Bs7aZeg7Ws88%2Bq1mzZnkeCfHggw9Kkp555hl1795dmZmZkqTk5GSlp6dr9erVSkxMlMPh0IQJE9S/f39J0tSpU7Vq1Spt3bpVGRkZKi4u1qOPPqrLLrtMkpSXl6cbbrhBhw4dUkxMjJG5AwCA1s1YwBo0aJDKy8vP2mbCoUOH9PXXX%2Bvo0aMaM2aMDh8%2BrKSkJBUUFMjpdCouLs7r9XFxcZ6nyzudTo0ZM8bTFxwcrNjYWJWUlCg2NlbHjh1TfHy8p79nz54KDQ2V0%2Bk854BVXl6uiooKrzabrb2io6N/7JJbvZCQYK%2BvOH/XP/mOr6dwzt6YOczXU/hR2KfmUVPzqKl/MBaw/v35V83t4MGDkqS//OUveu6552RZlnJzc/Xggw%2BqpqamSRCKiIjw3EfldrsVHh7u1R8eHi6XyyW32y1JstvtXv12u/287sNyOBxatmyZV1tOTo5yc3PP%2BRgXK7u9na%2BngBYQGdnB11O4IOxTcxprSU3No6a%2BZfxJ7i2h8fEPU6dO9YSpe%2B65R7/4xS%2BUnJx8zu//sf1nM2nSJKWnp3u12Wzt5XJVXdBxW7OQkGDZ7e1UWVmt%2BvoGX08HzSxQ/yywT82rrKympoaxT7356h90ARmwOnfuLMn7TFPXrl1lWZZqa2s9Z6IauVwuRUVFSZIiIyOb9LvdbvXu3dvzGrfbrQ4dvvsNOXr0qDp16nTO84uOjm5yObCi4pjq6tjoZ1Nf30CdLs2j7WwAABXGSURBVAKB/nvMPjWnMQBQU/OoqW8F5AXaSy%2B9VGFhYdqzZ4%2Bnbf/%2B/brkkkuUmprq9dgFSSotLfXc1J6QkCCn0%2Bnpq6%2Bv10cffaT%2B/furW7duCg8P9%2Br/5JNPdOrUKSUkJDTzqgAAQGsRkAHLZrMpMzNTK1as0L/%2B9S8dPnxYTz31lG666SaNHz9e%2B/fv1%2BrVq3Xy5Elt375d27dv18SJEyVJWVlZevXVV/XBBx%2Bourpay5cvV5s2bTR8%2BHCFhIRo4sSJWrFihQ4cOCCXy6UnnnhCo0aN8pw1AwAAOJuAvEQoSffdd59OnTqlW2%2B9VbW1tcrIyNCDDz6oDh06aOXKlVqwYIHmz5%2Bvrl27avHixbryyislSSkpKbr33nuVl5enw4cPKzExUUVFRQoNDZUk5ebmqqqqSuPGjVNdXZ3S0tJUUFDgw5UCAIBAE2TxAwNbREXFMV9Pwa/ZbMGKjOwgl6vKb%2B4ZCKTHHgSajXnX%2BnoKP4o/7tPTCaS9%2B8bMYQFR00ASKPu0pXTp0tEn4wbkJUIAAAB/RsACAAAwjIAFAABgGAELAADAMAIWAACAYQH7mAYAgSuQPuUmBe6nHgH4DmewAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMJuvJwAAQKC4/sl3fD2F87Ix71pfT%2BGixRksAAAAwwhYAAAAhrWKgPXII4%2Bob9%2B%2Bnu937dqlzMxMDRw4UDfccIPWrl3r9fpVq1YpIyNDAwcOVFZWlkpLSz19J0%2Be1EMPPaSUlBQlJSUpNzdXLperxdYCAAACX8AHrD179ui1117zfF9eXq4ZM2Zo8uTJ2rVrl%2BbMmaO5c%2BeqpKREkvTWW29p6dKleuyxx7Rz506lpaVp%2BvTpOnHihCRpyZIlcjqdcjgc2rRpkyzL0uzZs32yNgAAEJgCOmA1NDRo3rx5ys7O9rStW7dO3bt3V2Zmptq2bavk5GSlp6dr9erVkiSHw6EJEyaof//%2BCg0N1dSpUyVJW7duVV1dnYqLizVjxgxddtllioiIUF5enrZt26ZDhw75YokAACAABfSnCP/0pz%2Bpbdu2uummm/Tkk09KkpxOp%2BLi4rxeFxcXp40bN3r6x4wZ4%2BkLDg5WbGysSkpKFBsbq2PHjik%2BPt7T37NnT4WGhsrpdComJuac5lVeXq6KigqvNputvaKjo3/UOi8GISHBXl8Bf2Kzee9P9qk51LR5Ne5dtLyADVjffPONli5dqj/84Q9e7W63u0kQioiI8NxH5Xa7FR4e7tUfHh4ul8slt9stSbLb7V79drv9vO7DcjgcWrZsmVdbTk6OcnNzz/kYFyu7vZ2vpwA0ERnZwet79qk5jbWkps3j%2B3sXLSdgA9bChQs1YcIE9erVS19//fV5vdeyrAvqP5tJkyYpPT3dq81may%2BXq%2BqCjtuahYQEy25vp8rKatXXN/h6OoCXxj%2B77FPzKiurqWkz4v87vguZARmwdu3apX/%2B859av359k77IyEjPmahGLpdLUVFRZ%2Bx3u93q3bu35zVut1sdOnz3G3L06FF16tTpnOcXHR3d5HJgRcUx1dXxl8fZ1Nc3UCf4ne/vSfapOY2hipo2D2rqOwF5cXbt2rU6fPiw0tLSlJSUpAkTJkiSkpKS1KdPH6/HLkhSaWmp%2BvfvL0lKSEiQ0%2Bn09NXX1%2Bujjz5S//791a1bN4WHh3v1f/LJJzp16pQSEhJaYGUAAKA1CMiAdf/992vTpk167bXX9Nprr6moqEiS9Nprr%2Bmmm27S/v37tXr1ap08eVLbt2/X9u3bNXHiRElSVlaWXn31VX3wwQeqrq7W8uXL1aZNGw0fPlwhISGaOHGiVqxYoQMHDsjlcumJJ57QqFGj1LlzZ18uGQAABJCAvEQYHh7udaN6XV2dJOnSSy%2BVJK1cuVILFizQ/Pnz1bVrVy1evFhXXnmlJCklJUX33nuv8vLydPjwYSUmJqqoqEihoaGSpNzcXFVVVWncuHGqq6tTWlqaCgoKWnaBAAAgoAVZF3pHN85JRcUxX0/Br9lswYqM7CCXq8pv7hkItB/qiubT%2BANz/XGfnk4g7d03Zg4LiJo2CqTaSvywZ0nq0qWjT8YNyEuEAAAA/oyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYFbMDav3%2B/cnJylJSUpOTkZN1///2qrKyUJO3Zs0e33XabBg0apNGjR%2BvZZ5/1eu%2BGDRt00003acCAAZowYYLefvttT19DQ4OWLFmiESNGaPDgwZoyZYq%2B%2BuqrFl0bAAAIbAEbsKZPny673a633npLr7zyij799FM9%2Buijqqmp0bRp03TNNddox44dWrJkiVauXKnNmzdL%2BjZ8zZo1SzNnztS7776r7Oxs3X333Tp48KAk6YUXXtC6detUVFSkrVu3qnv37srJyZFlWb5cLgAACCABGbAqKyuVkJCg%2B%2B67Tx06dNCll16q8ePH6/3339e2bdtUW1uru%2B66S%2B3bt1d8fLxuvfVWORwOSdLq1auVmpqq1NRUtW3bVmPHjlWfPn20du1aSZLD4VB2drZ69uypsLAw5efnq6ysTLt37/blkgEAQACx%2BXoCP4bdbtfChQu92g4cOKDo6Gg5nU717dtXISEhnr64uDitXr1akuR0OpWamur13ri4OJWUlKimpkb79u1TXFycpy8sLExXXHGFSkpKdNVVV53T/MrLy1VRUeHVZrO1V3R09Hmt82ISEhLs9RXwJzab9/5kn5pDTZtX495FywvIgPV9JSUl%2BuMf/6jly5dr48aNstvtXv0RERFyu91qaGiQ2%2B1WeHi4V394eLj27duno0ePyrKs0/a7XK5zno/D4dCyZcu82nJycpSbm3ueK7v42O3tfD0FoInIyA5e37NPzWmsJTVtHt/fu2g5AR%2Bw/v73v%2Buuu%2B7Sfffdp%2BTkZG3cuPG0rwsKCvL899nup7rQ%2B60mTZqk9PR0rzabrb1crqoLOm5rFhISLLu9nSorq1Vf3%2BDr6QBeGv/ssk/Nq6yspqbNiP/v%2BC5kBnTAeuutt/SrX/1Kc%2BfO1c033yxJioqK0hdffOH1OrfbrYiICAUHBysyMlJut7tJf1RUlOc1p%2Bvv1KnTOc8rOjq6yeXAiopjqqvjL4%2Bzqa9voE7wO9/fk%2BxTcxpDFTVtHtTUdwL24uw//vEPzZo1S7/5zW884UqSEhIS9PHHH6uurs7TVlJSov79%2B3v6S0tLvY7V2N%2B2bVv17t1bTqfT01dZWakvv/xS/fr1a%2BYVAQCA1iIgA1ZdXZ0efPBBzZw5U0OHDvXqS01NVVhYmJYvX67q6mrt3r1bxcXFysrKkiRNnDhRO3fu1LZt23Ty5EkVFxfriy%2B%2B0NixYyVJWVlZWrVqlcrKynT8%2BHEVFhYqNjZWiYmJLb5OAAAQmALyEuEHH3ygsrIyLViwQAsWLPDq%2B8tf/qIVK1Zo3rx5KioqUufOnZWfn6/hw4dLkvr06aPCwkItXLhQ%2B/fvV69evbRy5Up16dJFkjR58mRVVFTo9ttvV1VVlZKSkprcsA4AAPBDAjJgXX311fr4449/8DUvvfTSGftGjx6t0aNHn7YvKChIubm5fOIPAAD8aAF5iRAAAMCfEbAAAAAMI2ABAAAYRsACAAAwLCBvcsd3rn/yHV9P4ZxtzLvW11MAAKBFcAYLAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIznYAEAfGZU4Q5fTwFoFpzBAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAzjOVhoMdc/%2BY6vpwAAQIvgDBYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjMc0AADQSgXS43E25l3r6ykYxRksAAAAwwhYAAAAhhGwTmP//v268847lZSUpLS0NC1evFgNDQ2%2BnhYAAAgQ3IN1Gvfcc4/i4%2BO1ZcsWHT58WNOmTVPnzp3185//3NdTAwAAAYAzWN9TUlKivXv3aubMmerYsaO6d%2B%2Bu7OxsORwOX08NAAAECM5gfY/T6VTXrl0VHh7uaYuPj9fnn3%2Bu48ePKyws7KzHKC8vV0VFhVebzdZe0dHRxucLoPnZbN/%2BWzQkxPsrAHMa/5y1FgSs73G73bLb7V5tjWHL5XKdU8ByOBxatmyZV9vdd9%2Bte%2B65x9xE/7/3//c648f0hfLycjkcDk2aNIkgagg1Na%2B8vFzPP/87v69pIP29wD41j5r6h9YVFw2xLOuC3j9p0iS98sorXr8mTZpkaHatU0VFhZYtW9bkzB9%2BPGpqHjU1j5qaR039A2ewvicqKkput9urze12KygoSFFRUed0jOjoaP7VAADARYwzWN%2BTkJCgAwcO6MiRI562kpIS9erVSx06dPDhzAAAQKAgYH1PXFycEhMT9fjjj%2Bv48eMqKyvTc889p6ysLF9PDQAABIiQgoKCAl9Pwt8MGzZM69ev169//Wu9/vrryszM1JQpUxQUFOTrqbVqHTp00JAhQzhTaBA1NY%2BamkdNzaOmvhdkXegd3QAAAPDCJUIAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYaDalpaW64447NGjQIA0bNkzPPPOMp2/z5s0aO3asBgwYoIyMDL388stnPM7tt9%2Bu%2BPh4JSYmen6NHTu2JZbgd36opi%2B88IIyMjJ01VVXadSoUV593%2Bd2u5WXl6fk5GQNHTpUc%2BbMUU1NTUsswe%2BYqml6eroSEhK89un06dNbYgl%2B54dq2qiqqkrDhw/X/ffff8bjsE%2B/Y6qm7NMWZAHNwOVyWUlJSVZhYaF14sQJ65NPPrHS0tKsDRs2WLt377YSExOtN954w6qtrbW2bdtmxcfHW%2B%2B9995pj3XbbbdZa9asaeEV%2BJ8fqukbb7xhDRkyxNq9e7dVX19vvffee54an87dd99t3Xnnndbhw4etgwcPWpMmTbJ%2B/etft/CKfM9kTdPS0qx33323hVfgf36opv9u4cKF1qBBg6xZs2ad8Vjs02%2BZrCn7tOVwBgvN4oMPPlBVVZXy8vLUrl079e7dW1OmTFFxcbHcbremTZumkSNHymazKTU1VX369NH777/v62n7tR%2BqaXR0tJYsWaJ%2B/fopODhYV199tXr27KlPP/20yXG%2B%2BeYbbdmyRfn5%2BYqKilJMTIxmzJihNWvWqLa21gcr8x1TNcV3fqimjfbu3av169dr/PjxZzwO%2B/Q7pmqKlkXAQrMJCgry%2Bj48PFx79uxRSkqKcnJyPO11dXWqqKhQTEzMGY%2B1YcMGjRkzRgMGDFB2dra%2B/PLLZpu3PztTTfv166fk5GRJUm1trTZu3KivvvpKaWlpTY6xZ88ehYSEqG/fvp62%2BPh4nThxQp999lnzLsAPmahpo1WrVmnkyJEaMGCAcnNzdfjw4Wadu786U00lybIsFRQUKD8/X3a7/YzHYJ96M1HTRuzTlkHAQrMYMGCA2rVrp9/85jeqrq7Wl19%2BqRdffFFHjx5t8trCwkK1b99eY8aMOe2xevbsqd69e%2BvFF1/Um2%2B%2BqaioKE2dOlWnTp1q7mX4lXOp6f/93/%2BpX79%2Bevjhh7Vo0SJdeeWVTY7jdrsVFhbm9Rd2eHi4JMnlcjX/QvyIqZpKUmxsrPr166fXXntNGzZskNvt1i9/%2BcuWWorfOFtNHQ6HgoKCNGHChB88Dvv0O6ZqKrFPW5Svr1Gi9XrvvfeszMxMa8CAAdbEiROtP/3pT1ZiYqKnv6GhwXrsscesa6%2B91iorKzvn4x47dsyKi4uzdu7c2RzT9mtnq6llWdbJkyetN9980xoyZIi1bdu2JsdYu3atNWTIEK%2B22tpaq0%2BfPtauXbuadf7%2ByERNT2ffvn1Wnz59rH/961/NMW2/dqaafvPNN9Y111xj7d2717Isy/rtb397xvuF2KfeTNT0dC7mfdrcbL4OeGi9rr76aq1evdrz/aZNmzyXARsaGjR79mx9%2BOGHeumll9StW7dzPm5YWJjCw8N16NAh43P2dz9U00Zt2rRRenq6MjIy9OKLLyo1NdWrPyoqSsePH1d9fb1CQkIkfXu2QJI6derUzCvwPyZqejpdu3aVJJWXl%2Bvyyy83O2k/d6aaLlq0SDfffLPXZb8zYZ96M1HT07mY92lz4xIhmsXJkyf15z//WcePH/e0vfPOOxowYIAk6ZFHHtGnn3561nB1/PhxFRQUeIWpI0eO6MiRI%2BcVylqDH6ppQUGBCgsLvV4fFBQkm63pv6FiY2NlWZb27t3raSspKZHdblePHj2abwF%2ByFRN9%2B/fr3nz5nldti4rK5Mk9qm%2Bq%2BnatWtVXFyspKQkJSUl6Xe/%2B51ef/11JSUlNTkO%2B/Q7pmrKPm1hvj6Fhtapvr7eSk9Ptx577DGrtrbW2rFjh9W/f3%2BrtLTUev/9963BgwdbFRUVp33v7t27rYyMDOvkyZOWZVnWzTffbN19992Wy%2BWy3G63lZuba40dO9aqr69vySX53A/V9PXXX7cGDBhgvfvuu1ZdXZ3197//3Ro0aJC1evVqy7Isa/PmzVZWVpbnWHl5edbUqVOtw4cPWwcOHLBuueUWa9GiRb5ams%2BYqml1dbU1dOhQa/78%2BVZVVZV18OBB67//%2B7%2BtadOm%2BXJ5PvFDNT1w4IDXr0ceecTKzc21Dhw4YFkW%2B/RMTNWUfdqyCFhoNh9%2B%2BKE1fvx4q1%2B/ftbo0aOtzZs3W5ZlWbNnz7auvPJKKyEhwevXz3/%2Bc8uyLOvdd9%2B1%2BvTpY9XU1FiWZVn79%2B%2B3cnJyrCFDhlhXXXWVddddd1kHDx702bp86Uw1tSzLevHFF620tDQrMTHRGjVqlPX00097%2BtasWWMlJyd7vq%2BsrLTy8/Otq666yho8eLA1f/58T6C92Jiq6d69e63s7Gxr0KBB1qBBg6z777/fOnr0aIuuxV/8UE3/3ffvF2KfnpmpmrJPW06QZVmWr8%2BiAQAAtCbcgwUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhv0/RYNA59lDKCkAAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common74061896390647916\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">93.994</td>\n",
              "        <td class=\"number\">7763</td>\n",
              "        <td class=\"number\">18.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">93.91799999999999</td>\n",
              "        <td class=\"number\">6685</td>\n",
              "        <td class=\"number\">16.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.89299999999999</td>\n",
              "        <td class=\"number\">5794</td>\n",
              "        <td class=\"number\">14.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:74%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">93.444</td>\n",
              "        <td class=\"number\">5175</td>\n",
              "        <td class=\"number\">12.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:66%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">94.465</td>\n",
              "        <td class=\"number\">4374</td>\n",
              "        <td class=\"number\">10.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:56%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">93.2</td>\n",
              "        <td class=\"number\">3616</td>\n",
              "        <td class=\"number\">8.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">93.075</td>\n",
              "        <td class=\"number\">2458</td>\n",
              "        <td class=\"number\">6.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:32%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.20100000000001</td>\n",
              "        <td class=\"number\">770</td>\n",
              "        <td class=\"number\">1.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.963</td>\n",
              "        <td class=\"number\">715</td>\n",
              "        <td class=\"number\">1.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.431</td>\n",
              "        <td class=\"number\">447</td>\n",
              "        <td class=\"number\">1.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (16)</td>\n",
              "        <td class=\"number\">3391</td>\n",
              "        <td class=\"number\">8.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme74061896390647916\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.20100000000001</td>\n",
              "        <td class=\"number\">770</td>\n",
              "        <td class=\"number\">1.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.37899999999999</td>\n",
              "        <td class=\"number\">267</td>\n",
              "        <td class=\"number\">0.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.431</td>\n",
              "        <td class=\"number\">447</td>\n",
              "        <td class=\"number\">1.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.469</td>\n",
              "        <td class=\"number\">178</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">92.649</td>\n",
              "        <td class=\"number\">357</td>\n",
              "        <td class=\"number\">0.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:46%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">94.199</td>\n",
              "        <td class=\"number\">303</td>\n",
              "        <td class=\"number\">0.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:7%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">94.215</td>\n",
              "        <td class=\"number\">311</td>\n",
              "        <td class=\"number\">0.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:8%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">94.465</td>\n",
              "        <td class=\"number\">4374</td>\n",
              "        <td class=\"number\">10.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">94.601</td>\n",
              "        <td class=\"number\">204</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">94.76700000000001</td>\n",
              "        <td class=\"number\">128</td>\n",
              "        <td class=\"number\">0.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_cons.conf.idx\">cons.conf.idx<br/>\n",
              "            <small>Numeric</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-6\">\n",
              "    <div class=\"row\">\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "                <tr>\n",
              "                    <th>Distinct count</th>\n",
              "                    <td>26</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Unique (%)</th>\n",
              "                    <td>0.1%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Missing (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Infinite (n)</th>\n",
              "                    <td>0</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "\n",
              "        </div>\n",
              "        <div class=\"col-sm-6\">\n",
              "            <table class=\"stats \">\n",
              "\n",
              "                <tr>\n",
              "                    <th>Mean</th>\n",
              "                    <td>-40.503</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Minimum</th>\n",
              "                    <td>-50.8</td>\n",
              "                </tr>\n",
              "                <tr>\n",
              "                    <th>Maximum</th>\n",
              "                    <td>-26.9</td>\n",
              "                </tr>\n",
              "                <tr class=\"ignore\">\n",
              "                    <th>Zeros (%)</th>\n",
              "                    <td>0.0%</td>\n",
              "                </tr>\n",
              "            </table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "<div class=\"col-md-3 collapse in\" id=\"minihistogram2497465625800538450\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAABLCAYAAAA1fMjoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAABLUlEQVR4nO3csU3FMBRA0XzESAzBTtTsxBDsZBZAVwQpxDydU6ewi6sXF/ZjrbUO4FtPdy8AdvZ89wL%2Bk5e3j1Pff76/XrQS/ooJAkEgEAQCwRlkAGej65ggEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJBIBAEAkEgEAQCQSAQBAJhxIWpsxeGjsOlIX7GBIEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgCgSAQCAKBIBAIAoEgEAgjHm3Y1aTHJCbt5QwTBIJAIGz3i/WbUT7JpP1P%2BC3bLhCuNynCqz3WWuvuRcCunEEgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgCASCQCAIBIJAIAgEgkAgfAG7lB7OAPTCeAAAAABJRU5ErkJggg%3D%3D\">\n",
              "\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#descriptives2497465625800538450,#minihistogram2497465625800538450\"\n",
              "       aria-expanded=\"false\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"row collapse col-md-12\" id=\"descriptives2497465625800538450\">\n",
              "    <ul class=\"nav nav-tabs\" role=\"tablist\">\n",
              "        <li role=\"presentation\" class=\"active\"><a href=\"#quantiles2497465625800538450\"\n",
              "                                                  aria-controls=\"quantiles2497465625800538450\" role=\"tab\"\n",
              "                                                  data-toggle=\"tab\">Statistics</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#histogram2497465625800538450\" aria-controls=\"histogram2497465625800538450\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Histogram</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#common2497465625800538450\" aria-controls=\"common2497465625800538450\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Common Values</a></li>\n",
              "        <li role=\"presentation\"><a href=\"#extreme2497465625800538450\" aria-controls=\"extreme2497465625800538450\"\n",
              "                                   role=\"tab\" data-toggle=\"tab\">Extreme Values</a></li>\n",
              "\n",
              "    </ul>\n",
              "\n",
              "    <div class=\"tab-content\">\n",
              "        <div role=\"tabpanel\" class=\"tab-pane active row\" id=\"quantiles2497465625800538450\">\n",
              "            <div class=\"col-md-4 col-md-offset-1\">\n",
              "                <p class=\"h4\">Quantile statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Minimum</th>\n",
              "                        <td>-50.8</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>5-th percentile</th>\n",
              "                        <td>-47.1</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q1</th>\n",
              "                        <td>-42.7</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Median</th>\n",
              "                        <td>-41.8</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Q3</th>\n",
              "                        <td>-36.4</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>95-th percentile</th>\n",
              "                        <td>-33.6</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Maximum</th>\n",
              "                        <td>-26.9</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Range</th>\n",
              "                        <td>23.9</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Interquartile range</th>\n",
              "                        <td>6.3</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "            <div class=\"col-md-4 col-md-offset-2\">\n",
              "                <p class=\"h4\">Descriptive statistics</p>\n",
              "                <table class=\"stats indent\">\n",
              "                    <tr>\n",
              "                        <th>Standard deviation</th>\n",
              "                        <td>4.6282</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Coef of variation</th>\n",
              "                        <td>-0.11427</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Kurtosis</th>\n",
              "                        <td>-0.35856</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Mean</th>\n",
              "                        <td>-40.503</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>MAD</th>\n",
              "                        <td>3.9383</td>\n",
              "                    </tr>\n",
              "                    <tr class=\"\">\n",
              "                        <th>Skewness</th>\n",
              "                        <td>0.30318</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Sum</th>\n",
              "                        <td>-1668200</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Variance</th>\n",
              "                        <td>21.42</td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th>Memory size</th>\n",
              "                        <td>321.9 KiB</td>\n",
              "                    </tr>\n",
              "                </table>\n",
              "            </div>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-8 col-md-offset-2\" id=\"histogram2497465625800538450\">\n",
              "            <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAYAAAByNR6YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nO3df1yUZb7/8TcwAQIOPzSsQ6auGvFLVluj1EQxtSw1DX/Qqdaz2mpSHEh3y9TU1tISs009KrW6udupSeqYWqbHUrc2t8fZth8wmiXpmh4VshlFBBSY7x99Hc%2BI5a9rmBl8PR%2BPHizXdc91X/dnrmHf3vfNTZDL5XIJAAAAxgT7egIAAADNDQELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhm8fUELhcVFZW%2BnoJbcHCQ4uIi9f33VWpocPl6Os0e9W461LrpUOumRb0v3pVXtvTJfjmDdRkKDg5SUFCQgoODfD2VywL1bjrUuulQ66ZFvQMPAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADLP4egIAzLj9%2Bb/6egrnbX1%2BT19PAQC8ijNYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIb5dcD64IMP1KNHDxUUFPzoNlVVVerTp48ee%2Bwxd1tDQ4MWLFigfv36qXv37ho7dqy%2B/fZbd7/T6VR%2Bfr569OihXr16aerUqaqpqXH379ixQ/fee69uuOEGDRgwQMuXL/fOAQIAgGbJbwPWiy%2B%2BqNmzZ6tdu3Y/ud3ChQt17Ngxj7ZXXnlFa9euVVFRkTZv3qz27dsrNzdXLpdLkjR9%2BnRVV1dr3bp1euONN1RWVqbCwkJJUk1NjcaPH6%2BbbrpJH3zwgRYsWKBly5Zp48aN3jlQAADQ7PhtwAoLC1NxcfFPBqwvv/xS69at07BhwzzabTabxowZo44dOyoqKkoFBQUqKyvT559/ru%2B%2B%2B06bNm1SQUGB4uLi1KZNG02cOFFvvPGGTp48qS1btujkyZN68MEHFRERoZSUFI0YMUI2m83bhwwAAJoJv/1jz/fff/9P9rtcLs2cOVMFBQX63//9X1VWVkr64QzUrl27lJyc7N42KipK7dq1U0lJiSorKxUSEqLExER3f0pKio4fP65vvvlGdrtdiYmJCgkJcfcnJydr1apV5z338vJyVVRUeLRZLBGKj48/7zG8KSQk2OMrvIt6N2axeKcW1LrpUOumRb0Dj98GrHOx2WwKCgrS8OHDtWjRInf7kSNH5HK5FB0d7bF9dHS0HA6HYmJiFBUVpaCgII8%2BSXI4HHI6nbJarR6vjYmJkdPpVENDg4KDz724bTabx5wkKTc3V3l5eRd8nN5ktbbw9RQuK9T7tNjYSK%2BOT62bDrVuWtQ7cARkwDp8%2BLB%2B//vf649//KNHUPq/Tt1vdaF9P%2BbH9nM2o0aNUlZWlkebxRIhh6PqgvfrDSEhwbJaW%2Bjo0WrV1zf4ejrNHvVuzFufBWrddKh106LeF8/b/6D7MQEZsObOnau77rrL4zLfKTExMQoODpbT6fRodzqdatWqleLi4nTs2DHV19e7LwOe2vZU/549exq99tS45yM%2BPr7R5cCKikrV1fnXh6K%2BvsHv5tScUe/TvF0Hat10qHXTot6BIyAD1po1a2S1WvXmm29K%2BuG%2Bq4aGBm3evFkff/yxOnfuLLvdrhtvvFGSdPToUe3du1ddunRRQkKCXC6XvvzyS6WkpEiSSkpKZLVa1aFDB6WmpurVV19VXV2dLBaLuz89Pd03BwsAAAJOQAasrVu3eny/YsUKHTx4UFOmTJEk5eTkqKioSL1791abNm1UWFiopKQkpaWlSZIGDhyo559/Xs8884xOnDihxYsXKzs7WxaLRZmZmYqKitKSJUs0btw4ffXVVyouLta8efOa/DgBAEBg8tuAdSoM1dXVSZI2bdok6YezSVdddZXHtlFRUWrRooW7ffTo0aqoqNB9992nqqoqZWRkeNx0/uSTT2rGjBnq16%2BfrrjiCt15553uh5mGhoZq6dKlmjFjhoqKitS6dWsVFBSoT58%2B3j5kAADQTAS5LuaOb1ywiopKX0/BzWIJVmxspByOKq7lN4Gmqvftz//Va2Obtj6/p1fGZW03HWrdtKj3xbvyypY%2B2S8P1AAAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACG%2Be1zsAAAF4dHdgC%2BxxksAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAML8OWB988IF69OihgoKCRn0bN27UkCFD1LVrVw0cOFCvv/66R//KlSs1cOBAdevWTTk5OSotLXX31dbW6oknnlDv3r2VkZGhvLw8ORwOd//%2B/fv161//WhkZGerbt6/mzZunhoYG7x0oAABoVvw2YL344ouaPXu22rVr16jviy%2B%2B0OTJk5WXl6f/%2BZ//0eOPP64nn3xSf//73yVJ77//vhYuXKhnn31WH330kfr27asJEybo%2BPHjkqQFCxbIbrfLZrNpw4YNcrlcmjJlinv8hx9%2BWG3atNGmTZu0YsUKbdq0SS%2B//HLTHDgAAAh4fhuwwsLCVFxcfNaA5XQ6NX78eN16662yWCzKzMzUdddd5w5YNptNw4cPV3p6usLDwzVu3DhJ0ubNm1VXV6fi4mJNnDhRV199tWJiYpSfn68tW7bo0KFDKikp0ZdffqnJkyerZcuWat%2B%2BvcaMGSObzdakxw8AAAKXxdcT%2BDH333//j/b17t1bvXv3dn9fV1eniooKtWnTRpJkt9s1aNAgd39wcLCSkpJUUlKipKQkVVZWKiUlxd3fsWNHhYeHy263q7y8XAkJCYqOjnb3p6SkaPfu3Tp27JiioqJMHiYAAGiG/DZgXYjCwkJFRES4Q5XT6fQISJIUHR0th8Mhp9MpSbJarR79VqvV3X9m36mxHA7HeQWs8vJyVVRUeLRZLBGKj4%2B/sAPzkpCQYI%2Bv8C7q3ZjF4p1aUOvA46210NywtgNPQAcsl8ulwsJCrVu3TitXrlRYWJhH37leezF958Nms2nRokUebbm5ucrLy7ukcU2zWlv4egqXFep9WmxspFfHp9aBw9troblhbQeOgA1YDQ0NmjJlir744gu9%2Buqratu2rbsvNjbWfabqFKfTqc6dOysuLs79fWTk6Q/2kSNH1KpVK9XX15/1tUFBQe7XnsuoUaOUlZXl0WaxRMjhqLqgY/SWkJBgWa0tdPRoterr%2Be1Ib6PejXnrs0CtA4%2B//Fz0d6zti%2BerEB%2BwAevpp5/W119/rVdffVUxMTEefampqbLb7Ro2bJgkqb6%2BXtu3b1d2drbatm2r6Oho2e12JSQkSJK%2B%2BuornThxQqmpqSovL9eBAwf0/fffuwNVSUmJOnXq5BHIfkp8fHyjy4EVFZWqq/OvD0V9fYPfzak5o96nebsO1Dpw8D5dGNZ24AjIi7mffPKJ1qxZo6KiokbhSpJycnK0evVqffbZZ6qurtaSJUsUGhqqPn36KCQkRCNHjtTSpUt14MABORwOPffcc%2Brfv79at26t5ORkpaWlaf78%2BTp27JjKysq0YsUK5eTk%2BOBIAQBAIPLbM1hpaWmSfvgNQUnatGmTpB/OJr3xxhuqrKxU3759PV7TvXt3LV%2B%2BXL1799Yjjzyi/Px8HT58WGlpaSoqKlJ4eLgkKS8vT1VVVRo6dKjq6urUt29fzZw50z3OCy%2B8oOnTp6tnz56KiorS6NGjdc899zTBUQMAgOYgyHWpd3TjvFRUVPp6Cm4WS7BiYyPlcFRxqrkJNFW9b3/%2Br14b27T1%2BT29Mi5r%2BwesheaHtX3xrryypU/2G5CXCAEAAPwZAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAY5tcB64MPPlCPHj1UUFDQqO%2Bdd97R4MGD1bVrVw0fPlwffvihu6%2BhoUELFixQv3791L17d40dO1bffvutu9/pdCo/P189evRQr169NHXqVNXU1Lj7d%2BzYoXvvvVc33HCDBgwYoOXLl3v3QAEAQLPitwHrxRdf1OzZs9WuXbtGfTt27NCjjz6qyZMn629/%2B5vGjBmjhx56SAcPHpQkvfLKK1q7dq2Kioq0efNmtW/fXrm5uXK5XJKk6dOnq7q6WuvWrdMbb7yhsrIyFRYWSpJqamo0fvx43XTTTfrggw%2B0YMECLVu2TBs3bmy6gwcAAAHNbwNWWFiYiouLzxqwVq1apczMTGVmZiosLExDhgzRddddpzVr1kiSbDabxowZo44dOyoqKkoFBQUqKyvT559/ru%2B%2B%2B06bNm1SQUGB4uLi1KZNG02cOFFvvPGGTp48qS1btujkyZN68MEHFRERoZSUFI0YMUI2m62pSwAAAAKUxdcT%2BDH333//j/bZ7XZlZmZ6tCUnJ6ukpEQ1NTXatWuXkpOT3X1RUVFq166dSkpKVFlZqZCQECUmJrr7U1JSdPz4cX3zzTey2%2B1KTExUSEiIx9irVq0677mXl5eroqLCo81iiVB8fPx5j%2BFNISHBHl/hXdS7MYvFO7Wg1oHHW2uhuWFtBx6/DVg/xel0Kjo62qMtOjpau3bt0pEjR%2BRyuc7a73A4FBMTo6ioKAUFBXn0SZLD4ZDT6ZTVavV4bUxMjJxOpxoaGhQcfO7FbbPZtGjRIo%2B23Nxc5eXlXdBxepvV2sLXU7isUO/TYmMjvTo%2BtQ4c3l4LzQ1rO3AEZMCS5L6f6mL6z/Xas/m/gexcRo0apaysLI82iyVCDkfVBe/XG0JCgmW1ttDRo9Wqr2/w9XSaPerdmLc%2BC9Q68PjLz0V/x9q%2BeL4K8QEZsGJjY%2BV0Oj3anE6n4uLiFBMTo%2BDg4LP2t2rVSnFxcTp27Jjq6%2BvdlwFPbXuqf8%2BePY1ee2rc8xEfH9/ocmBFRaXq6vzrQ1Ff3%2BB3c2rOqPdp3q4DtQ4cvE8XhrUdOALyYm5qaqpKS0s92kpKSpSenq6wsDB17txZdrvd3Xf06FHt3btXXbp0UVJSklwul7788kuP11qtVnXo0EGpqanauXOn6urqGo0NAABwPgIyYI0cOVIfffSRtmzZotraWhUXF2vPnj0aMmSIJCknJ0crV65UWVmZjh07psLCQiUlJSktLU1xcXEaOHCgnn/%2BeX3//fc6ePCgFi9erOzsbFksFmVmZioqKkpLlixRdXW1Pv/8cxUXFysnJ8fHRw0AAAKF314iTEtLkyT3maRNmzZJ%2BuFs0nXXXafCwkLNmTNH%2B/fvV6dOnbRs2TJdeeWVkqTRo0eroqJC9913n6qqqpSRkeFx0/mTTz6pGTNmqF%2B/frriiit05513uh9mGhoaqqVLl2rGjBkqKipS69atVVBQoD59%2BjTh0QMAgEAW5LqYO75xwSoqKn09BTeLJVixsZFyOKq4lt8Emqretz//V6%2BNbdr6/J5eGZe1/QPWQvPD2r54V17Z0if7DchLhAAAAP6MgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYZjxgZWVladGiRTpw4IDpoQEAAAKC8YB1991365133tGtt96qcePGaePGjR5/OBkAAKC5Mx6wcnNz9c477%2Bj1119X586d9fTTTyszM1Pz5s3T7t27Te8OAADA73jtHqyUlBQ9%2Buij2rx5sx5//HG9/vrrGjRokMaOHasvvvjCW7sFAADwOa8FrJMnT%2Bqdd97RAw88oEcffVRt2rTRlClTlJSUpDFjxmjt2rXe2jUAAIBPWUwPWFZWpuLiYq1evVpVVVUaOHCgXn75Zd1www3ubbp3766ZM2dq8ODBpncPAADgc8YD1h133KEOHTpo/PjxuuuuuxQTE9Nom8zMTH3//femdw0AAOAXjAeslStX6sYbbzzndp9//rnpXQMAAPgF4/dgJSYmasKECdq0aZO77Y9//KMeeOABOZ1O07sDAADwO8YD1pw5c1RZWalOnTq52/r06aOGhgbNnTvX9O4AAAD8jvFLhB9%2B%2BKHWrl2r2NhYd1v79u1VWFioO%2B%2B80/TuAAAA/I7xM1g1NTUKCwtrvKPgYFVXV5veHQAAgN8xHrC6d%2B%2BuuXPn6siRI%2B62Q4cOadasWR6PagAAAGiujF8ifPzxx/WrX/1KN998s6KiotTQ0KCqqiq1bdtWf/rTn0zvDgAAwO8YD1ht27bV22%2B/rb/85S/au3evgoOD1aFDB/Xq1UshISGmdwcAAOB3jAcsSQoNDdWtt97qjaEBAAD8nvGA9e2332r%2B/Pn6%2BuuvVVNT06j/vffeM71LAAAAv%2BKVe7DKy8vVq1cvRUREmB4eAADA7xkPWKWlpXrvvfcUFxdnemgAAICAYPwxDa1ateLMFQAAuKwZD1jjx4/XokWL5HK5TA8NAAAQEIxfIvzLX/6if/zjH3rzzTd1zTXXKDjYM8O99tprpncJAADgV4wHrKioKPXu3dv0sI1s375dc%2BfO1fbt2xUWFqabb75Zjz/%2BuOLi4rRt2zbNnz9f33zzja6%2B%2BmqNHz9eQ4YMcb925cqVeuWVV1RRUaHExERNnTpVqampkqTa2lo99dRT2rJli2pra5WRkaFZs2Z5/G1FXJzbn/%2Brr6dwQdbn9/T1FAAAAcp4wJozZ47pIRupq6vTr3/9aw0fPlwvvfSSqqqqNGnSJM2cOVPTpk3TxIkTNXXqVA0ePFiffPKJHnzwQXXo0EFpaWl6//33tXDhQr300ktKTEzUypUrNWHCBG3cuFERERFasGCB7Ha7bDabWrRooenTp2vKlClaunSp148LAAA0D8bvwZKkb775RgsXLtSUKVPcbZ9%2B%2Bqmx8SsqKlRRUaGhQ4cqNDRUsbGx6t%2B/v3bs2KG1a9eqffv2ys7OVlhYmHr06KGsrCytWrVKkmSz2TR8%2BHClp6crPDxc48aNkyRt3rxZdXV1Ki4u1sSJE3X11VcrJiZG%2Bfn52rJliw4dOmRs/gAAoHkzHrC2bdumIUOGaOPGjVq3bp2kHx4%2Bev/99xt7yGibNm2UlJQkm82mqqoqHT58WBs3blSfPn1kt9uVnJzssX1ycrJKS0slqVF/cHCwkpKSVFJSor1796qyslIpKSnu/o4dOyo8PFx2u93I3AEAQPNn/BLhggUL9Jvf/Ea//OUv1aVLF0k//H3CuXPnavHixerXr98l7yM4OFgLFy7UmDFj9PLLL0uSbrzxRk2aNEkTJ05UmzZtPLaPiYmRw%2BGQJDmdTkVHR3v0R0dHy%2BFwyOl0SpKsVqtHv9Vqdb/%2BfJSXl6uiosKjzWKJUHx8/HmP4U0hIcEeX3F2FouZ%2BlDvxkzV9kzUOvB4ay00N6ztwGM8YH311Vf685//LEkKCgpyt9922216/PHHjezjxIkTmjBhgm677TZNmDBBx48f16xZszR58uTzev25HiFxqY%2BYsNlsWrRokUdbbm6u8vLyLmlc06zWFr6egl%2BLjY00Oh71Ps10bc9ErQOHt9dCc8PaDhzGA1bLli1VU1Oj0NBQj/by8vJGbRdr27Zt2rdvnx555BGFhISoZcuWysvL09ChQ3XLLbe4z0Sd4nA43E%2BWj42NbdTvdDrVuXNn9zZOp1ORkac/9EeOHFGrVq3Oe36jRo1SVlaWR5vFEiGHo%2BqCjtNbQkKCZbW20NGj1aqvb/D1dPyWqfeLejfmrc8CtQ48/vJz0d%2Bxti%2Ber0K88YDVrVs3Pf3005o2bZq7bffu3ZoxY4ZuvvlmI/uor69XQ0ODx5mmEydOSJJ69Oih//qv//LYvrS0VOnp6ZKk1NRU2e12DRs2zD3W9u3blZ2drbZt2yo6Olp2u10JCQmSfjgjd%2BLECfdjHM5HfHx8o8uBFRWVqqvzrw9FfX2D383Jn5iuDfU%2Bzdt1oNaBg/fpwrC2A4fxi7lTpkzRp59%2BqoyMDNXW1qpbt24aNGiQnE6nHnvsMSP76Nq1qyIiIrRw4UJVV1fL4XBoyZIl6t69u4YOHar9%2B/dr1apVqq2t1datW7V161aNHDlSkpSTk6PVq1frs88%2BU3V1tZYsWaLQ0FD16dNHISEhGjlypJYuXaoDBw7I4XDoueeeU//%2B/dW6dWsjcwcAAM2f8TNYV111ldatW6etW7dq9%2B7dCg8PV4cOHdSzZ0%2BPe7IuRWxsrP7whz/omWeeUe/evRUaGqobb7xRM2fOVKtWrbRs2TLNnj1bs2bNUkJCgubNm6frr79ektS7d2898sgjys/P1%2BHDh5WWlqaioiKFh4dLkvLy8lRVVaWhQ4eqrq5Offv21cyZM43MGwAAXB6CXPzRwCZRUVHp6ym4WSzBio2NlMNR1aSnmi/XJ7k3Vb0Dqb7eekq%2Br9a2v2EtND%2Bs7Yt35ZUtfbJf42ewsrKyfvJMlalnYQEAAPgr4wFr0KBBHgGrvr5eu3fvVklJiX75y1%2Ba3h0AAIDfMR6wfuxZVBs2bNDHH39sencAAAB%2Bp8keCXvrrbfq7bffbqrdAQAA%2BEyTBazt27df8hPSAQAAAoHxS4SjR49u1FZdXa2ysjINGDDA9O4AAAD8jvGA1b59%2B0a/RRgWFqbs7GyNGDHC9O4AAAD8jvGANXfuXNNDAgAABBTjAWv16tXnve1dd91levcAAAA%2BZzxgTZ06tdEfYpakoKAgj7agoCACFgAAaJaMB6yXXnpJy5cv14QJE5SYmCiXy6WdO3fqxRdf1L333quMjAzTuwQAAPArXrkHq6ioSG3atHG3/eIXv1Dbtm01duxYrVu3zvQuAQAA/Irx52Dt2bNH0dHRjdqtVqv2799vencAAAB%2Bx3jASkhI0Ny5c%2BVwONxtR48e1fz583Xttdea3h0AAIDfMX6J8PHHH9ekSZNks9kUGRmp4OBgHTt2TOHh4Vq8eLHp3QEAAPgd4wGrV69e2rJli7Zu3aqDBw/K5XKpTZs2uuWWW9SyZUvTuwMAAPA7xgOWJLVo0UL9%2BvXTwYMH1bZtW2/sAgAAwG8ZvwerpqZGjz76qLp27arbb79d0g/3YI0bN05Hjx41vTsAAAC/YzxgzZs3Tzt27FBhYaGCg08PX19fr8LCQtO7AwAA8DvGA9aGDRv0wgsv6LbbbnP/0Wer1ao5c%2BZo48aNpncHAADgd4wHrKqqKrVv375Re1xcnI4fP256dwAAAH7HeMC69tpr9fHHH0uSx98efPfdd/Uv//IvpncHAADgd4z/FuE999yjhx9%2BWHfffbcaGhq0YsUKlZaWasOGDZo6darp3QEAAPgd4wFr1KhRslgs%2BvOf/6yQkBAtXbpUHTp0UGFhoW677TbTuwMAAPA7xgPW999/r7vvvlt333236aEBAAACgvF7sPr16%2Bdx7xUAAMDlxnjAysjI0Pr1600PCwAAEDCMXyK8%2Buqr9dRTT6moqEjXXnutrrjiCo/%2B%2BfPnm94lAACAXzEesHbt2qWf/exnkiSHw2F6eAAAAL9nLGAVFBRowYIF%2BtOf/uRuW7x4sXJzc03tAgAAICAYuwfr/fffb9RWVFRkangAAICAYSxgne03B73924RLlixRr1699POf/1xjxozRvn37JEnbtm1Tdna2unXrpjvuuENr1qzxeN3KlSs1cOBAdevWTTk5OSotLXX31dbW6oknnlDv3r2VkZGhvLw8LnUCAIALYixgnfrDzudqM%2BWVV17RmjVrtHLlSn344Yfq1KmT/vjHP6q8vFwTJ07U6NGjtW3bNk2dOlXTp09XSUmJpB/OtC1cuFDPPvusPvroI/Xt21cTJkxw/53EBQsWyG63y2azacOGDXK5XJoyZYrXjgMAADQ/xh/T0FSWL1%2BugoIC/exnP1NUVJSmTZumadOmae3atWrfvr2ys7MVFhamHj16KCsrS6tWrZIk2Ww2DR8%2BXOnp6QoPD9e4ceMkSZs3b1ZdXZ2Ki4s1ceJEXX311YqJiVF%2Bfr62bNmiQ4cO%2BfJwAQBAADH%2BW4RN4dChQ9q3b5%2BOHDmiQYMG6fDhw8rIyNDMmTNlt9uVnJzssX1ycrL72Vx2u12DBg1y9wUHByspKUklJSVKSkpSZWWlUlJS3P0dO3ZUeHi47Ha72rRpc17zKy8vV0VFhUebxRKh%2BPj4iz1ko0JCgj2%2B4uwsFjP1od6Nmartmah14PHWWmhuWNuBx1jAOnnypCZNmnTONhPPwTp48KAk6d1339WKFSvkcrmUl5enadOmqaamplEQiomJcd9H5XQ6FR0d7dEfHR0th8Mhp9MpSbJarR79Vqv1gu7DstlsWrRokUdbbm6u8vLyznuMpmC1tvD1FPxabGyk0fGo92mma3smah04vL0WmhvWduAwFrBuuOEGlZeXn7PNhFM3z48bN84dph5%2B%2BGE98MAD6tGjx3m//mL7z2XUqFHKysryaLNYIuRwVF3SuKaEhATLam2ho0erVV/f4Ovp%2BC1T7xf1bsxbnwVqHXj85WEnWVcAABQ3SURBVOeiv2NtXzxfhXhjAev/Pv/K21q3bi3J80xTQkKCXC6XTp486T4TdYrD4VBcXJwkKTY2tlG/0%2BlU586d3ds4nU5FRp5%2BQ44cOaJWrVqd9/zi4%2BMbXQ6sqKhUXZ1/fSjq6xv8bk7%2BxHRtqPdp3q4DtQ4cvE8XhrUdOALyYu5VV12lqKgo7dixw922f/9%2BXXHFFcrMzPR47IIklZaWKj09XZKUmpoqu93u7quvr9f27duVnp6utm3bKjo62qP/q6%2B%2B0okTJ5SamurlowIAAM1FQAYsi8Wi7OxsLV26VP/85z91%2BPBhLV68WIMHD9awYcO0f/9%2BrVq1SrW1tdq6dau2bt2qkSNHSpJycnK0evVqffbZZ6qurtaSJUsUGhqqPn36KCQkRCNHjtTSpUt14MABORwOPffcc%2Brfv7/7rBkAAMC5BORvEUrSpEmTdOLECY0YMUInT57UwIEDNW3aNEVGRmrZsmWaPXu2Zs2apYSEBM2bN0/XX3%2B9JKl379565JFHlJ%2Bfr8OHDystLU1FRUUKDw%2BXJOXl5amqqkpDhw5VXV2d%2Bvbtq5kzZ/rwSAEAQKAJcnn7ceuQ9MM9WP7CYglWbGykHI6qJr2Wf/vzf22yfZmwPr%2BnkXGaqt6BVF9TtT2Tr9a2v2EtND%2Bs7Yt35ZUtfbLfgLxECAAA4M8IWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGBYswhYTz/9tBITE93fb9u2TdnZ2erWrZvuuOMOrVmzxmP7lStXauDAgerWrZtycnJUWlrq7qutrdUTTzyh3r17KyMjQ3l5eXI4HE12LAAAIPAFfMDasWOH3nrrLff35eXlmjhxokaPHq1t27Zp6tSpmj59ukpKSiRJ77//vhYuXKhnn31WH330kfr27asJEybo%2BPHjkqQFCxbIbrfLZrNpw4YNcrlcmjJlik%2BODQAABKaADlgNDQ2aMWOGxowZ425bu3at2rdvr%2BzsbIWFhalHjx7KysrSqlWrJEk2m03Dhw9Xenq6wsPDNW7cOEnS5s2bVVdXp%2BLiYk2cOFFXX321YmJilJ%2Bfry1btujQoUO%2BOEQAABCAAjpgvfbaawoLC9PgwYPdbXa7XcnJyR7bJScnuy8DntkfHByspKQklZSUaO/evaqsrFRKSoq7v2PHjgoPD5fdbvfy0QAAgObC4usJXKzvvvtOCxcu1J/%2B9CePdqfTqTZt2ni0xcTEuO%2Bjcjqdio6O9uiPjo6Ww%2BGQ0%2BmUJFmtVo9%2Bq9V6QfdhlZeXq6KiwqPNYolQfHz8eY/hTSEhwR5fcXYWi5n6UO/GTNX2TNQ68HhrLTQ3rO3AE7ABa86cORo%2BfLg6deqkffv2XdBrXS7XJfWfi81m06JFizzacnNzlZeXd0njmma1tvD1FPxabGyk0fGo92mma3smah04vL0WmhvWduAIyIC1bds2ffrpp1q3bl2jvtjYWPeZqFMcDofi4uJ%2BtN/pdKpz587ubZxOpyIjT3/ojxw5olatWp33/EaNGqWsrCyPNoslQg5H1XmP4U0hIcGyWlvo6NFq1dc3%2BHo6fsvU%2B0W9G/PWZ4FaBx5/%2Bbno71jbF89XIT4gA9aaNWt0%2BPBh9e3bV9LpM04ZGRn61a9%2B1Sh4lZaWKj09XZKUmpoqu92uYcOGSZLq6%2Bu1fft2ZWdnq23btoqOjpbdbldCQoIk6auvvtKJEyeUmpp63vOLj49vdDmwoqJSdXX%2B9aGor2/wuzn5E9O1od6nebsO1Dpw8D5dGNZ24AjIi7mPPfaYNmzYoLfeektvvfWWioqKJElvvfWWBg8erP3792vVqlWqra3V1q1btXXrVo0cOVKSlJOTo9WrV%2Buzzz5TdXW1lixZotDQUPXp00chISEaOXKkli5dqgMHDsjhcOi5555T//791bp1a18eMgAACCABeQYrOjra40b1uro6SdJVV10lSVq2bJlmz56tWbNmKSEhQfPmzdP1118vSerdu7ceeeQR5efn6/Dhw0pLS1NRUZHCw8MlSXl5eaqqqtLQoUNVV1envn37aubMmU17gAAAIKAFZMA60zXXXKOdO3e6v%2B/evbvHw0fPdM899%2Biee%2B45a19oaKhmzJihGTNmGJ8nAAC4PATkJUIAAAB/RsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhhGwAAAADCNgAQAAGEbAAgAAMIyABQAAYBgBCwAAwDACFgAAgGEELAAAAMMIWAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhgVswNq/f79yc3OVkZGhHj166LHHHtPRo0clSTt27NC9996rG264QQMGDNDy5cs9XvvOO%2B9o8ODB6tq1q4YPH64PP/zQ3dfQ0KAFCxaoX79%2B6t69u8aOHatvv/22SY8NAAAEtoANWBMmTJDVatX777%2BvN998U19//bWeeeYZ1dTUaPz48brpppv0wQcfaMGCBVq2bJk2btwo6Yfw9eijj2ry5Mn629/%2BpjFjxuihhx7SwYMHJUmvvPKK1q5dq6KiIm3evFnt27dXbm6uXC6XLw8XAAAEkIAMWEePHlVqaqomTZqkyMhIXXXVVRo2bJj%2B/ve/a8uWLTp58qQefPBBRUREKCUlRSNGjJDNZpMkrVq1SpmZmcrMzFRYWJiGDBmi6667TmvWrJEk2Ww2jRkzRh07dlRUVJQKCgpUVlamzz//3JeHDAAAAojF1xO4GFarVXPmzPFoO3DggOLj42W325WYmKiQkBB3X3JyslatWiVJstvtyszM9HhtcnKySkpKVFNTo127dik5OdndFxUVpXbt2qmkpEQ///nPz2t%2B5eXlqqio8GizWCIUHx9/QcfpLSEhwR5fcXYWi5n6UO/GTNX2TNQ68HhrLTQ3rO3AE5AB60wlJSX685//rCVLlmj9%2BvWyWq0e/TExMXI6nWpoaJDT6VR0dLRHf3R0tHbt2qUjR47I5XKdtd/hcJz3fGw2mxYtWuTRlpubq7y8vAs8Mu%2ByWlv4egp%2BLTY20uh41Ps007U9E7UOHN5eC80NaztwBHzA%2BuSTT/Tggw9q0qRJ6tGjh9avX3/W7YKCgtz/%2B1z3U13q/VajRo1SVlaWR5vFEiGHo%2BqSxjUlJCRYVmsLHT1arfr6Bl9Px2%2BZer%2Bod2Pe%2BixQ68DjLz8X/R1r%2B%2BL5KsQHdMB6//339Zvf/EbTp0/XXXfdJUmKi4vTnj17PLZzOp2KiYlRcHCwYmNj5XQ6G/XHxcW5tzlbf6tWrc57XvHx8Y0uB1ZUVKquzr8%2BFPX1DX43J39iujbU%2BzRv14FaBw7epwvD2g4cAXsx9x//%2BIceffRR/f73v3eHK0lKTU3Vzp07VVdX524rKSlRenq6u7%2B0tNRjrFP9YWFh6ty5s%2Bx2u7vv6NGj2rt3r7p06eLlIwIAAM1FQAasuro6TZs2TZMnT1avXr08%2BjIzMxUVFaUlS5aourpan3/%2BuYqLi5WTkyNJGjlypD766CNt2bJFtbW1Ki4u1p49ezRkyBBJUk5OjlauXKmysjIdO3ZMhYWFSkpKUlpaWpMfJwAACEwBeYnws88%2BU1lZmWbPnq3Zs2d79L377rtaunSpZsyYoaKiIrVu3VoFBQXq06ePJOm6665TYWGh5syZo/3796tTp05atmyZrrzySknS6NGjVVFRofvuu09VVVXKyMhodMM6AADATwnIgPWLX/xCO3fu/MltXn311R/tGzBggAYMGHDWvqCgIOXl5fndb/wBAIDAEZCXCAEAAPwZAQsAAMAwAhYAAIBhAXkPFoDAdvvzf/X1FC7I%2Bvyevp4CgADDGSwAAADDCFgAAACGcYkQAIDzxOVtnC/OYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAELAADAMAIWAACAYQQsAAAAwwhYAAAAhll8PQFcmtuf/6uvpwAAAM7AGSwAAADDOIMFAOfAmWIAF4ozWAAAAIZxBgsAgGYqkM6%2Brs/v6espGMUZLAAAAMM4gwUA8JlAOsMCXAjOYAEAABhGwAIAADCMgHUW%2B/fv169//WtlZGSob9%2B%2BmjdvnhoaGnw9LQAAECC4B%2BssHn74YaWkpGjTpk06fPiwxo8fr9atW%2Bvf/u3ffD01AAAQADiDdYaSkhJ9%2BeWXmjx5slq2bKn27dtrzJgxstlsvp4aAAAIEJzBOoPdbldCQoKio6PdbSkpKdq9e7eOHTumqKioc45RXl6uiooKjzaLJULx8fHG5wvvsVjM/PsjJCTY4ysAoDFTP3P9BQHrDE6nU1ar1aPtVNhyOBznFbBsNpsWLVrk0fbQQw/p4YcfNjfR/%2B/vT912wa8pLy%2BXzWbTqFGjCH1NoLy8XC%2B//JLX630xa6G5YW03HWrdtKh34GlecdEQl8t1Sa8fNWqU3nzzTY//Ro0aZWh2l66iokKLFi1qdJYN3kG9mw61bjrUumlR78DDGawzxMXFyel0erQ5nU4FBQUpLi7uvMaIj4/nXxgAAFzGOIN1htTUVB04cEDff/%2B9u62kpESdOnVSZGSkD2cGAAACBQHrDMnJyUpLS9P8%2BfN17NgxlZWVacWKFcrJyfH11AAAQIAImTlz5kxfT8Lf3HLLLVq3bp1%2B97vf6e2331Z2drbGjh2roKAgX0/NmMjISN14442clWsi1LvpUOumQ62bFvUOLEGuS72jGwAAAB64RAgAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABgGAHrMpKVlaXU1FSlpaW5/5swYYK7f8eOHbr33nt1ww03aMCAAVq%2BfLkPZ9u8vPzyy0pMTNS%2BffskSfv27VNiYqLHe5GWlqY//OEPPp5p4Duz1hJr26R9%2B/Zp4sSJuvHGG5WRkaEHHnhAu3fvdvexrs35qVpLrGt/Z/H1BNC0/vCHPygjI6NRe01NjcaPH6%2BRI0eqqKhIu3fv1q9%2B9Stdc801GjBggA9m2nwcOnToR3/wlZSUNPFsmrez1Zq1bVZubq7S09O1efNmuVwuTZ8%2BXQUFBVq9erV7G9a1GT9Va9a1/%2BMMFiRJW7Zs0cmTJ/Xggw8qIiJCKSkpGjFihGw2m6%2BnFvCeeuopjR492tfTuCycrdasbXNOnDihe%2B%2B9V5MmTVJkZKSioqJ05513ateuXeLP2pp1rlqzrv0fAesys3LlSt16663q2rWr8vLydPjwYUmS3W5XYmKiQkJC3NsmJyertLTUV1NtFrZu3aqdO3dq7NixZ%2B3/7W9/q169eummm27S/PnzdfLkySaeYfPxY7VmbZsTGhqqESNGKDo6WpJ04MAB/ed//qduu%2B02BQUFubdjXV%2B6c9Wade3/CFiXkaSkJHXp0kVvvfWW3nnnHTmdTv37v/%2B7JMnpdMpqtXpsHxMTI6fTqYaGBl9MN%2BDV1NTod7/7nZ544gmFhoZ69IWGhqpr167q37%2B/Nm/erKKiIq1Zs0b/8R//4aPZBrafqjVr2ztSU1PVp08ftWjRQk8%2B%2BaQk1rW3nK3WrGv/xz1Yzchbb72l3/72t2ftmzNnjhYvXuz%2BPjIyUjNmzNCgQYO0d%2B/eHx3z//6rFJ7OVe9//vOfSk1NVc%2BePRv1x8fH67XXXnN/36VLF40fP17Lli1zh16cdim1/jGs7bM7V62HDx8uSSotLdXBgwf1zDPPaOzYsXrllVdY1xfoUmr9Y1jX/oOA1YwMHTpUQ4cOPe/tExISJEnl5eWKi4vTnj17PPqdTqdiYmIUHMyJzrP5qXqXlZVp3rx5Hjf%2BnktCQoK%2B%2B%2B47uVwufkie4VJqzdq%2BMBfyc%2BSqq67SlClTdMstt8hutystLa3RNqzrH3cptWZd%2Bz/ehcvE/v37NWPGDJ04ccLdVlZWJklq27atUlNTtXPnTtXV1bn7S0pKlJ6e3uRzbQ7Wr1%2BvyspKDRkyRBkZGe7f3Bw%2BfLhefPFFbdu2TUuWLPF4zTfffKOEhAT%2BT%2BgCnavWrG1zvvnmG2VmZsrhcLjbTv2f%2BRVXXMG6NuhctWZdBwAXLgvV1dWuXr16uWbNmuWqqqpyHTx40PWv//qvrvHjx7tcLpertrbW1bdvX9cLL7zgOn78uOuzzz5z/eIXv3Bt3rzZtxMPUJWVla4DBw54/Hfddde5Pv30U1dlZaWrpKTElZKS4lq9erXrxIkTri%2B%2B%2BMLVs2dP1/Lly3099YBzrlqzts2pq6tz3XHHHa5HHnnEdeTIEVdlZaXrsccec916662u2tpa1rVB56o169r/Bblc/G7t5WLnzp2aO3eu%2Bxk1/fv315QpU9w3Sn711VeaMWOGSktL1bp1az3wwAO65557fDnlZiUxMVHvvfeerrnmGknSf//3f2vRokXas2ePWrZsqfvuu08PPPAAp/cNOLPWrG1z9u/fr9mzZ%2Btvf/ubQkND1aVLFz322GPq2LGjJNa1SeeqNevavxGwAAAADOOfFAAAAIYRsAAAAAwjYAEAABhGwAIAADCMgAUAAGAYAQsAAMAwAhYAAIBhBCwAAADDCFgAAACGEbAAAAAMI2ABAAAYRsACAAAwjIAFAABg2P8D%2BkTTUsGxTq4AAAAASUVORK5CYII%3D\"/>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\" id=\"common2497465625800538450\">\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">-36.4</td>\n",
              "        <td class=\"number\">7763</td>\n",
              "        <td class=\"number\">18.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-42.7</td>\n",
              "        <td class=\"number\">6685</td>\n",
              "        <td class=\"number\">16.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:86%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-46.2</td>\n",
              "        <td class=\"number\">5794</td>\n",
              "        <td class=\"number\">14.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:74%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-36.1</td>\n",
              "        <td class=\"number\">5175</td>\n",
              "        <td class=\"number\">12.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:66%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-41.8</td>\n",
              "        <td class=\"number\">4374</td>\n",
              "        <td class=\"number\">10.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:56%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-42.0</td>\n",
              "        <td class=\"number\">3616</td>\n",
              "        <td class=\"number\">8.8%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:47%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-47.1</td>\n",
              "        <td class=\"number\">2458</td>\n",
              "        <td class=\"number\">6.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:32%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-31.4</td>\n",
              "        <td class=\"number\">770</td>\n",
              "        <td class=\"number\">1.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-40.8</td>\n",
              "        <td class=\"number\">715</td>\n",
              "        <td class=\"number\">1.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:10%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-26.9</td>\n",
              "        <td class=\"number\">447</td>\n",
              "        <td class=\"number\">1.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:6%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"other\">\n",
              "        <td class=\"fillremaining\">Other values (16)</td>\n",
              "        <td class=\"number\">3391</td>\n",
              "        <td class=\"number\">8.2%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:44%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "        <div role=\"tabpanel\" class=\"tab-pane col-md-12\"  id=\"extreme2497465625800538450\">\n",
              "            <p class=\"h4\">Minimum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">-50.8</td>\n",
              "        <td class=\"number\">128</td>\n",
              "        <td class=\"number\">0.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:3%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-50.0</td>\n",
              "        <td class=\"number\">282</td>\n",
              "        <td class=\"number\">0.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:5%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-49.5</td>\n",
              "        <td class=\"number\">204</td>\n",
              "        <td class=\"number\">0.5%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:4%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-47.1</td>\n",
              "        <td class=\"number\">2458</td>\n",
              "        <td class=\"number\">6.0%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:42%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-46.2</td>\n",
              "        <td class=\"number\">5794</td>\n",
              "        <td class=\"number\">14.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "            <p class=\"h4\">Maximum 5 values</p>\n",
              "            \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">-33.0</td>\n",
              "        <td class=\"number\">172</td>\n",
              "        <td class=\"number\">0.4%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:23%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-31.4</td>\n",
              "        <td class=\"number\">770</td>\n",
              "        <td class=\"number\">1.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-30.1</td>\n",
              "        <td class=\"number\">357</td>\n",
              "        <td class=\"number\">0.9%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:46%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-29.8</td>\n",
              "        <td class=\"number\">267</td>\n",
              "        <td class=\"number\">0.6%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:35%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">-26.9</td>\n",
              "        <td class=\"number\">447</td>\n",
              "        <td class=\"number\">1.1%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:58%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "</div><div class=\"row variablerow ignore\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_euribor3m\"><s>euribor3m</s><br/>\n",
              "            <small>Highly correlated</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <p><em>This variable is highly correlated with <a href=\"#pp_var_emp.var.rate\"><code>emp.var.rate</code></a> and should be ignored for analysis</em></p>\n",
              "</div>\n",
              "<div class=\"col-md-6\">\n",
              "    <table class=\"stats \">\n",
              "        <tr>\n",
              "            <th>Correlation</th>\n",
              "            <td>0.97224</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow ignore\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_nr.employed\"><s>nr.employed</s><br/>\n",
              "            <small>Highly correlated</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <p><em>This variable is highly correlated with <a href=\"#pp_var_euribor3m\"><code>euribor3m</code></a> and should be ignored for analysis</em></p>\n",
              "</div>\n",
              "<div class=\"col-md-6\">\n",
              "    <table class=\"stats \">\n",
              "        <tr>\n",
              "            <th>Correlation</th>\n",
              "            <td>0.94515</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "</div><div class=\"row variablerow\">\n",
              "    <div class=\"col-md-3 namecol\">\n",
              "        <p class=\"h4 pp-anchor\" id=\"pp_var_y\">y<br/>\n",
              "            <small>Categorical</small>\n",
              "        </p>\n",
              "    </div><div class=\"col-md-3\">\n",
              "    <table class=\"stats \">\n",
              "        <tr class=\"\">\n",
              "            <th>Distinct count</th>\n",
              "            <td>2</td>\n",
              "        </tr>\n",
              "        <tr>\n",
              "            <th>Unique (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (%)</th>\n",
              "            <td>0.0%</td>\n",
              "        </tr>\n",
              "        <tr class=\"ignore\">\n",
              "            <th>Missing (n)</th>\n",
              "            <td>0</td>\n",
              "        </tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-6 collapse in\" id=\"minifreqtable-7279775792428764372\">\n",
              "    <table class=\"mini freq\">\n",
              "        <tr class=\"\">\n",
              "    <th>no</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:100%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 88.7%\">\n",
              "            36548\n",
              "        </div>\n",
              "        \n",
              "    </td>\n",
              "</tr><tr class=\"\">\n",
              "    <th>yes</th>\n",
              "    <td>\n",
              "        <div class=\"bar\" style=\"width:13%\" data-toggle=\"tooltip\" data-placement=\"right\" data-html=\"true\"\n",
              "             data-delay=500 title=\"Percentage: 11.3%\">\n",
              "            &nbsp;\n",
              "        </div>\n",
              "        4640\n",
              "    </td>\n",
              "</tr>\n",
              "    </table>\n",
              "</div>\n",
              "<div class=\"col-md-12 text-right\">\n",
              "    <a role=\"button\" data-toggle=\"collapse\" data-target=\"#freqtable-7279775792428764372, #minifreqtable-7279775792428764372\"\n",
              "       aria-expanded=\"true\" aria-controls=\"collapseExample\">\n",
              "        Toggle details\n",
              "    </a>\n",
              "</div>\n",
              "<div class=\"col-md-12 extrapadding collapse\" id=\"freqtable-7279775792428764372\">\n",
              "    \n",
              "<table class=\"freq table table-hover\">\n",
              "    <thead>\n",
              "    <tr>\n",
              "        <td class=\"fillremaining\">Value</td>\n",
              "        <td class=\"number\">Count</td>\n",
              "        <td class=\"number\">Frequency (%)</td>\n",
              "        <td style=\"min-width:200px\">&nbsp;</td>\n",
              "    </tr>\n",
              "    </thead>\n",
              "    <tr class=\"\">\n",
              "        <td class=\"fillremaining\">no</td>\n",
              "        <td class=\"number\">36548</td>\n",
              "        <td class=\"number\">88.7%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:100%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr><tr class=\"\">\n",
              "        <td class=\"fillremaining\">yes</td>\n",
              "        <td class=\"number\">4640</td>\n",
              "        <td class=\"number\">11.3%</td>\n",
              "        <td>\n",
              "            <div class=\"bar\" style=\"width:13%\">&nbsp;</div>\n",
              "        </td>\n",
              "</tr>\n",
              "</table>\n",
              "</div>\n",
              "</div>\n",
              "    <div class=\"row headerrow highlight\">\n",
              "        <h1>Correlations</h1>\n",
              "    </div>\n",
              "    <div class=\"row variablerow\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoUAAAI6CAYAAABRtTJ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nOzde1zUdb7H8ddwR1EQBTS8ruYVFbxRaZqoSam42UUlTdMy72Ha2lqb1qnN1VxdL5na5mUtl7Jdu7h7NCyzNmvzeEO8FZoXNMEEFAWGy5w/kMlRNGy%2Bkwy%2Bn48Hj4Hf/OY93/nCDJ/5/C5jsdlsNkRERETkpuZxowcgIiIiIjeeikIRERERUVEoIiIiIioKRURERAQVhSIiIiKCikIRERERQUWhiIiIiKCiUERERERQUSgiIiIiqCgUEREREcDrRg9ARCqXZs2aXfU6b29vgoKCiIiI4Le//S29e/fGYrH8iqMTEZGrseizj0XEpNKisE2bNoSEhDhcl5%2Bfz6FDhzhx4gQA3bt3Z/78%2Bfj4%2BPzq4xQREUcqCkXEqNKicNGiRfTs2bPMdZKSknj66ae5cOECI0aMYOrUqb/mEEVEpAzap1BEfnU9e/Zk8uTJAKxZs4b8/PwbPCIREdE%2BhSJyQ9x99938z//8D7m5uezdu5eoqCiH67du3cpbb73Fzp07ycrKIiAggObNm3P//ffTr1%2B/MjOzsrJYvnw5n332GUeOHMFqtRIYGEibNm149NFHiY6OvuI2MTExpKWlsWzZMvLz85k7dy7Hjh1jxYoVtG/fHoCDBw/y5ptvsm3bNk6dOoWXlxehoaF06tSJIUOGlLkf5blz51i5ciWbNm3iyJEjFBQUUKtWLdq1a8fw4cNp3bq1w/rHjx%2BnR48eAOzevZvU1FQWL17Mjh07yMrKIjQ0lB49ejBp0iSqVKnyi%2BZcRORa1CkUkRsiKCjI/v3Zs2cdrps7dy7Dhw/n448/JiAggOjoaIKDg9m6dStTpkwhISGBoqIih9tkZGQwYMAAXn/9dQ4dOkTLli257bbb8PHx4dNPP2XYsGG89957Vx3PoUOHmDRpEt7e3tx22234%2BfkB8N///pf777%2Bff/7zn%2BTn59O%2BfXuioqLIycnhnXfe4aGHHmLr1q0OWWlpadx3330sWLCA7777jubNm3Pbbbdhs9n46KOPeOihh1i7du1Vx7J9%2B3bi4%2BNJSUmhefPmNGrUiLS0NFatWsWECRPKPcciItfFJiJiUNOmTW1Nmza1ffzxx9dcLzU11b7u7t277cs3btxoa9q0qa1jx462r7/%2B2uE2//3vf2133HGHrWnTprbly5c7XPfSSy/ZmjZtauvdu7ft9OnT9uWFhYW2l19%2B2da0aVNb%2B/btbTk5OQ636969u61p06a2mJgY2%2BLFi68Y54MPPmhr2rSpbebMmbaioiKH3Llz59qaNm1q69u3r8Nt4uPjbU2bNrUNGDDAdurUKfvyoqIi24IFC2xNmza1RURE2A4dOmS/7tixY/b5uOuuu2xLly61FRcX26//5z//ab9%2B375915paEZFfRJ1CEbkhNm7cCEBgYCAtWrSwL1%2B8eDEAU6dOpVOnTg636dixI7/73e8AWLlypcN1oaGh9OnTh/Hjx1OzZk37ck9PTyZNmoSHhwfnzp1j586dZY6nuLiYUaNGXbF83759AAwYMAAPj59eMj09PZk4cSITJ05k6NChWK1WAHbt2sW2bdsAmDVrFqGhofbbeHh4MH78eFq1aoXVauXvf/97mWO59dZbefzxxx1O19O/f3%2BCg4Pt9yEiYpr2KRSRX91nn33GkiVLAHjsscfw8ip5KTp16hQpKSkA9OrVq8zb9uzZE4vFwokTJzh8%2BDCNGjUC4PHHH7/q/fn7%2B1OzZk0yMjLIyMgoc53bbrvNoegrVb16dU6fPs2//vUvnnzySYfrPDw8GDdunMOyzz//HCg5Crtx48ZXfQwpKSlXbHYuVdY%2BkxaLhXr16nHmzBkyMzPLvJ2IiDNUFIqISyxZsoR//OMfDsusVivff/89x44dA0q6b4899pj9%2BoMHD9q/f%2BaZZ66a7eXlRUFBAd9//729KCzN/%2Byzz9izZw/p6emcPXsW28Wzbp07dw4o6QiWJSwsrMzlw4YNY86cObz22mt8/vnn9O3bl9tvv/2qJ%2Bn%2B7rvvgJJu39X85je/AUr2YyxL/fr1y1zu6%2BsLQEFBwVWzRUR%2BKRWFIuISu3fvvmKZp6cnNWrUoEePHgwcOJBu3bo5XJ%2BdnW3/ftOmTT97H6WFHsD%2B/fsZN24cx48f/0XjDQwMLHN56SblJUuWkJycTHJyMgAhISHExsYybNgw6tWrd8WYqlevftX7qlatGlBS3OXl5dkPaimlk3mLyI2golBEXOJaJ6%2B%2BmtJ96Ly9vUlOTi73R%2BDl5eUxZswYTpw4QcOGDRk7diy33347NWrUwNvbG/jp1DM/d99lGTVqFIMGDSIpKYnPPvuML7/8koyMDP72t7%2BRmJjIrFmzuOeee8r9OG2XfGZAWZusRURuBBWFIlJhlJ6mpqCggMzMTPuBFT9ny5YtnDhxAovFwtKlS2nQoMEV6%2BTl5Tk1turVqzNgwAAGDBhAUVERX375JYsWLWLHjh08%2B%2Byz3HbbbdSoUcPecbz8NDuXKr3Oz89PXUERqTD0FlVEKoymTZvav//222/Lfbvvv/8eKNkXr6yC8OjRo/z4449Oj6%2BUp6cnd955J6tWrSIsLIzz58/bN5eX7kt46f6Rlyu97lr7HYqI/NpUFIpIhRESEkKrVq0AeOedd8pc5/Dhw/Tv399%2B6hr4aR%2B9q31c3qJFi%2BzfX37S62vZtWsX06ZN48033yzzeh8fH2rVqgX81Iks3U/y4MGDZRaGxcXF9tPx3HnnneUei4iIq6koFJEKZfTo0QB89NFHrFixwmH/uyNHjjBx4kT279/PyZMn7cubN28OwA8//EBSUpJ9eW5uLi%2B99BLJycn2j9G72hG/ZfH09OS9995j/vz5bN68%2BYrrP/74Y/bt24e3tzft2rUDoGXLlnTp0gWAadOmcebMGfv6hYWFzJ49m9TUVKpVq8agQYPKPRYREVfTPoUiUqHcfffdjBo1iqVLl/LKK6%2BwatUqfvOb35CZmcm%2BffsoKiqiZcuWTJkyxX6bqKgounTpwhdffMGECRNo27Ytvr6%2B7NmzBy8vL5YvX8769evZsWMHq1at4sCBA4wZM4aOHTtecywRERGMGDGCN998kyeeeILw8HAaNGiAh4cHx48ft2%2B2fvbZZwkJCbHf7o9//CPDhg0jOTmZmJgYmjZtiq%2BvL99%2B%2By2ZmZn4%2BfkxZ86cq54GR0TkRlBRKCIVzuTJk7njjjtYvXo1O3fuZOvWrfj6%2BhIREcG9995LfHz8FQdozJs3j1dffZVNmzaxZ88eQkND6d27N6NHj6Z%2B/frUrl2bvXv3sn37dr799ttyH9k8depUOnbsyLp169izZw87duygsLCQkJAQ%2Bvbty5AhQ%2BxdyFJhYWGsXbuWFStW8PHHH/Ptt99SWFhIWFgYd999NyNHjixz30cRkRvJYrt024yIiIiI3JS0T6GIiIiIqCgUERERERWFIiIiIoKKQhERERGX%2Bfzzz7njjjuYNGnSNdcrLi5m7ty59OjRg44dOzJy5EiOHTtmvz4rK4uEhATuuOMOunTpwrPPPuv0JzVdTkWhiIiIiAssW7aMl156qVxnG3jrrbf48MMPWbp0KZ9%2B%2BikNGzZk3Lhx9nO1/uEPfyA3N5ePPvqI9957j9TUVF599VWj41VRKCIiIuICvr6%2BrF27tlxFYWJiIsOHD6dx48YEBAQwadIkUlNT2bVrF6dPnyYpKYlJkyYRHBxMWFgYY8eO5b333qOgoMDYeHWeQhEREZEypKenk5GR4bAsJCSE0NDQct3%2BkUceKdd6eXl5fPfdd7Rs2dK%2BLCAggAYNGpCcnMy5c%2Bfw9PSkWbNm9utbtWrFhQsXOHTokMNyZ6godFflPPFuuTVqBN9%2BC7feCocPG4u1FbvPaTAtuMdYc/PM/u4tFvD1hfx8MH3WUn%2BPsj%2BL%2BBfz8QGr1WwmYPPxNZ7pCu7yNwrAxU97McLLC%2BrWhePHobDQXC5A/fpm8wA8PKC42HhsQbGn0TwvL/PTCeDtbT6zXEz/XwQS589n4cKFDsvGjx/PhAkTjN5PdnY2NpuNwMBAh%2BWBgYFkZmYSFBREQECAw0n3S9fNzMw0Ng4VhVIiKAg8PUsuKziLxXzx4jJuMlgXvJaaZ7H89OUmc%2BoGw3SPgXp4lIzTw032eHKLJ5TbDPOGGjhwIDExMQ7LLv1IS9Ou9Xkiv8ZnjagoFBEREffngjcNoaGh5d5U7IygoCA8PDzIyspyWJ6VlUXNmjUJDg4mJyeHoqIiPD097dcB1KxZ09g43ORtl4iIiEjl5Ovry6233kpKSop92dmzZzl69Cht2rShRYsW2Gw29u/fb78%2BOTmZ6tWr06hRI2PjUFEoIiIi7s/Dw/yXC506dYrY2Fj7uQgHDx7MqlWrSE1NJScnh1dffZUWLVrQunVrgoOD6d27N/PmzePMmTP88MMPLFq0iAceeAAvL3MbfbX5WERERNxfBdzntHXr1gAUXjyiJykpCSjp8hUUFHD48GGsFw%2BeGzRoEBkZGQwdOpTz588THR3tcJDLiy%2B%2ByPTp0%2BnRowfe3t707dv3Z0%2BIfb0stl9jz0Uxz/QewlFRsH07tGsHO3YYi3XF0ceu2i/eJUd2umCwrjj62M8P8vIq%2BNHHFstPRx8bHqgrjj52xd%2Bpu/yNAmaPPvbxgfBwSEszf/S5K44%2B9vSEoiLjsaaPPvb2BoOnuHPIvSF8XXAWgXzDZ1Co4NQpFBEREfdXATuF7kYzKCIiIiLqFIqIiEgloE6h01QUioiIiPtTUeg0zaCIiIiIqFMoIiIilYA6hU7TDIqIiIiIOoUiIiJSCahT6DQVhSIiIuL%2BVBQ6TTMoIiIiIuoUioiISCWgTqHTVBSKiIiI%2B1NR6DQVheWUnJzMK6%2B8wsGDB/Hx8aFXr14899xzeHt78%2B677zJ37lysVisDBw4kKyuLoqIiZs6cCcDq1at56623OHHiBHXr1mXSpEn07NnzBj8iERERkZ%2BoKCynSZMmERcXx9/%2B9jdOnTrFoEGDaNKkCe3ateMPf/gDf/nLX7jrrrtYtmwZ7777LjExMQBs3LiRhQsX8sYbb9C8eXM%2B%2BeQTEhIS2LhxI7fccku57js9PZ2MjAyHZSGNGhEaFGTuATZv7ngpFZbF4po807nGQ106UDHOx8dclre346VIWdQpdJrFZrPZbvQg3EFOTg4%2BPj74XHyhmzx5Ml5eXjRs2JD//d//5f333wegqKiImJgYbr/9dmbOnMmoUaNo2rQpU6ZMsWcNGzaMzp07M2rUqHLd94IFC1i4cKHDsvFjxzLhyScNPToRERHnFRTcwNq9Th3zmSdPms%2BswNQpLKevvvqKRYsW8f3331NYWEhhYSGxsbFkZGQQHh5uX8/T05OWLVvafz569Cj/%2Bc9/WLlypX2ZzWajSZMm5b7vgQMH2juPpUL69YNLMp3WvDm8/TbEx8P%2B/cZibf%2B33VhWKYsFXPFWxoIrQs0PNi/fbKfMYgFfX8jPNz%2Bvfh5Wc2EWS8l/m4IC4wO1eRvsal3kir9Td/kbBeDECXNZ3t4QGgrp6SW/f5Nq1zabB%2BDpCUVFxmMLij2N5pU%2BnSoNdQqdpqKwHFJTU3nyySeZOnUqDz30EH5%2Bfjz99NMUFhZSXFyMl5fjNHpc8ofp5%2BfH5MmTGTFixC%2B%2B/9DQUEJDQx0XHj78i/Ouaf9%2B2LHDNdlihKt6%2BzabC7JdMViXDFSMsxp8Q1CqoMA1uVI5qCh0mmawHPbt24ePjw%2BPPPIIfn5%2B2Gw29u3bB0DNmjU5cck74qKiIvbu3Wv/uX79%2Bhw4cMAh78SJE2irvYiIiFQkKgrLITw8nLy8PPbt20d2djazZ8/Gx8eH9PR0oqOj2bNnD5s3b8ZqtbJ48WLy8vLstx04cCD/%2Bte/2Lx5M4WFhXz11Vf07duXXbt23cBHJCIiUsl4eJj/uslo83E5REVF8fDDDzNkyBD8/f0ZM2YM06ZNY8yYMbz99tskJCQwZcoUvL29GTZsGNHR0VguHiHZuXNnpk6dyosvvsjp06epW7cuM2bMIDIy8gY/KhEREZGf6OhjA6xWq/2oZIAhQ4bQoUMHEhISXHenpk/LERUF27dDu3ZG9ym0FZv/87rZDzTJzTN/oImfH%2BTlmZ9Xf498c2EWS8lpTqxW8wea%2BPgazQMdaML335vL8vGB8HBISzO/T2H9%2Bmbz4KY/0OSGHX3cqJH5TFftv19B3Xy9UcOOHTtGVFQUn3zyCcXFxXzxxRfs2LGDrl273uihiYiI3Dy0%2Bdhp2nzspHr16jFz5kxmz57NU089RVhYGNOnT6ddu3Y3emgiIiIi5aai0IB%2B/frRr1%2B/Gz0MERGRm9dN2NkzTTMoIiIiIuoUioiISCWgTqHTVBSKiIiI%2B1NR6DTNoIiIiIioUygiIiKVgDqFTtMMioiIiIg6hSIiIlIJqFPoNBWFIiIi4v5UFDpNMygiIiIi6hSKiIhIJaBOodNUFLopW7HNeKYFsP3fdrOZHhajeURFwfbtWNq3gx07jEb/eNrsnHp6QlAQZGVbKCoyl%2BvjYy4LfnodLSqC4mKz2fi54EXaYin5MshqNRqHxVLyeyooAJvBPytfw797Vyqq38honidQVDvcaCa45nfv5wl5BZ5Gf/eA0dcRDw/w9i75GzX9vPf2Npsnvx4VhSIiIuL%2B1Cl0mopCERERcX8qCp2mGRQRERERdQpFRESkElCn0GmaQRERERFRp1BEREQqAXUKnaaiUERERNyfikKnqSgUERERcYG0tDReeOEFdu3aRZUqVbj33nuZPHkyHpcVsCNGjOCbb75xWFZYWMi4ceMYP348Q4cOZfv27Q63a9SoER988IHR8aooFBEREfdXATuFEyZMoFWrViQlJfHjjz/yxBNPUKtWLR599FGH9d58802Hn8%2BePcu9995Lr1697Mv%2B53/%2BhwEDBrh0vBVvBkVERETcXHJyMvv372fKlClUq1aNhg0bMnz4cBITE3/2tvPmzaNXr140a9bsVxjpT9QpFBEREffngk5heno6GRkZDstCQkIIDQ392dumpKQQHh5OYGCgfVmrVq04fPgwOTk5BAQElHm7I0eOsG7dOpKSkhyW/%2Btf/%2BKNN97g5MmTtG3blhdffJH69ev/gkd1deoUioiIiPvz8DD%2BlZiYyIABAxy%2BytPpA8jKyqJ69eoOy0oLxMzMzKvebunSpdx///0EBwfblzVu3Jhbb72Vt99%2Bm02bNhEcHMxjjz2G1fCHd6tTKCIiIlKGgQMHEhMT47AsJCSk3Le32WzXdX9ZWVm8//77/Pvf/3ZYPmPGDIefX3zxRaKjo/m///s/br/99uu6j2tRUSgiIiLuzwWbj0NDQ8u1qbgswcHBZGVlOSzLysrCYrE4dAEvtWnTJho1akS9evWumR0QEEBgYCCnTp36RWO7Gm0%2BFhERETEsIiKCkydPcubMGfuy5ORkmjRpQtWqVcu8zaZNm%2BjcubPDspycHGbMmOFQAJ45c4YzZ878bPF4vVQUioiIiPtzwT6FzmjZsiWtW7dmzpw55OTkkJqayvLlyxk8eDAAsbGxbNu2zeE2%2B/bto27dug7LAgIC2LVrFy%2B99BJZWVlkZ2fzwgsv0KxZM6Kiopwa4%2BUqTVG4Zs2aK7b7u8I333xD69atje/cKSIiIk6oYEUhwPz580lPT6dz58488sgj/Pa3vyU%2BPh6Aw4cPc%2BHCBYf1MzIyqFWr1hU5ixYtwmaz0bt3b%2B666y4KCgpYunTpFSfBdpb2KSyHjRs30qxZMxo0aEDHjh1JTk6%2B0UMSERGRCq527dosW7aszOsOHDhwxbI9e/aUue4tt9zCwoULjY6tLJWmU%2BhK8%2BfP58iRIzd6GCIiInI1FbBT6G7c9hHv2rWLuLg4IiMjefTRR/nxxx8B%2BMc//nHFTpoPPfQQCxYsAGDBggU88cQTJCQk0K5dO6Bkh82JEydy%2B%2B2306FDBx5//HFOnjwJQFxcHN9%2B%2By1jx47l97//PV9//TXNmjUjPz8fgB9%2B%2BIExY8YQHR1N%2B/btmTRpkv1oo6%2B//pr27duzZcsWYmNjiYyMZOTIkWRnZ/8qcyQiInLTUFHoNLfcfFxUVMTEiRPp06cPTz75JPv372fChAl4eZXv4ezcuZMnn3ySOXPmADB79mzOnz/Ppk2bsNlsJCQk8Mc//pEFCxbwwQcf0KxZM1577TW6du3K119/7ZA1duxYmjRpwqZNm8jLy%2BPJJ59k%2BvTp/OUvfwEgNzeX9evXk5iYSG5uLg888ADvvPMOjz/%2BeLkfb1lnVK9Vq3xnVL/hDO8ES/PmjpcGeXq6Js90runXqdK8m/D1z85icU2e6Vwxz51%2B9yafo5eO02RucbG5LPn1uWVRuGfPHtLT0xkzZgy%2Bvr60bduWXr168emnn5br9p6engwePBjLxWfFCy%2B8QGFhIVWqVAGgZ8%2BevP766z%2Bbs2/fPlJSUliyZAkBAQEEBAQwatQoxo0bZz8QpaioiMcee4zAwEACAwNp3749hw4duq7Hm5iYeMW%2BBOPGjWfixAnXlVMexl/Itm83HHjR228bjwwynliiWjUXBRvm7%2B%2BKVG8XRJrP9DGeWML8UF1UZbqggjH9ZsidMgF8fV2Ta5rp531Ojtm863Izv7M1xC2Lwh9%2B%2BIHq1atT7ZL/tg0bNiz37WvXrm0vCKHkcwZnzpzJ7t27ycvLo7i4mKCgny8Rjh8/TmBgoMPZzevXr09BQYHD%2BYQuPbzc39%2BfvLy8co8Vyj6jeq1aIVznidJ/lsWC%2Bcz27cwGNm9eUhDGx8P%2B/Uajsz4xW8B6epYUhOfOQVGRuVzThYaHR8k/htxc8%2B/yq/oUmA309oYCw5mA1WZ2Ui2Wn4Zq8jnl4234CQqueeIDRcVmC01PT7PPo1Km/5wslpKCMD/f/LSafH5aLD89713w6xc35ZZFodVqpeiyV4fiazxbLl/30s3MxcXFPPHEE7Rv354NGzYQHBzMu%2B%2B%2By7x588o1jqu5tOh09pDxss6o7jZP4h07XJO7f7/xbFf8wynNNZntqs5GcfHNu%2BnHVc8nm82Nnqs3KXf63Zt8fpb%2BW7LZKtHzXp1Cp7nlDIaGhpKTk8O5c%2Bfsy1JTUwHw9fUlNzfXvryoqIi0tLSrZp0%2BfZq0tDSGDh1q/9iZvXv3lmsc9erVIzs7m9OnT9uXHTp0CF9fX8LCwq7rMYmIiIgTdKCJ09zyEbdt25bAwEDeeOMNrFYr27Zts%2B9P2KBBA86fP88XX3yB1WplyZIl1/xA6uDgYKpUqcLOnTvJz8/nww8/ZN%2B%2BfeTk5HD%2B/HmgpNA8cuQIOZftLNG6dWsaN27MnDlzuHDhAqdOnWLx4sX06dMHbxfs9yQiIiLiKm5ZFPr5%2BbFo0SI2bdpEx44dWbhwISNGjABKPmtw%2BPDhTJo0ia5du%2BLl5XXNj4Hx8vJixowZLF26lDvuuINvvvmGBQsWULt2be6%2B%2B24ABg0axKxZs3j66acdbmuxWHjttddIT0/nrrvu4qGHHqJt27Y8//zzrnvwIiIiciV1Cp1msV2rjSYVlit%2Bay450MTD8FGNUVElRzS3a2d8n8IfT5t98J6eEBQEWVlm9yn0MXyorIcHVK0K58%2Bb37eomp97HGiSX2z%2BQBMfH7BazT6nfH3c5ImP%2BxxoYvoTSy0W8PODvDzz02ry8Xt4QJUqcOGC%2Bed9QIDZvHK7/37zme%2B9Zz6zAnPLA01EREREHNyEnT3TVBSKiIiI%2B1NR6DTNoIiIiIioUygiIiKVgDqFTtMMioiIiIg6hSIiIlIJqFPoNBWFIiIi4v5UFDpNMygiIiIi6hSKiIhIJaBOodM0gyIiIiKiTqGIiIhUAuoUOk1FoYiIiLg/FYVO0wyKiIiIiDqFIiIiUgmoU%2Bg0FYVuyoLNJammc388bTbP0xOCgKxPtlNUZDSamrUsZgOjomD7doJi2sGOHeZyz541lwWUbDCoSlXOA8VGkzOyqhnL8vKCGjUgM8ebwkJjsQCEnDlgNtDXFxo2xOfE95Cfby7Xx8dcVmleeDicOAFWq9ns%2Bo3M5rmI/4lUs4E%2BPlCvHn4Zx8zPabHB56evL9SvT5XTR83%2BjQLceqvZPPnVqCgUERER96dOodNUFIqIiIj7U1HoNM2giIiIiKhTKCIiIpWAOoVO0wyKiIiIiDqFIiIiUgmoU%2Bg0FYUiIiLi/lQUOk0zKCIiIiLqFIqIiEgloE6h0zSDIiIiIqJOoYiIiFQC6hQ6TUWhiIiIuD8VhU7TDIqIiIiIOoUiIiJSCahT6DTNoIiIiIioUygiIiKVgDqFTtMMXqdvvvmG1q1bY7Vab/RQREREpJSHh/mvm8zN94id1LFjR5KTk/Hx8bnRQxEREZEKLC0tjVGjRhEdHU337t2ZPXs2xcXFV6y3YMECWrRoQevWrR2%2BTp8%2BDUB%2Bfj7PP/88Xbt2JTo6mokTJ5KZmWl8vCoKRURExP1VwE7hhAkTCAsLIykpieXLl5OUlMTKlSvLXLd///4kJyc7fNWqVQuAuXPnkpKSQmJiIhs2bMBms/H73//e6fFdrsIVhceOHWPEiBFERUXRvXt3Vq1aBUBycjLx8fF06NCBO%2B64g%2BnTp1NQUADA119/Tbt27di0aRMxMTFERUUxb948kpOTiYuLIyoqivHjx9vXHzp0KH/%2B859JSEggMjKSbt268fHHH9vHcPToUUaOHEl0dDTR0dE89dRTnD171n5fzZo1Iz8/H4Ddu3fTu3dv2rZty%2BjRo1m9ejUxMTH2ddu3b8%2BWLVuIjY0lMjKSkSNHkp2d/avNp4iIiPz6kpOT2b9/P1OmTKFatWo0bNiQ4cOHk5iYeF05hYWFrF27lrFjx1KnTh2CgoJISEhg8%2BbNnDp1yuiYK9yBJuPHj6dTp04sWrSI77//nocffpjGjRszffp04uLi%2BNvf/sapU6cYNGgQTZo0YejQoQDk5uaydetW1q9fz4YNG3jmmWc4cOAAK7qRH1YAACAASURBVFasIDs7m7i4OD755BN69%2B4NwN///ndmzZrFrFmzeOedd5g0aRJbtmwhODiY5557jvDwcD7//HNycnIYOXIkr732Gs8884zDWK1WK6NHjyYuLo6EhAS%2B/vprpk2bhq%2Bvr32d3Nxc1q9fT2JiIrm5uTzwwAO88847PP744%2BWek/T0dDIyMhyWhdSqRWho6C%2Bd5l%2BNp6dr8kznAhAVZTaveXPHS1NM7%2BdSmueC/We8DL7CuPR3f8lz1ojS3UtM72bi7e2aPNO57sRVvyNXzKnNZi7LVeO82DC5IVzwGlbm/9%2BQkHL9/01JSSE8PJzAwED7slatWnH48GFycnIICAhwWP/AgQMMGjSIgwcPUqdOHX7/%2B9/TpUsXjh49yrlz52jVqpV93caNG%2BPn50dKSgphYWFOPsqfVKiicO/evRw4cICVK1fi7%2B9PixYtWLhwIWFhYaxbtw4fHx88PT255ZZb6NixI3v27LHftri4mPj4ePz9/YmJicFms9G7d2%2BCg4MJDg7mN7/5DUeOHLGvHxkZyV133QVAfHw8CxYs4IsvviAuLo6lS5disVjw8fEhODiYO%2B%2B8k%2B3bt18x3uTkZM6cOcOYMWPw8/OjW7du3HbbbezYscO%2BTlFREY899hiBgYEEBgbSvn17Dh06dF3zkpiYyMKFCx2WjR83jgkTJ15XTrlYLEbjgoKMxtlVq%2BaC0DJ%2Bx0a8/bZrck3z9zceWaOq8UiqVzefSY2GLggFbrnFNbmmueANpitqd5e8IahXzwWhQO3arsk1rU4ds3nffms273q4oCgs8//v%2BPFMmDDhZ2%2BblZVF9ctesEoLxMzMTIeisHbt2tSrV4/JkycTGhpKYmIio0eP5oMPPiArKwvgiqzq1asb36%2BwQhWFR48eJSAggKBLKok77rgDgKSkJHv3sLCwkMLCQmJjYx1uX%2BfiH3dpp%2B7S6tnX19e%2ByRegUaNG9u89PDyoU6cO6enpAOzZs4c5c%2BZw4MABCgoKKCoqIiIi4orxZmRkEBAQ4PAuoHXr1g5FIUDdunXt3/v7%2B5OXl1fOGSkxcOBA%2BybpUiG1apl91wglBaHhzKxss0Wmp2dJQXjuHBQVGY0mKKad2cDmzUsKwvh42L/fXO7nn5vLgpIXUn9/yM2FMnaAdkam1VxV6OlZUhCePWv%2Bd18j%2B3uzgT4%2BJQXhiRNg8kwFrugUhoZCejpc3L3GlKLa4UbzPD3N/94BPE8cMxvo7V1SEP7wg/E5Nd4prFMHTp40P85KpMz/vyEh5b69rZy/swcffJAHH3zQ/vPw4cNZv349H3zwAV27dr2uLGdUqKLQw8OjzKNyUlNTefLJJ5k6dSoPPfQQfn5%2BPP300xQWFl5x%2B2v9fKmiy15dbDYbFouF7OxsRo0axeDBg1m2bBkBAQHMmzePL7/88oqM4uJivC7bPmYpo9N2rXGUR2ho6JWt6l/hj8MEV7yIl%2BYaz76smDdm/36z2YYLN4dcw9mXPUWNKCpyQa6rNnlZrWazXfW8LygwW7y6E1c9blfMqSue%2BwUFN3aTr0ku6BSW%2Bf%2B3nIKDg%2B1dvlJZWVlYLBaCg4N/9vbh4eGkp6fb183KyqJq1Z/eaGdnZ1OzZs1fNLarqVAHmtSrV4/z58/bO3ZQ0iH897//jY%2BPD4888gh%2Bfn7YbDb27dvn1H0dO/bTu8Pi4mJ%2B%2BOEHateuzaFDhzh//jwjR460t3b37t1bZkbNmjXJzs4mJyfHviw5OdmpcYmIiMgvUMGOPo6IiODkyZOcOXPGviw5OZkmTZo4FHcAr732Glu3bnVYlpqaSr169ahXrx6BgYGkpKTYrzt48CBWq7XMrZjOqFBFYYsWLWjZsiXz5s3j/PnzHDx4kGeffRZvb2/y8vLYt28f2dnZzJ49Gx8fH9LT039xO3XHjh18%2BeWXWK1WVq9ezfnz5%2BncuTO33HILHh4e7NixgwsXLrBixQpOnz7N6dOnr%2BhMRkRE4O/vz7Jly7BarWzZsoX//ve/JqZCRERE3FjLli1p3bo1c%2BbMIScnh9TUVJYvX87gwYMBiI2NZdu2bUBJF/CFF17g0KFD5Ofn8%2Babb3L06FHuu%2B8%2BPD09eeihh3j99dc5efIkmZmZ/PnPf6ZXr172U9aYUqGKQoDXX3%2BdtLQ07rjjDkaPHs3YsWN54oknePjhhxkyZAh9%2BvQhPDycadOmcfDgQSZNmvSL7icuLo7ExEQ6derEG2%2B8wV/%2B8heCgoIICwvjqaeeYtq0aXTv3p3s7GxeffVVrFYr8fHxDhlVq1Zl3rx5rFu3jujoaN5//32GDx9e5iZkERERcaEK1ikEmD9/Punp6XTu3JlHHnmE3/72t/Za4vDhw1y4cAGAyZMn07VrV4YPH07Hjh356KOPWLFiBbUvHrA0ceJE2rZtS//%2B/enRowdVq1bl5Zdfdnp8l7PYfo09FyuYoUOH0rZtW6ZMmeJ0Vum%2BiZ4XD4ubP38%2BX331FW%2B7%2BohTV/zaXHCgyY9nzB9oEhQEWVnm9ymsWctwMR8VVXJEc7t2ZvcpvHjOTGM8PKBqVTh/3vg%2BSxl55g4T9/KCGjUgM9P8PoUhZw6YDfT1hYYN4fvvze6vZfr0KT4%2BEB4OaWnG938rqt/o51e6Di470OT7VLOBPj4lRzQfO1ax9yn09YX69eHoUfP7FN56q9m88nrlFfOZLjhBdEVW4TqF7sRmsxEbG8vcuXMpKCjgyJEjrFu3jm7dut3ooYmIiNxcKmCn0N1UqKOP3Y3FYmHu3Lm8/PLLdOrUiWrVqtG7d28effTRGz00ERGRm8tNWMSZdlMWhX/729%2BMZUVERLBmzRpjeSIiIiI3wk1ZFIqIiEglo06h0zSDIiIiIqJOoYiIiFQC6hQ6TUWhiIiIuD8VhU7TDIqIiIiIOoUiIiJSCahT6DTNoIiIiIioUygiIiKVgDqFTlNRKCIiIu5PRaHTNIMiIiIiok6hu8rNsxjNs1jAzw/y8i3YbOZyfXzMZcFPbwS9vcHT02w2Z8%2BazSsd7OefQ3Gxudzq1c1lAURFwfbtcOedsGOH0ejnR5v7Y6pXD6ZNg8WL4dgxY7EALB5w1GxgtWrQsCH88AOcO2cuNzLSXBaA18V/AVWqGH%2Bynj5tLsvLC2rWhKwsKCw0lwsQtnev2cDAwJI/1sOHITvbbPaZM%2BaygoOhfn3YtctsLsCtt5rNKy91Cp2mGRQRERERdQpFRESkElCn0GkqCkVERMT9qSh0mmZQRERERNQpFBERkUpAnUKnaQZFRERERJ1CERERqQTUKXSaikIRERFxfyoKnaYZFBERERF1CkVERKQSUKfQaZpBEREREVGnUERERCoBdQqdpqJQRERE3J%2BKQqdpBkVEREREnUIRERGpBNQpdJpmUERERETUKbyWSZMm4evry8yZM2/0UERERORa1Cl0mopCERERcX8qCp2mGRQRERGRm6soPH78OM2aNWPDhg306dOHNm3aMGTIEDIyMgB45513iImJoX379rzwwgsUFxfbb2uz2Xj11Vfp1q0bUVFR3HfffXzzzTcALFq0iAEDBjjc17Zt22jTpg05OTls3ryZfv36ERUVRZcuXZg9e7ZDtoiIiDjJw8P8103mptx8vHr1at588038/PwYP348M2bMYPLkyTz//PMsXLiQrl278sEHH/DSSy8RGxsLwPvvv8%2B6detYu3YtISEhLF68mIkTJ/LFF1/Qv39/FixYQGpqKo0bNwZgw4YNdO/eHV9fXyZNmsSiRYu4/fbbOXLkCI899hhRUVH07NmzXONNT0%2B3F66lqlcPISQk1NicWCyOl6aYfk6V5rnmueomg42KMpvXvLnjpUH16pnLCgtzvDSqWjWzeVWqOF6a4mX4JdvT0/HSIC%2BD73tdOEwIDDSbFxDgeFlRlT5u04//zBmzefKruimLwvj4eMIu/mcZPnw4CQkJtG3blpYtW9oLtQceeICVK1fab9OvXz969OhBtYv/PPr06cOCBQs4ceIE9erVo0OHDnz44YckJCQAkJSUxLPPPkt%2Bfj55eXlUqVIFi8VCw4YN2bhxIx7XUSgkJiaycOFCh2Xjxo1n4sQJTs1DWXx9jUe6hL%2B/K1KruiLU/GC3bzebV%2Brtt41HTjOeCCNHuiCU21wRCm3auCbXtOrVjUfWNJ4IQUEuCO3a1QWhQLt2rsk1zfTjv%2BT/5q/uJuzsmXZTFoWNGjWyfx8eHo7VaiUtLY26des6rNewYUP797m5ufzxj39ky5YtZGdn25dbrVYA%2Bvfvz5IlS0hISCA5OZnz58/TtWtXfHx8GDduHEOGDKFNmzZ07tyZAQMGUKdOnXKPd%2BDAgcTExDgsq149hLy863nU12axlBSE%2Bflgs5nLLSoylwUlz3l/f8jNBdNb4Kty3mygqwZ7553msqCkQ/j22xAfD/v3G43%2B4wPmCtiwsJKC8K9/hVOnjMUCMC3mK7OBVaqUFIS7d8OFC%2BZymzUzlwUlrbfq1eHsWeNP1h%2BLaxjL8vQsKQizssy/ptRM2WI2MCCgpCDcvh1ycsxmX/K/x2mBgSUF4ZYtZnNvJBWFTrspi8LL9xWEkuKusLDwquu98MILHDhwgLfeeosGDRpw7NgxevXqZb/%2Bnnvu4aWXXmLnzp18%2BumnxMbG4uPjA8D48eN58MEHSUpKIikpiTfeeIOVK1fSppxdhNDQUEJDHTcV5%2BaaLd5K2Wxmc12162RxsSuy3WSwO3aYy7rU/v3Gs49FG40DSgrCY8cMh547ZzjwogsXzGZf9hplTFGR8exCFzydXDBM1xVEOTnms12xaTY7W5t8xe6mLKuPHj1q/z4tLQ0/Pz/q1KnDDz/84LBeamqq/fvdu3cTFxdHw4YNsVgspKSkOKwbEBBAjx49%2BN///V/%2B/e9/ExcXZ78uKyuLsLAwHn74YZYvX05sbCzvv/%2B%2Bix6diIjITUgHmjjt5nvEwJo1azh9%2BjRZWVmsXLmSbt260a1bN/bu3cvmzZuxWq289dZbnLpkG1XdunVJTk7GarWyc%2BdO1q9fD5QcBFKqf//%2BvPvuuxQUFNC%2BfXsAduzYwT333MPu3bux2Wz8%2BOOPHD58mPr16/%2B6D1pERKQyU1HotJty83FcXBzDhg3j6NGjREZGMn36dGrWrMlzzz3HjBkzOHv2LP369SM2Nta%2BeXny5Mn87ne/o1OnTrRt25ZZs2YBMHbsWFavXk2rVq3o0qUL/v7%2B9O3bF8vFw3ijoqIYM2YMCQkJnD59mqCgIO655x4efvjhG/b4RURERC53UxaFkZGR9k7fpYYMGcKQIUPKvE3Lli356KOPHJZdfkRwbm4uubm5PPDAAw7LH3nkER555BEnRy0iIiJXdRN29kzTDBqSn5/Piy%2B%2BSJcuXWjQoMGNHo6IiIjcYGlpaYwaNYro6Gi6d%2B9%2BzQ%2BvWLNmDb179yYqKor%2B/fuTlJRkv%2B6ZZ56hZcuWtG7d2v7VoUMH4%2BO9KTuFpm3bto0RI0bQsWNHZs%2BefaOHIyIicvOpgJ3CCRMm0KpVK5KSkvjxxx954oknqFWrFo8%2B%2BqjDehs2bGDOnDksWbKENm3asG7dOhISEvj3v/9NvYufAjBmzBgmTDB/fuJLVbwZdKG6dety4MAB%2B6eOmNKhQwd2797NX//6V4KDg41mi4iISDlUsANNkpOT2b9/P1OmTKFatWo0bNiQ4cOHk5iYeMW6eXl5PPXUU7Rv3x5vb28efPBBqlatys6dO50aw/VSp1BERESkDGV9zGxISMgV5w4uS0pKCuHh4QRe8lGCrVq14vDhw%2BTk5BBwyUch9u/f3%2BG2Z8%2Be5fz58/ZPXwP46quv2LRpE0eOHKFx48bMmDGDiIiIX/rQyqSiUERERNyfCzYfl/Uxs%2BPHjy/XZtysrCyqX/YRkqUFYmZmpkNReCmbzcZzzz1H27Zt6dSpEwD16tXDw8ODJ598kqpVq7Jw4UJGjBjBhg0bqFHD3KcHqSgUERERKUNZHzMbEhJS7tvbrvMjwgoKCnjmmWf47rvvWLVqlX35uHHjHNZ7%2Bumn%2Beijj0hKSuLBBx%2B8rvu4FhWFIiIi4v5c0Cks62Nmyys4OJisrCyHZVlZWVgsljKPP8jLy2Ps2LHk5uby1ltvXbMD6OnpSZ06dRw%2BQMOEm%2BpAExEREamkKtiBJhEREZw8eZIzl3y2dHJyMk2aNKFq1aoO69psNiZNmoSXlxcrVqxwKAhtNhuvvPIK%2B/fvty%2BzWq0cPXrUfmSyKSoKRURERAwrPa/gnDlzyMnJITU1leXLlzN48GAAYmNj2bZtGwAffvgh3333HX/5y1/w9fV1yLFYLBw/fpwXXniBU6dOcf78eV599VW8vb3p2bOn0TFr87GIiIi4vwp4nsL58%2Bfzhz/8gc6dOxMQEMCgQYOIj48H4PDhw1y4cAGA9957j7S0NPuBJaX69%2B/PSy%2B9xMsvv8yf/vQnBgwYQE5ODm3atGHlypVUqVLF6HhVFIqIiIi4QO3atVm2bFmZ1x04cMD%2B/cqVK6%2BZExQUxCuvvGJ0bGVRUSgiIiLurwJ2Ct2NikIRERFxfyoKnaYZFBERERF1Ct2Vv0e%2B2UCLBfDBz8MK13myzWvyc8X7Dm%2Bq%2BhQYT83IqmY0z8sLalSFTGtVCgvN5T4/2uDvB6hXD6YBf3xgO8eijUaz%2BHWLubCoKJi2nWlr28GOHeZygYWtzM5pSAgMvA0Sj9zGZZ%2BQ5ZToIHNZAFWqQKsakHKiBhf3dzemeXNzWaUNID8/KC42lwswe38/o3mhoTCsK6w83BXDp5AjJ8dcVp1cGA28ntaPkyfN5QK8YDau/NQpdJpmUERERETUKRQREZFKQJ1Cp6koFBEREfenotBpmkERERERUadQREREKgF1Cp2mGRQRERERdQpFRESkElCn0GkqCkVERMT9qSh0mmZQRERERNQpFBERkUpAnUKnaQZFRERERJ1CERERqQTUKXSaikIRERFxfyoKnaYZFBERERF1CkVERKQSUKfQaZrBq%2BjduzfvvvvujR6GiIiIyK9CncKr2LBhw40egoiIiJSXOoVOU1EoIiIi7k9FodPcagaPHz9Os2bN2LBhA3369KFNmzYMGTKEjIwMvv76a6KiolixYgXt2rVjx44dAKxevZp77rmHtm3b0qdPH5KSkgCYPXs2Q4cOdcj/%2BOOP6dChA1arlZiYGNasWQNAcXExixYtolevXrRp04b77ruPrVu32m/XrFkztmzZYv95zZo1xMTE2G87c%2BZMunTpQmRkJHFxcXz%2B%2BecunScRERGR6%2BWWncLVq1fz5ptv4ufnx/jx45kxYwaPPPIIBQUFHDlyhC%2B//BJfX182btzIwoULeeONN2jevDmffPIJCQkJbNy4kdjYWJYvX05WVhZBQUFASVHYs2dPfHx8HO7vrbfe4t1332XJkiU0atSI1atXM3bsWJKSkqhZs%2BY1x7p%2B/Xq%2B/PJLPvjgAwIDA1m3bh1Tp07ls88%2Bw9vbu1yPNz09nYyMDIdlIYGBhIaGXses/QyLxfHyJuRl%2BNng6el4aUq9embzwsIcL42KijKX1by546VBISFm82rUcLw0pUoVs3l%2Bfo6XJpls2pRmuaIRZPJlFCA42PHSpGrVzGXVquV4acrJk2bzros6hU5zy6IwPj6esIv/wYYPH05CQgJDhgyhoKCA%2BPh4/C6%2Bwq1du5YHHniAiIgIAO6%2B%2B27at2/PRx99xKhRo6hTpw6ffvop9913H4WFhWzevJnZs2dfcX9r164lPj6eZs2aATBixAjeeOMNNm/ezP3333/NsZ49exYvLy/8/f3x9PTk/vvv57777sPjOv54ExMTWbhwocOy8ePGMWHixHJnlFs5C9UbzgXjNP0PvFT16mbzpk0zm1dq5EgXhE7bbj7z7beNRw40nlji7rtdFGxY48Y3egTl4%2B9vPnPYMPOZAP36uSbXtAceMJs3fbrZPPl1uWVR2KhRI/v34eHhWK1WsrKyALjlllvs1x09epT//Oc/rFy50r7MZrPRpEkTAGJjY0lKSuK%2B%2B%2B7jv//9LxaLhc6dO19xf8ePH6fxZa%2Ba9evXJy0t7WfH2qdPH95//326du1K586dueuuu%2BjTp891FYUDBw60b44uFRIYCFZruTN%2BlsVSUmgVFIDNZjbXtNJxGpaZY7bQ9PQsKQjPnoWiInO5ixeby4KSDuHIkfDXv8KpU2azp61tZy6sefOSgjA%2BHvbvN5cLJE41W7zWqFFSEG7cCJmZ5nIvvr81xs%2BvpCBMTYW8PLPZDRuay/LwKCkIc3OhuNhcLsDatWbzgoNLCsIPP4QzZ8xm5%2Baay6pVq6QgXLsWTp82l3tDqVPoNLcsCosveVWwXSxgLBeLD69LtgH6%2BfkxefJkRowYUWbOPffcw5AhQ8jLy2Pjxo3cfffdDrcvZb1K8WW5SsFz6fiCgoJ455132L59O59%2B%2Binz589nzZo1vPXWW2XeV1lCQ0Ov3FScn2%2B2eCtls1X8otBFCgtdk1tUZDb72DFzWZc6dcoF2Rf37TVq/37juZftnWFMZqbZ7AsXzGVdKi/PfLbp4q0003RuerrZvFJnzpjPzskxmwclBeEN3eRrkopCp7nlDB49etT%2BfVpaGn5%2Bfvb9Ai9Vv359Dhw44LDsxIkT9kIyIiKCWrVq8eWXX5KUlMS9995b5v3Vr1%2BfQ4cO2X8uLCzkyJEj1Lu4c5ePjw95l7zNvnR8%2Bfn55Obm0q5dOyZPnsxHH33EwYMH2W%2B40yEiIiLiDLcsCtesWcPp06fJyspi5cqVdOvWrcyu3cCBA/nXv/7F5s2bKSws5KuvvqJv377s2rXLvk5sbCx//etfsdlsdOrUqcz769%2B/P2%2B//TapqalYrVZef/11ioqK7Jt0GzZsSFJSEoWFhSQnJ7N582b7bV9%2B%2BWWmTp3KmTNnsNlspKSkUFxc7LCZW0RERJzk4WH%2B6ybjlpuP4%2BLiGDZsGEePHiUyMpLp06fz3XffXbFe586dmTp1Ki%2B%2B%2BCKnT5%2Bmbt26zJgxg8jISPs6sbGxLFu2jCFDhuB5lcNER4wYQWZmJo8//jhnz56lRYsWrFq1iuoXjyCYNm0azz//PB06dKBDhw6MGDGCJUuWADB58mSmT59O7969KSwspEGDBsyZM4dgVxyaJiIicrO6CYs409yyKIyMjGT9%2BvUOy2rWrHnFpmKAIUOGMGTIkKtmRURElHm7Tz75xP69l5cXU6dOZerUqWVm3H777Xz88ccOywYOLDmeMTAwkHnz5l39wYiIiIhUAG5ZFIqIiIg4UKfQaZpBEREREXGvTmHdunXL3NQrIiIiNzl1Cp3mVkWhiIiISJlUFDpNMygiIiIi6hSKiIhIJaBOodM0gyIiIiKiTqGIiIhUAuoUOk1FoYiIiLg/FYVO0wyKiIiIiDqFIiIiUgmoU%2Bg0zaCIiIiIC6SlpTFq1Ciio6Pp3r07s2fPpri4uMx1V61aRe/evWnXrh2DBw9mz5499uvy8/N5/vnn6dq1K9HR0UycOJHMzEzj41VRKCIiIu7Pw8P8l5MmTJhAWFgYSUlJLF%2B%2BnKSkJFauXHnFep988gkLFixg1qxZfPnll3Tv3p3Ro0dz4cIFAObOnUtKSgqJiYls2LABm83G73//e6fHdzkVhSIiIuL%2BKlhRmJyczP79%2B5kyZQrVqlWjYcOGDB8%2BnMTExCvWTUxMZMCAAbRt2xY/Pz8ee%2BwxAD799FMKCwtZu3YtY8eOpU6dOgQFBZGQkMDmzZs5deqUU2O8nPYpdFM2H1/jmRbA5u1jNNNqNRqHxQI%2BgNXmjc1mNjvkjOHP1fb1hRoNqZH9PeTnG4tdPOCosSwAqlUDbmNazFdw7pzR6IWtzP2SQkJgIJA4dTsZGcZiARg/wWI2MCoKBm5n4J/awY4d5nJvu81cFkDTprByJa1mDYODB81mL1xoLsvfH1q2pOqRvZCbay4XePq5O4zmERkJw75m2GvRsHOn2eygIHNZrVvD6CRGr%2B0JycnmcgFeMFuo3Ejp6elkXPaCExISQmho6M/eNiUlhfDwcAIDA%2B3LWrVqxeHDh8nJySEgIMBh3Xvvvdf%2Bs4eHBy1atCA5OZkWLVpw7tw5WrVqZb%2B%2BcePG%2BPn5kZKSQlhYmDMP0YGKQhEREXF7Ngy/uaOkg7fwsjc448ePZ8KECT9726ysLKpXr%2B6wrLRAzMzMdCgKs7KyHIrH0nUzMzPJysoCuCKrevXqxvcrVFEoIiIiUoaBAwcSExPjsCwkJKTct7ddxyatn1v3erJ%2BKRWFIiIi4vauclCvU0JDQ8u1qbgswcHB9i5fqaysLCwWC8HBwQ7La9SoUea6t956q33drKwsqlatar8%2BOzubmjVr/qKxXY0ONBERERG3V1xs/ssZERERnDx5kjNnztiXJScn06RJE4firnTdlJQU%2B89FRUXs3buXtm3bUq9ePQIDAx2uP3jwIFarlYiICOcGeRkVhSIiIiKGtWzZktatWzNnzhxycnJITU1l%2BfLlDB48GIDY2Fi2bdsGwODBg1m3bh07d%2B4kNzeXxYsX4%2BPjw1133YWnpycPPfQQr7/%2BOidPniQzM5M///nP9OrVi1q1ahkdszYfi4iIiNtzxeZjZ82fP58//OEPdO7cmYCAAAYNGkR8fDwAhw8ftp%2BHsGvXrjz11FMkJCTw448/0rp1a5YuXYqfnx8AEydO5Pz58/Tv35/CwkK6d%2B/OjBkzjI9XRaGIiIiIC9SuXZtly5aVed2BA46nQYuPj7cXjJfz8fFh%2BvTpTJ8%2B3fgYL6WiUERERNxeRewUuhsVhSIiIuL2VBQ6TweaiIiIiIg6hSIiIuL%2B1Cl0njqFIiIiIqJOoYiIiLg/dQqdp6JQRERE3J6KQudp87GIiIiIqFMoIiIi7k%2BdQuepUygiIiIiKgpNW7t2LWfOnLnRwxAREbmpFBeb/7rZqCg0qKioiJkzZ5KZmXmjhyIiInJTUVHoPKNFYVpaKlfGdAAAIABJREFUGqNHjyY6OpqOHTvyu9/9jpycHL7%2B%2BmvatWvHpk2biImJISoqinnz5pGcnExcXBxRUVGMHz%2BegoICAIYOHcqf//xnEhISiIyMpFu3bnz88cdX3F9xcTFdunThn//8p8PyMWPG8PzzzwPwxRdfMGDAAKKiorjzzjuZP3%2B%2Bfb1//OMf9O3bl5kzZxIZGcmpU6cccp555hmeffZZhg4dSt%2B%2BfQE4evQoI0eOJDo6mujoaJ566inOnj0LQKdOnTh37hz9%2B/dn4cKFAGzdupWBAwfa73/RokWGZltERETEHGMHmthsNsaOHUu7du2YO3cuFy5c4KmnnuJPf/oTffv2JTc3l61bt7J%2B/Xo2bNjAM888w4EDB1ixYgXZ2dnExcXxySef0Lt3bwD%2B/ve/M2vWLGbNmsU777zDpEmT2LJlC8HBwfb79PDw4O677yYpKYn77rsPgAsXLvCf//yHZcuWceHCBSZMmMC0adN44IEHOHjwIIMGDSIiIoKYmBgA0tPT8fX15ZtvvsHb2/uKx7Vp0yZeeeUV7rrrLgCee%2B45wsPD%2Bfzzz8nJyWHkyJG89tprPPPMM7z//vv06NGD999/n8aNG/PDDz8wduxYpk%2BfTr9%2B/fjuu%2B947LHHqF%2B/Pv369Sv33Kanp5ORkeGwrFatEEJDQ6/rd3QjWCyuyTOdC4Cvr9k8Hx/HS1OqVTObV6WK46VBISHmsmrUcLw0KirKbF7z5o6XpjRtajavQQPHS5P8/c1l%2Bfk5XpoUGWk2r1kzx0uTTD73mzRxvDQlOdls3nW4GTt7phkrCpOTk/n2229Zs2YN/v7%2B%2BPv7M2HCBEaOHEmfPn0oLi4mPj4ef39/YmJisNls9O7dm%2BDgYIKDg/nNb37DkSNH7HmRkZH2Qiw%2BPp4FCxbwxRdfEBcX53C/99xzD48//jh5eXn4%2Bfnx%2BeefU716dTp27IiHhwdbtmyhatWqWCwWmjVrRrNmzdizZ4%2B9KDx37hyPP/54mQUhQHh4ON27d7f/vHTpUiz/z969x%2BV8938Af3UupYjKqULIMZ0wNKm4a0K2uwmzOZtDDmEZM6fN2E1kyGHsjg13Nm4shxEzP4zdxqY5jkJFp3U%2Bn76/Py5d69IV5fpcu7rm9Xw8PK76fq/rdb27dHhf7%2B9JRweGhoawtLTEq6%2B%2BiitXrih9bHR0NNq3b49hw4YBABwdHTFixAgcOnSoTk1hVFSUfPJYafr0YMycOaPWGbUlutkS3Q9VquG/SzWtW6shFECLFmLz1FWnk5PwyKBXhEfiH/8Qn4kg5T/DKtuzRz25oi1frukKaqdtW/GZly6JzwSAXbvUkyvali1i82xsxObRX0pYU5iQkIDy8nL06tVLYXl5ebl8H7vmzZsDAIyeTGRsqnzzGBkZobi4WP55mzZt5B/r6uqiefPmSE1Nrfa8bm5uMDMzw7lz5zBgwACcPHkSfn5%2B0NWVbRk/duwYIiMjkZSUhIqKCpSWlsLd3V3%2BeHNzc5iZmdX4dbVs2VLh899%2B%2Bw1hYWG4ffs2SktLUV5ejq5duyp97MOHDxEbG4tu3brJl0mSpPC11UZQUJC8ia3UtKkVJKlOMc%2BlowPhmU/2CBBGR0fWEJaWiq/V8NF9wYGGsobw0SOgpERcbnKyuCxANiF0cgKuXQMKCoRGRz0Q1xU2bixrCE%2BcAETvthv0qavYwI4dZQ3hqFHArVvicqv8LhHC3l7WEC5eDFR5Uy7E/PnisoyNZQ1hXBxQVCQuFwDGjROb5%2BgoawjfeQe4fVtstuhJ4ZYtwJQpwN274nI1iJNC1QlrCo2MjNCgQQNcvXq12rpLT96JVTZqlZ7%2BvKry8nKFzyVJgo6SMZauri58fX1x6tQpeHp64syZM9i2bRsA2f58S5cuxZo1azBw4EAYGBhg1KhRCo/X13/2S6Cnpyf/ODs7G5MnT8bIkSPx%2Beefw8zMDOHh4bhw4YLSxxobG8PT0xNbVHwnZm1tXW1TseiGSF3UVackqSG7ypsSoUpKxGbn5orLqqqgQHj2U3s9CJGZqYZcJb%2B3hLh1S2y26F0cKj14ANy5IzazsFBsHiBrCEXn/vKL2LxKt2%2BLz27USGweIGsINbjJVyQ2haoTdqCJnZ0dCgoKkJCQIF%2BWl5f3wkfiVs2pqKhAcnIymjVrpvS%2Bfn5%2BOHPmDC5cuICGDRvC5cn%2BQdeuXUObNm0waNAgGBgYoLi4GPfu3XuhegAgLi4O%2Bfn5mDBhgny6eOPGjRrvb2dnhzt37kCq0r2kpaWhROTUiIiIiEgAYU1hhw4d4OLighUrViAjIwM5OTlYsmQJQkNDXyjv6tWruHDhAkpKSvDVV18hPz8fffv2BQCEhYVh1apV8vu6ublBT08P27Ztg5%2Bfn3yi2LJlSyQnJ%2BPx48dIT0/H0qVLYW1tXe0o40opKSnw8/NTaEiratGiBXR1dXH16lUUFBQgMjIS6enpSE9PR1lZGYyf7AR9//595OXlwd/fH1lZWYiIiEBRURESEhIwfvx47Ny584VeEyIiIlKOp6RRndBT0oSFhUGSJPj4%2BGDgwIHy8/a9iKFDhyIqKgo9e/bE9u3bsX79ejR6MjpPS0tTaOwqNyFfvnwZ/v7%2B8uW%2Bvr7o168fBg0ahKCgIPTv3x9Tp05FTEwMVq9eXe05S0tLER8fX%2BMkz8bGBnPmzMHChQvh5eWF7OxsrFmzBiUlJRg1ahSaNm0KX19fzJo1C%2BHh4WjcuDEiIiJw6tQp9OjRA6NHj4aXlxfGjx//Qq8JERERkbroSFL92zvt7bffRvfu3TFv3jxNl1JvqeN/TR0HmojeUq6jIzt%2Bo6REfK1G9wXvFG5kJDtS%2BP59sfsUPnwoLguQ7bz%2ByivAxYvC9ynceHugsCwrKyAoCIiKEr9PYfAMwYfdu7gAV64Arq5i9yl8RfDh3B06ADt3AmPGiN%2Bn8KkzJqjExATo3Bm4cUP8PoV9%2BojNc3aWHdHcq1f93qewWzcgJgYYMED8PoU1bI1Tt/h48Zl1PC5U6wk70ISIiIhIU17Gzb2i8TJ3RERERFQ/J4VffvmlpksgIiIiLcJJoerqZVNIREREVBdsClXHzcdERERExEkhERERaT9OClXHSSERERERcVJIRERE2o%2BTQtWxKSQiIiKtx6ZQddx8TEREREScFBIREZH246RQdZwUEhEREREnhURERKT9OClUHZtCLaUDSS2ponONDIXGPaEDQwM1fP2Ggos1MPjzVhJYr7OzuCwA0H/ya8DRESgrExrdq5G4rAYNZLdduwIFBeJyAQCvvCI2r0MH2W23boCRkbjcixfFZQFAcbHsNjYWuHpVbHa7duKydJ9s1LK1Ff%2BX389PbF7btrLbPn2AZs3EZnfpIi6rRQvZ7bBhQM%2Be4nI1iE2h6rj5mIiIiIg4KSQiIiLtx0mh6jgpJCIiIiJOComIiEj7cVKoOjaFREREpPXYFKqOm4%2BJiIiIiJNCIiIi0n6cFKqOk0IiIiIiDcjKysLs2bPRp08feHh44IMPPkBRUVGN9z9x4gSGDh0KFxcX%2BPr6Yt%2B%2BffJ1GzZsQKdOndCtWzeFf%2Bnp6bWuh5NCIiIi0nraOCn88MMPUVJSgujoaJSWlmLWrFlYs2YNFi1aVO2%2B165dw7x587B27Vr0798f58%2Bfx/Tp09G2bVu4u7sDAAICArBq1aoXrodNIREREWk9dTSFqampSEtLU1hmZWUFa2trlbPT09MRExOD//73v7C0tAQATJs2DbNmzcL8%2BfNhUHlVrCeysrLw7rvvYsCAAQAAT09PdOjQAZcvX5Y3hapiU0hERESkRFRUFDZu3KiwLDg4GDNmzFA5%2B%2BbNm9DT04Ojo6N8WZcuXVBQUIC4uDiF5QDQr18/9OvXT/55WVkZ0tLSYGNjI192%2B/ZtjBgxAnfu3EHz5s2xYMECeHh41LomNoVERESk9dQxKQwKCoK3t7fCMisrKyHZWVlZMDMzg46OjnyZhYUFACAzM/O5j1%2BzZg0aNGiAQYMGAQCaNWsGW1tbzJ07F9bW1oiKisKUKVNw%2BPBhtK28JvdzsCkkIiIiUsLa2lqlTcWHDh1CaGio0nUhISGQJKnOmZIkYc2aNYiOjsauXbtgZGQEAHjzzTfx5ptvyu83duxYHDlyBIcPH8bs2bNrlc2mkIiIiLRefTzQJCAgAAEBAUrXnT9/Hnl5eSgvL4eenh4A2fQQAJo0aaL0MRUVFViwYAGuXbuGvXv3wtbW9pnP37JlS6Smpta6Xp6ShoiIiLReRYX4f%2BrUqVMnSJKEW7duyZfFxsbC3Nwcbdq0UfqYTz75BL///rvShjAiIgI//vijwrJ79%2B49t3Gsik0hERER0V/M0tISvr6%2BCA8PR0ZGBpKTk7Fp0yYEBgZCX1%2B2IXfMmDE4evQoAODnn3/G4cOHsW3bNjRq1KhaXlZWFpYtW4a4uDgUFxfjiy%2B%2BwMOHD/H666/Xuqa/fVMYERGB0aNHC8/19fXF119/rXRdcXExHB0dcenSJeHPS0RERNVp26QQAJYvX46GDRvCx8cHQ4cOhZOTE0JCQuTrExISkJ2dDQDYv38/cnNz4eXlpXBy6vHjxwMA5s6di379%2BmHs2LHo0aMHoqOjERkZiWbNmtW6nr/9PoXTpk3DtGnThOd%2B9913wjOJiIjo5dGwYUOsXbu2xvWnT5%2BWf/zJJ5/gk08%2BqfG%2BRkZGWLhwIRYuXPjC9fztm0IiIiL6%2B6uPB5pomzpvPk5ISMD48ePh4uICLy8v7Nq1CwCQnJyMqVOnolevXnBzc0NISIj8KJpLly7Bzc0NZ8%2BehZ%2BfH5ydnTFhwgT5SDQ%2BPh5jx46Fu7s7evTogeDg4BrP0ePo6IgDBw4gMDAQTk5OGDZsGOLi4gAAiYmJcHR0xJ49e9CzZ09ER0djw4YNGD58uPzx586dw9ChQ%2BHs7IyAgACFnTJ//PFHBAUFwcXFBa%2B%2B%2Bio2bdpU4%2Bvg7e2NvXv3AgAKCgowZ84cuLu7Y8CAAQqd/blz5%2BDm5iY/I3pxcTF8fHwQGRlZ15eeiIiIaqCNm4/rmzpPCoODg9GzZ09s2rQJ9%2B/fx1tvvQUHBweEhYWhXbt2OHXqFIqKijBr1iwsWbIE69evBwAUFhbiyJEjiIqKQmFhIQIDA7Fv3z5MmjQJH330EVxdXbF9%2B3bk5%2Bdj/vz52Lx5c40j0H//%2B98IDw9Hs2bNsGTJEsyZMwcHDx6Ur//pp59w%2BvRpmJqaKpyJPCUlBTNmzMCKFSswcOBAfPvtt5g%2BfTpOnz6NoqIiTJs2DUuWLMGQIUNw9%2B5dTJw4EXZ2dhgyZMgzX5MtW7bg1q1bOHLkCIyMjLBkyRL5Og8PD3h7e2PNmjX49NNPsWPHDjRp0gTvvPNOrV9zpZfZadpUyGV2qApDQ7F5lZcoeupSRSrTFzzgf3IqBPmtQA0aiMsyNla8FapDB7F59vaKt6IUF4vN69hR8VYkXYG7rFdmicysVMuT%2BtZaq1aKtyK1aCEuq/IEzIJOxCz36JHYPPpL1emvy40bN3D79m3s3LkTJiYm6NSpEzZu3AgTExNcv34dW7duhZmZGczMzDB58mRMnz4dJSUlAIDy8nJMnDgRFhYWsLCwgJubm3zCl5OTA2NjY%2Bjr68PCwgIRERHQfcYPf0BAABwcHAAAEydOREBAAFJSUuTrhw0bBjMzs2qPO3bsGGxtbeVn/37jjTdgZGSEiooKREdHo3379hg2bBgA2URyxIgROHTo0HObwpMnT2LUqFHyS81MmjQJx48fl69fsGAB/P39cezYMfz73//G7t27n/n1PU3pZXamT8eMmTNrnVFrVc6sXq%2Bpo86WLcVnAoC2NO/m5sIjuzQWHoknP/pi7dyphlAAy5erJ1e0PXs0XUHtmJqKz1y3TnwmAMydq55c0YKCxOapsD%2Bbql7GyZ5odWoKHz58CDMzM4VDofv06YOTJ0/CwsJC4dIvdnZ2KC0tVWjWWlV552RiYoKioiIAsunje%2B%2B9h4MHD8LDwwODBw%2BGk5NTjXVUPX9Pyyd/yFNSUuQXlG5Rw7uphw8fKtQAAP7%2B/vJ1sbGx6Natm3ydJEk1niuoquTkZIXc1q1bK6y3tLTE/PnzERISgnfffRcd6jiVUHqZnaZNgRc4E/oz6eiIz1QHddUp%2Bh2ugYGsIUxNBUpLxeWKHL8BsgmhuTmQkwOUlwuNvv5IXFdobCxrCO/dA5786hCmy7/GiA20t5c1hIsXAw8eiMuNjRWXBcgmhHv2AKNGAVXOlSbEDz%2BIy9LVlTWE%2Bfni//IvXiw2r1UrWUMYFgYkJorNFvmOyMpK1hBGRQFPbYmil1edmkJdXV1UKPmBrJwGKlP1mn41Tcf69%2B%2BPM2fO4IcffsCpU6cwevRohIaG1ngqmao1VF4ipurz6NWwGaym%2BgHA2NgYnp6e2LJlS41fS01KS0tRXuWPqbLL1iQmJsLExAT379%2Bvc77Sy%2BxoQ/OmbZ7xfayS0lKx2aI3c1cqLwfKyoRGFhQIjQMgawiF5965IzjwiQcPxGZfvSouq6pbt8Rnq2Nso44dvZ5ssRIuMVF8tomJ2DxA1hD%2BTTb5clKoujrtoGFra4v8/HyFS6bExMTAysoK2dnZSE9Ply%2BPi4uDkZGRfJPqs2RmZsLU1BSDBg1CWFgYli1bhqioqBrv//DhQ/nHj558M9fmPDytWrVCfHy8wrKvvvoKCQkJsLOzw507dxQaurS0tGc2vJWsra3x%2BPFj%2Bed3795VWH/v3j1ERkZiz549%2BPnnnxETE/PcTCIiIqo9Hmiiujo1hZ06dULnzp0RHh6O/Px83LlzBx988AEKCwvlB5sUFBQgJSUFmzdvhr%2B/Pwyes5N9UVERfH19cejQIZSVlaGoqAjXr1%2BHnZ0dgD/316vq0KFDePDgAfLz8/H555%2Bja9euCpuuazJ48GA8fvwY%2B/btQ0lJCY4cOYK1a9fC1NQU/v7%2ByMrKQkREBIqKiuRHWe98sq/RtWvX4Ofnp7RJfPXVV7Fv3z6kpaUhIyMD27dvl6%2BTJAmLFy/G2LFj0alTJ4SGhmLZsmXIzc19br1EREREf5U6H8q1ZcsWJCUloU%2BfPpgyZQqmTZsGT09PREREIDU1Ff3798fw4cPRvXt3LK7FvhrGxsZYv349IiMj4e7ujv79%2ByM5OVn%2B2NzcXDx4ap%2BcwMBAzJ07F71798bdu3cRFhZWq9qbNm2KHTt2IDIyEj169MC2bduwadMmWFpaonHjxoiIiMCpU6fQo0cPjB49Gl5eXvIzhRcWFiI%2BPl7ppuH33nsPbdq0gZ%2BfHwIDA/H666/LL1ETFRWFlJQUTJo0CQAwdOhQtG7dGqtXr65VzURERPR8nBSqTkdS1uXUY46Ojvj888/Rr18/TZeiWer4b3vZDzR5gf09n8nQUHZEc1KS2H0KlRxZrxJ9faBxYyAzU/g%2Bhf%2B7L%2B50Fw0aAF26ANevi9%2BnsMfM3mIDO3SQHdE8ZozYfQovXhSXBQAuLsCVK4Crq/h9Cp%2Bcp1YIXV2gYUMgN1f8X%2Bo6nB6sVtq2lR3RHBIifp/CLl3EZbVoAQQHAxs3it%2Bn8BlX3VCn/fvFZ/7zn%2BIz6zNe0YSIiIi03ss42RONTSERERFpPTaFqtO6pvD27duaLoGIiIjob0frmkIiIiKip3FSqDo2hURERKT12BSqTg1XFyciIiIibcNJIREREWk9TgpVx0khEREREXFSSERERNqPk0LVsSkkIiIircemUHXcfExEREREnBQSERGR9uOkUHWcFBIRERERJ4VERESk/TgpVJ2OJEmSpougFxAfLzbP0BBo2RJISgJKSoTFltu1EZZVSU8PKC8XHqsW6qg1PV1snr4%2B0KQJ8McfQFmZ2OwGDcRl6eoCpqZAfr74X/4N7/wsNtDEBOjcGbhxAygsFJfbrp24LED2ojZsCOTmin9RGzUSl%2BXiAly5Ari6AlevissFgOPHxeY1bAj06QNcuCB7XUVychKXpa8PWFkBaWnif/CbNxebV0tbt4rPfPdd8Zn1GTcfExERERE3HxMREZH24%2BZj1XFSSEREREScFBIREZH246RQdWwKiYiISOuxKVQdNx8TERERESeFREREpP04KVQdJ4VERERExEkhERERaT9OClXHppCIiIi0HptC1XHzMRERERFxUkhERETaj5NC1XFSSEREREScFBIREZH246RQdWwKiYiISOuxKVQdNx8/w4oVK%2BDi4oJt27ZVW%2Bfr64uvv/5a6eOKi4vh6OiIS5cuqbtEIiIiIiE4KaxBVlYWdu3ahc2bN8Pb27va%2Bu%2B%2B%2B04DVREREZEy2jgpzMrKwtKlS/HTTz9BV1cXnp6e%2BPDDD2FsbFztvgcOHMDChQthYGCgsHz37t1wcnJCRUUF1q9fj%2BjoaOTk5MDJyQlLly6Fra1trevhpLAG%2Bfn5AAB7e3sNV0JERER/Rx9%2B%2BCEKCwsRHR2N/fv34969e1izZk2N9%2B/RowdiY2MV/jk5OQGQNYfffvsttm3bhu%2B//x6tW7fG9OnTIUlSretRuSlMSEjA%2BPHj4eLiAi8vL%2BzatQsAkJycjKlTp6JXr15wc3NDSEgIsrKyAACXLl2Cm5sbzp49Cz8/Pzg7O2PChAnIzs4GAMTHx2Ps2LFwd3dHjx49EBwcjMzMTKXPn5GRgZkzZ8LNzQ0eHh5Yu3at/AXIzs5GaGgoPDw84OLigsmTJyMxMREAkJiYCEdHR5w/fx7Dhg2Ds7MzRowYgcTERMTHx8PX1xcAEBAQgIiIiGrP6%2B3tjb179wIACgoKMGfOHLi7u2PAgAE4ffq0/H7nzp2Dm5sb0tLSAMg2Lfv4%2BCAyMlLVl56IiIieqKgQ/0%2Bd0tPTERMTg5CQEFhaWsLGxgbTpk3D/v37UVpaWue8qKgojB07Fg4ODjAzM0NISAju3buHX3/9tdYZKm8%2BDg4ORs%2BePbFp0ybcv38fb731FhwcHBAWFoZ27drh1KlTKCoqwqxZs7BkyRKsX78eAFBYWIgjR44gKioKhYWFCAwMxL59%2BzBp0iR89NFHcHV1xfbt25Gfn4/58%2Bdj8%2BbNWLhwYbXnX7RoEfT19fHDDz8gKysLo0ePRqtWrTB8%2BHAsWrQIeXl5OHz4MAwNDbFw4ULMnj0b33zzjfzxu3btwtatW2FkZIR33nkH27dvx9KlS3H8%2BHH4%2BPjg0KFDcHBweOZrsGXLFty6dQtHjhyBkZERlixZIl/n4eEBb29vrFmzBp9%2B%2Bil27NiBJk2a4J133qn1a5yamipvKitZlZbC2sqq1hnPVTmOfmosTfWPvuCdPvT0FG9F0hW4LaIyS2SmnImJ2LzKTT9KNgGpRPQXr84X1cVFXFbHjoq3IjVsKDbP1FTxViSRP/yVWaJ/oZSVic2rA3U0cUr//lpZwdraWuXsmzdvQk9PD46OjvJlXbp0QUFBAeLi4hSWV3r8%2BDHGjRuH3377Debm5pg5cyYCAgJQVFSEu3fvonPnzvL7mpmZwd7eHrGxsXB2dq5VTSp9N9y4cQO3b9/Gzp07YWJigk6dOmHjxo0wMTHB9evXsXXrVpiZmcHMzAyTJ0/G9OnTUVJSAgAoLy/HxIkTYWFhAQsLC7i5uSEuLg4AkJOTA2NjY%2Bjr68PCwgIRERHQVfJLKzMzE99//z32798vf55169ZBX18fWVlZOHnyJKKiomBpaQkAmDlzJvz9/ZGQkAAdHR0AwMiRI2FjYwNA1sDFxsbW%2BXU4efIkRo0aJc%2BZNGkSjh8/Ll%2B/YMEC%2BPv749ixY/j3v/%2BN3bt3K/16ahIVFYWNGzcqLAuePh0zZs6sc63PJeAbvSo19BmyXHUFq4HoWps0EZtXqVEj9eSKJrp/AwBU%2BUUqVNu26skVTR0NzJUr4jP37BGfqS7du2u6gtpp3Fhs3uPHYvM0TOnf3%2BBgzJgxQ%2BXsrKwsmJmZyfsRALCwsAAApVtHLS0t0bp1a8yZMwft2rXDyZMnERoaCmtra7Rt2xaSJMkfXzWvpi2tyqjUFD58%2BBBmZmZoVOWvSZ8%2BfXDy5ElYWFjAqsoky87ODqWlpUhJSZEva9WqlfxjExMTFBUVAZC94O%2B99x4OHjwIDw8PDB48WL7NvKrExERUVFQo5Lg8eXd648YNSJKkMOWzs7MDACQlJckf83QNxcXFdX4dkpOTFXJat26tsN7S0hLz589HSEgI3n33XXTo0KFO%2BUFBQdUOdrEqLQWSkupca40MDGQNYWoq8AJj65qUN2spLKuSnh5QXi48Vi3UUeuTvTCE0dOTNYRZWeJrFTko09WVNYSFheInAqYPbogNNDaWNYRxccCT32tC1GGH8VrR1ZU1hPn54l9UT09xWR07yhrCUaOAW7fE5QLAU3/wVWZqKmsIf/1V9rqK1L69uCx9fVlDmJmp0emeSOqYFCr9%2B1uHrXSHDh1CaGio0nUhISF12t%2Bvf//%2B6N%2B/v/xzf39/nDx5EgcOHMC8efMAoE55yqjUFOrq6qJCyf9C5TRQmaodcU3Tsv79%2B%2BPMmTP44YcfcOrUKYwePRqhoaEYPXp0tecHoFINVT9%2BUaWlpSiv8tdU2X9KYmIiTExMcP/%2B/TrnW1tbVx9Vx8cDz/gaX1hpqXpySRh1/f4uLxefrY5f0mrZ16ewUHDgE0VFYrPVtZOTOl7Uq1fF5gGyhlB0bm6u2LxK%2Bfnis9Xxw19W9rdpCtVB6d/fOggICEBAQIDSdefPn0deXh7Ky8uh92STUuWxF01quUmoZcuW%2BO2339CoUSPo6urKH18pKyur1lmAigea2NraIj8/H6mpqfJlMTExsLKyQnZ2NtLT0%2BXL4%2BLiYGRkJN/E%2BiyZmZkwNTXFoEGDEBYWhmXLliEqKqra/Vq2bAldXV3Ex8fLl128eBGnT5%2BWH4JduUm66seVE0NRrK2t8bjKyPzu3bsK6%2B/du4fIyEjs2bMHP//8M2JiYoQ%2BPxER0ctO2w406dSpEyRJwq0q0%2B/00HukAAAgAElEQVTY2FiYm5ujTZs21e6/d%2B9eHD16VGHZvXv3YGtrCyMjI7Rv3x7Xr1%2BXr8vJycHDhw%2BVbmmtiUpNYadOndC5c2eEh4cjPz8fd%2B7cwQcffIDCwkL5wSYFBQVISUnB5s2b4e/vX%2B38Ok8rKiqCr68vDh06hLKyMhQVFeH69evyRq5y/z0AaNSoEXx8fLBp0yZkZWXh0aNH%2BPDDD5GSkoImTZrAw8MD69evR1ZWFrKzsxEeHo5evXqhefPmdf5ar127Bj8/P6UTyFdffRX79u1DWloaMjIysH37dvk6SZKwePFijB07Fp06dUJoaCiWLVuGXHW9OyUiInoJaVtTaGlpCV9fX4SHhyMjIwPJycnYtGkTAgMDof/kAKAxY8bIG8GSkhJ89NFHiI2NRWlpKaKjo3H27FmMGDECgOwYiV27duHevXvIy8vDmjVr0KlTJ3Tr1q3WNal8yNmWLVuQlJSEPn36YMqUKZg2bRo8PT0RERGB1NRU9O/fH8OHD0f37t2xePHi5%2BYZGxtj/fr1iIyMhLu7O/r374/k5GT5Y3Nzc/HgwQP5/VeuXIkGDRrAy8sLQUFB8PPzQ1BQEADg008/RYMGDfDaa69h0KBBMDMzkx/9XFeFhYWIj49Xumn4vffeQ5s2beDn54fAwEC8/vrr8v/QqKgopKSkYNKkSQCAoUOHonXr1li9evUL1UFERER/D8uXL0fDhg3h4%2BODoUOHwsnJCSEhIfL1CQkJ8tP1vfPOO3j77bcxa9YsuLq6YtOmTdi0aRO6du0KABgxYgRef/11vP322%2Bjbty%2BSk5OrHSTzPDqSqnslkmZU2WQuhKEh0LKl7OAVgfsUlttVH4Gr6mU/0KTKXhlC6OvLjmj%2B4w/xuxY1aCAuS53HRDS887PYQBMT2RHNN26I3aewXTtxWYDsRW3YULbvm%2BgXVeTh7C4usqOZXV3F71NY5UwRQjRsCPTpA1y4IH6fwjpsBnwufX3AygpISxP/g/8CW%2BNEWLBAfObKleIz6zNe0YSIiIiIeO1jIiIi0n7aeO3j%2BoZNIREREWk9NoWq4%2BZjIiIiIuKkkIiIiLQfJ4WqY1NIREREWo9Noeq4%2BZiIiIiIOCkkIiIi7cdJoeo4KSQiIiIiTgqJiIhI%2B3FSqDo2hURERKT12BSqjpuPiYiIiIiTQiIiItJ%2BnBSqjpNCIiIiIuKkUGvZ2aknt1kzoXElJULjoKMD6OkBpaWAJInNNnl0T2ygoSFgawu9RwlCXwibGzeEZQEALCyAfv3Q5PpZIDtbaPTqW0OEZVlbA2PGAN98A6SmCosFALy3qI/YQGdn4NIlYNw44JdfxOX6%2BYnLAoC2bYF164DFi4G4OLHZx4%2BLy2rYUHa7cSOQmysuFxD/mrq4AFeuAMHBwNWrYrPNzcVlde8OnD0L/POfwK%2B/issFhP8eqS1OClXHppCIiIi0HptC1XHzMRERERFxUkhERETaj5NC1XFSSEREREScFBIREZH246RQdWwKiYiISOuxKVQdNx8TERERESeFREREpP04KVQdJ4VERERExEkhERERaT9OClXHppCIiIi0HptC1XHzMRERERFxUkhERETaj5NC1XFSSEREREScFBIREZH246RQdWwKiYiISOuxKVTd33Lzsa%2BvL77%2B%2BmsAwNtvv401a9ZouCIiIiKi%2Bu1vOSn87rvv1Jq/e/du7Nq1CykpKbCyssKIESMwYcIEtT4nERER1YyTQtX9LZtCdYqJicFnn32Gzz//HF27dsWVK1cwfvx42NvbY8CAAZouj4iIiOiF1JvNx0lJSZgyZQp69eqFHj16IDQ0FHl5eThw4AD69u2rcN/hw4djw4YNAIANGzbg3XffxezZs%2BHq6goA8Pb2xt69e%2BX3Lyoqwty5c%2BHi4oKBAwfi%2BPHj8nXZ2dkIDQ2Fh4cHXFxcMHnyZCQmJgIAEhMT4ejoiD179qBnz56Ijo6GtbU11q1bBycnJ%2Bjq6sLd3R0ODg74/fffAQDvv/8%2Bli1bhsWLF8PFxQU%2BPj64cuUKtm3bht69e6N37944cOCAWl9LIiKil01Fhfh/L5t6MSmUJAnTpk2Dq6sr1q1bh4KCAsyZMweffvopXFxcnvv4X375BbNmzUJYWJjS9YcOHcKqVauwcuVK7N27F/PmzYOLiwtsbGywaNEi5OXl4fDhwzA0NMTChQsxe/ZsfPPNN/LH//TTTzh9%2BjRMTU2ho6MjX15aWoqYmBgkJCTAy8tLvvzo0aNYtWoVPvjgAwQHB2POnDl488038cMPP2D79u345JNPMGzYMOjq1q4nT01NRVpamsIyqyZNYG1lVavHa1KVl0tonuhcAIChodg8AwPFW1EsLMTmmZkp3gpkbS0uy9JS8VYoZ2exeY6OireitG0rNq9VK8VbkRo2FJdlaqp4K1It/sbUSceOirciifwZ7dBB8VaUX38Vm1cHL2MTJ1q9aApjY2Px%2B%2B%2B/Y%2B/evTAxMYGJiQlmzJiBCRMmwLkWv6z19PQwcuRIhYatKicnJ/j4%2BAAARo8ejYiICFy4cAFeXl44efIkoqKiYPnkL83MmTPh7%2B%2BPhIQEed6wYcNg9tQPY0REBDZs2IBGjRph1apV6FjlF0Dr1q3lTWLfvn1x6dIlTJo0CYaGhvDy8sL69evxxx9/wKqWTV1UVBQ2btyosCx4%2BnTMmDmzVo%2BvEz09oXHGYuPkjIzUEGprq4ZQAM2aic1TV51PJu0ijeknPBJDhojPxJhLaggFsGuXenJFmztX0xXUTvfu4jOvXBGfCQB79qgnV7Tt28XmiX7TSn%2BpetEUJiQkoLy8HL169VJYXl5ejszMzOc%2BvlmzZjU2hADQrl07%2Bcd6enpo2bIlUlJS8OjRI0iSBAcHB/l6Ozs7ALLN2a2evHtu0aJFtcxp06Zh4sSJOHfuHBYsWAADAwN4enrK66lkZGQES0tLGD6ZQlXeFhcXP/frqhQUFARvb2%2BFZVZNmgDl5bXOqBU9PeGZRaViu0IdHVlDWFwMSJLQaBinJYgNNDCQNYTJyUBpqbjc%2BHhxWYBs%2BuDqKvvjmJcnNHpnvLiu0NJS1hB%2B%2By2QkSEsFgAwJqLX8%2B9UF46OsobwnXeA27fF5fbpIy4LkE0I584FwsKAJ7vNCPPmm%2BKyTE1lDeGvvwL5%2BeJyASA4WGxex46yhnDUKODWLbHZoieF27cDEycCd%2B6Iy9UgTgpVVy%2BaQiMjIzRo0ABXr16ttk7Z/nflTzUu%2BvrP/jKUbaY1MjJCSUlJjY%2Bp2mTq1TA9MzQ0hLe3N3x9fbFnzx55U/j089V2M3FNrK2tYf30djjRDaGaiG7cquYKz37G94NKSkvFZmdni8uqKi9PeHZqqtA4ALKGUHjuL78IDnzi9m2x2aKnzpUSE4G4OLGZubli8wBZQyg6V8nfHSFu3RKfbW4uNg%2BQNYQa3ORL9Uu9ONDEzs4OBQUFSEj4c1KTl5eHzMxMGBkZobCwUL68vLwcSUlJdcqPrzJZqXy8jY0NbJ9shour8suw8uPKieHTli5dWu28hzo6Os9tTImIiEh9eKCJ6upFU9ihQwe4uLhgxYoVyMjIQE5ODpYsWYLQ0FDY29sjPz8f586dQ0lJCbZu3QqpjiOiK1eu4Pz58ygtLcV//vMfFBUVwcPDA02aNIGHhwfWr1%2BPrKwsZGdnIzw8HL169ULz5s2VZvXs2RN79uzBpUuXUF5ejitXruDIkSMKB5oQERHRX4tNoerqzXgrLCwMy5cvh4%2BPDwwNDdG7d2%2BsWrUKTZo0wdixYxESEgI9PT2MHz%2B%2BVkckVzV8%2BHDs27cPwcHB8lPKmD8Zw3/66adYtmwZXnvtNejq6qJ3795YuXJljVmDBg1CdnY2FixYgPT0dDRr1gxTpkxBYGCgSl8/ERERkSbVm6awZcuW2Lp1q9J1CxYswIIFC5SumzFjBmbMmKGw7PTp0/KPv/zyy2c%2Bb9OmTeXnPHxaq1atcFvJTuIjR47EyJEjlT5m1apVz7yvg4OD0kwiIiJ6cdo42cvKysLSpUvx008/QVdXF56envjwww9hbGxc7b6LFi3CoUOHFJaVl5cjICAAK1euxPvvv4/Dhw8rHAdhZGSEy5cv17qeerH5mIiIiOhl8%2BGHH6KwsBDR0dHYv38/7t27V%2B24hUoff/wxYmNj5f%2BuXr2Ktm3bws/PT36fqVOnKtynLg0hwKaQiIiI/ga0bZ/C9PR0xMTEICQkBJaWlrCxscG0adOwf/9%2BlNbiNGY7d%2B5EixYt5Gc%2BEaHebD4mIiIielHqaOKUXlHMyqr6aeJewM2bN6GnpwfHKlc%2B6tKlCwoKChAXF6ew/Gk5OTnYsmUL9jx1kvSLFy/i1KlTePDgARwcHLB06VJ07dq11jWxKSQiIiJSQukVxYKDqx3L8CKysrJgZmamcF5kiydXhHnehTu%2B%2Buor9OjRA%2B3bt5cvs7W1ha6uLmbNmgVTU1Ns3LgR48ePx3fffYfGjRvXqiY2hURERKT11DEpVHpFsVpeohYADh06hNDQUKXrQkJC6nyKPUB2cMnu3bsRFhamsHz69OkKn7/33nuIjo5GTEwM3qzlFYbYFBIREREpofSKYnUQEBCAgIAApevOnz%2BPvLw8lJeXy48YzsrKAgA0adKkxsz//e9/KCkpgbu7%2BzOfW09PD82bN0dqHS4BxQNNiIiISOtp24EmnTp1giRJuFXlGtmxsbEwNzdHmzZtanzcqVOn8MorryhcSU2SJKxcuVIhq6SkBA8fPpRfva022BQSERGR1tO2ptDS0hK%2Bvr4IDw9HRkYGkpOTsWnTJgQGBsobvjFjxuDo0aMKj7t58yZatWqlsExHRweJiYlYtmwZUlJSkJ%2BfjzVr1sDAwAADBgyodU1sComIiIg0YPny5WjYsCF8fHwwdOhQODk5ISQkRL4%2BISEB2dnZCo9JS0tD06ZNq2WtWLECrVu3xhtvvIE%2Bffrg5s2b2LlzJxo0aFDrerhPIREREWk9bbyiScOGDbF27doa11e9Qlul7777Tul9GzVq9MzL9NYGm0IiIiLSetrYFNY33HxMRERERJwUEhERkfbjpFB1nBQSERERESeF2qq0Qk94poGe%2BNzycqFx0H3yNkYtpwsQHVh5pnpJEpudkSEuq6rsbOHZeXnisho2lN0WForNBQA0aiQ2r7LYhg3FZnfpIi4LAFq0kN06OAAmJmKznZzEZVWej619e6CsTFwuAJibi80zM/vzVnR2To64rMoforw8sbkaxEmh6tgUEhERkdZjU6g6bj4mIiIiIk4KiYiISPtxUqg6TgqJiIiIiJNCIiIi0n6cFKqOTSERERFpPTaFquPmYyIiIiLipJCIiIi0HyeFquOkkIiIiIg4KSQiIiLtx0mh6tgUEhERkdZjU6g6bj4mIiIiIk4KiYiISPtxUqg6TgqJiIiIiJNCIiIi0n6cFKrupZ0UbtiwAcOHD//Ln/fevXtwdHREYmLiX/7cREREf1cVFeL/vWxe2qaQiIiIiP7EzcdERESk9V7GyZ5owieFjo6OOHHiBEaOHAlnZ2cMGTIEN27cAABcunQJLi4uiIyMhKurK65evVrt8V999RVee%2B01dO/eHf7%2B/oiJiZGve/vttxEREYHg4GA4Oztj8ODBiIuLw8cffwx3d3d4enri7Nmz8ufq0qULvv/%2Be/j4%2BMDJyQnBwcHIz89XWvfly5cxfPhwuLi4wMPDA%2BvWrUNFRQUuX76Mrl27IjMzU37foqIiuLi44Ny5c8%2Bt%2BY8//sDEiRPh4uICf39/XLt2TfUXmYiIiEgwtUwKt2/fjlWrVqF58%2BYIDg7GunXr8PnnnwMASktL8eDBA1y4cAFGRkYKjztx4gQ2btyI7du3o2PHjjh9%2BjRmz56NEydOoEWLFgCAffv24bPPPsOqVavw1ltvYfz48Zg5cyZCQ0Px0UcfYfXq1ejXrx8AoKysDAcPHsSBAwdQVFSEcePGYf369Vi4cKHC86anp2PChAkIDQ3Fm2%2B%2Bibt372LSpEmwtrbGqFGjYGNjg%2BPHj2PkyJEAgHPnzsHU1BS9e/d%2Bbs2ffPIJiouLcebMGRQVFWHevHl1fj1TU1ORlpamsKxxYytYWVnXOeuvpiv4bYeOzp%2B3orPx1PejygwMFG9FsbQUm2dhoXgrUPNCcVlNmyreCtWtm9i8du0Ub0V58ntQGCsrxVuR9AX%2BeanMEplZqXt3sXkdOijeipSXJy6rY0fFW1GUDHv%2BKpwUqk4tTWFAQADatm0LAPD29saOHTvk60pLSzFq1CgYGxtXe9w333yDwMBAdO3aFQDwj3/8A25uboiOjsbkyZMBAK6urnBycgIA9OzZE99//z3eeOMNAICnpycOHjyokDlhwgRYWFjAwsICI0aMwFdffVWtKYyOjkaLFi3w1ltvAQA6d%2B6MgIAAHDt2DG%2B99RYCAgLw7bffypvCEydOYNCgQdDT03tuzTExMVi3bp28htGjR%2BOnn36q0%2BsZFRWFjRs3KiybPj0YM2fOqFNObYjuX0TnVTIxUUOonZ0aQgE0by42T111PnkzJdIU4YlAYKAaQqfEPP8%2BL2LLFvXkihYUpOkKaqdxY/GZT7YuCbd9u3pyRduzR2xe5Tt3DWBTqDq1NIWtWrWSf2xiYoLi4mKF9S1qeLf78OFDnD9/Hjt37pQvkyQJ7aq8227WrJn8YyMjI9jY2Mg/NzQ0RElJiUJmZXNa%2BbypqanVnjcxMREODg4Ky%2Bzt7XHs2DEAwLBhw7B582YkJSXB2toaZ86ckTe6z6o5MzMTRUVFCq9H69atlX7tzxIUFARvb2%2BFZY0bW6G0tM5Rz2RgAOGZovN0dGQNYWEhIElisxukPxQbaGAgawgfPxb7Qvz6q7gsQDYh7NdP9scxO1to9JakIcKymjaVNYTffAOkpwuLBQBM%2BWaA2MB27WQN4ZQpwN274nKHDROXBcgmhEFBQFQU8NTWCJWJbDT19WUNYWYmUFYmLhcA/vlPsXkdOsgawokTgTt3xGaLnhTu2QOMGgXcuiUul7SaWppCnee8U9CvYROAsbEx5s6di/Hjx9f4WN2nthk%2B/fnTysvLn1vb043k0/e1s7ND9%2B7dceTIEXTp0gWWlpbo9mRz07NqTklJqVaD9AKdjLW1NaytFTcVi2621EX0O7fK/25JUsO7wqfevAhTWio2OyNDXFZV2dnCsx8/FhoHQNYQCs%2BNjRUc%2BMTdu2Kze/YUl1VVWhrw6JHYTNHNW2Wm6FzRb7Iq3bkjPjsnR2weIGsINbjJVyROClVXr05JY2dnh9u3bysse/To0Qs1UpUePvxz%2BpOUlKQwWaz6vHFxcQrL4uLiYGtrK/982LBhOH78OI4dO4YhQ4YoPLammi0tLWFgYIDHVf6C3RU5NSAiIiISRONNoZ%2BfHy5fvgxAtpn06NGjOHPmDMrKynDx4kUMHjwYv6rwbisyMhK5ublITk5GVFQUvLy8qt3ntddeQ0JCAqKiolBWVoZr167hv//9L15//XX5fQYNGoS7d%2B9WawqfVbOBgQFeeeUV7Nq1C7m5uUhKSsLu3btf%2BGshIiIi5XjyatVp/DyF8fHxKCgoAAD07dsX8%2BfPx/Lly5Geno5WrVph6dKlcHZ2fuF8Hx8fDBs2DKmpqfD09MTMmTOr3adly5bYuHEj1q9fj1WrVsHa2hqzZs3CsCr775ibm6N///5ISUmBXZUd/Z9X84oVKzB//nz069cPzZs3x8yZM3HlypUX/nqIiIioupexiRNNeFP49KbUN954Q350cK9evaqtf/rz0aNHY/To0Uqzv/zyS4XPnz69S79%2B/arl%2Bfj4YNCgQdWyZsyYgRkz/jx619PTE56enkqft1JGRgYClRz6%2BKyabWxsEBkZqbDs6RqJiIiINE3jk0JtIEkS9u7di6SkJIVNx0RERFQ/cFKoOjaFtdC9e3fY2tpi/fr1Ss%2BvSERERKTt/rZNobJN1S%2BKl6YjIiKq3zgpVN3ftikkIiKilwebQtVp/JQ0RERERKR5nBQSERGR1uOkUHWcFBIRERERJ4VERESk/TgpVB2bQiIiItJ6bApVx83HRERERMSmkIiIiLRfRYX4f3%2BF2NhYDBw4EMOHD3/ufXft2gVfX1%2B4urpi5MiR%2BO233%2BTriouLsXjxYvTr1w%2B9evXCzJkzkZmZWada2BQSERGR1tPGpvDw4cOYMWMG7O3tn3vf06dPY8OGDfjXv/6FCxcuwMvLC1OmTEFBQQEAYN26dbh%2B/TqioqLw3XffQZIkLFiwoE71sCkkIiIi0oDi4mJERUWhe/fuz71vVFQU3njjDXTv3h3GxsaYOHEiAOD7779HWVkZvvnmG0ybNg3NmzdHo0aNMHv2bJw5cwYpKSm1rocHmhAREZHWU8dkLzU1FWlpaQrLrKysYG1tLST/zTffrPV9r1%2B/jkGDBsk/19XVRadOnRAbG4tOnTohNzcXXbp0ka93cHCAsbExrl%2B/Dhsbm1o9B5tCLWVgIDYvNTUVUVFRCAoKEvbNDqinzh07xNcJAGjfXmhcamoqojZskNVqZycuWJ11Cn5NlwnMSk1NxYYNavq/X1b7d9K1IX9N9%2BwRX6tA6vy/F0mhzubNxYZnZwuNk9f6zTfa8ZoeP16v66wLSRKfuWFDFDZu3KiwLDg4GDNmzBD/ZM%2BRlZUFCwsLhWUWFhbIzMxEVlYWAMDc3Fxhvbm5eZ32K%2BTmYwIApKWlYePGjdXeEdU32lInoD21sk7xtKVW1imettSqLXVqWlBQEA4cOKDwLygoqNaPP3ToEBwdHZX%2BO3DgQJ3rkZ7T%2BT5v/fNwUkhERESkhLW1tUqT1ICAAAQEBAippXHjxvKJYKWsrCy0b98elpaW8s9NTU3l67Ozs9GkSZNaPwcnhURERET1XNeuXXH9%2BnX55%2BXl5bhx4wa6d%2B8OW1tbWFhYKKy/c%2BcOSkpK0LVr11o/B5tCIiIionrIz88Ply9fBgCMHDkSBw8exC%2B//ILCwkJs3rwZhoaG6N%2B/P/T09DB8%2BHBs2bIFjx8/RmZmJtauXYuBAweiadOmtX4%2BvaVLly5V09dCWsbU1BQ9e/ZUGD3XR9pSJ6A9tbJO8bSlVtYpnrbUqi11/p35%2BvriX//6F3766SckJydj69at2Lx5MwICAmBubo6PPvoIr732Guzt7WFvbw8zMzOsWLECn332GUpKShAWFiY/stjd3R2///47li9fjsjISHTo0AEfffQRjIyMal2PjqTqXolEREREpPW4%2BZiIiIiI2BQSEREREZtCIiIiIgKbQiIiIiICm0IiIiIiAptCIiIiIgKbQiIiIiICm0IiIiIiAptCIiIiIgKbQiIiIiICoK/pAoiInqW8vBx6enoAgIqKCty6dQvNmzdH48aNNVwZ0Z/%2B%2BOMPFBcXV1veokULDVTzp0ePHtX6vpqulTSP1z5%2BySUnJyM6OhrJyclYtGgRAODatWtwcnLScGV/ys3Nxb59%2B3Dv3j2lv3TDwsI0UFV12lJnpVOnTsHHxwcAEBsbi0OHDsHe3h5vvfUWdHXrx0aEc%2BfO4f3338e5c%2BdQVlaG0aNH4/bt2wCAtWvXwsvLS8MV/unIkSM4ePAg0tLScPDgQZSUlODLL7/E%2BPHjoaOjo%2Bny5Hbu3IkxY8ZUW56Xl4fVq1dj2bJlGqiqOm2p87///S9WrlyJ3NxcheWSJEFHRwc3b97UUGUyHTt2rPX3n6ZrJc3jpPAldurUKYSEhMDV1RU///wzFi1ahMePH2PcuHFYvnw5/P39NV0iACAkJAS3b9%2BGm5sbTExMNF1OjbSlTgBYs2YNTpw4AR8fHyQnJ2PMmDHw9fXF0aNH8ejRI8yfP1/TJQKQ1TlnzhwAsqYrPT0dFy5cwG%2B//YaVK1fWm6YwIiICUVFRCAoKwpYtWwAAOTk5OHjwIHJzczF79mwNV/inL7/8EseOHcOKFSvg4OAAADh9%2BjSWLl2K1q1ba7a4KrSlzjVr1mDcuHHw8vKCoaGhpsup5ujRo/KPr127hv379%2BPtt99G69atUVFRgbt372LPnj0YO3as5oqk%2BkOil9bgwYOlkydPSpIkSd26dZMv//HHHyV/f39NlVWNs7OzlJycrOkynktb6pQkSfLw8JAePHggSZIkbdq0SRo3bpwkSZKUnp4ueXp6arAyRc7OzlJFRYUkSZI0e/Zsae3atfJ1Li4umiqrmn79%2Bkl37tyRJEmSnJyc5MsfPnwo9e/fX1NlKVVcXCxt2rRJ6tmzpxQeHi7Nnj1b8vDwkA4fPqzp0hRoS529e/eWSktLNV1GrQwePFhKSUmptjwxMbFe/c4nzeGk8CWWkJAAb29vAFDYvNCjRw8kJiZqqqxqmjVrBlNTU02X8VzaUicg2wRnZ2cHADh//rx8KtykSRNkZWVpsjQFpqamyMnJgZGREc6fPy/fnJiZmQl9/frz6ys3Nxft27evttza2hoZGRkaqKhmhoaGmDZtGtq2bYuQkBA0aNAAu3fvRseOHTVdmgJtqXPs2LH4/PPPMWnSpHr1PalMUlISGjRoUG25hYUFkpKSNFAR1Tf1%2BzuY1KpFixa4ffs2OnXqpLD83LlzaNKkiYaqqm7hwoVYsWIFJk6ciFatWlXbP6a%2BbLLRljoBwNbWFhcvXkSDBg1w7do1rFu3DoBs38L69H8/ePBgjBkzBnp6emjTpg2cnZ1RVFSEJUuWoG/fvpouT65Dhw44fPgwhg4dqrD8iy%2B%2BkG/6rC/%2B%2BOMPrF69Gv/3f/%2BHlStXIjExEe%2B88w7Gjh2LiRMn1pvvU22ps0ePHpgzZw62bt0KS0vLaj/3p06d0lBl1bm6umLatGmYMGECWrZsibKyMiQnJ2PXrl1wcXHRdHlUD/BAk5fYV199hYiICAQGBuKLL77AvHnzcPv2bRw9ehShoaF46623NF0iAMDd3R2FhYWoqKhQur6%2B7BytLXUCwJkzZxASEoKSkhJMnToVwdc%2BVmQAACAASURBVMHByM7OxmuvvYZp06Zh9OjRmi4RgGxn/ejoaOTm5sLf3x8WFhYoKSnBxx9/jHnz5sHc3FzTJQIAfvzxR0yfPh0dOnTAtWvX4OnpiTt37iA7OxsRERHo2bOnpkuUc3d3h4%2BPD95//335Edz37t3DkiVLkJqaihMnTmi4QhltqXPgwIFo06YN%2BvbtCyMjo2rrR4wYoYGqlMvIyMCKFStw6tQpFBUVAQD09fXRu3dvrFixAtbW1hqukDSNTeFL7sSJE9i/fz8ePnwIY2Nj2NraYsSIEejTp4%2BmS5P76aefnrm%2BvvzB1ZY6ASA/Px9GRkYoLi5W2OT9yy%2B/wNnZWYOVaa%2BUlBRER0fLf5bs7Ozg7%2B%2BPRo0aabo0BefPn69xylp5sEx9oC11urq64uLFi/VmcllbWVlZKCkpgaWlZb3f7E1/HTaFpDVKS0uRmpoKHR0d2NjYyM9dR3XXvXt3%2BPj4YMiQIXj11Vfr7R8Fb2/vZ55Oo75smtu0aROmT59ebXl%2Bfj7Cw8PxwQcfaKCqP/3vf/%2Br9X179OihxkqeTVvqrGrx4sXw8fGBp6enpkuplcrTkKWkpMi/L%2BvbachIc9gUvsQWLFhQ4zpdXV3Y2NigX79%2BGp8c5eTkYMmSJYiJiUFZWRkAwMjICIMHD8aHH36odJONJjyrgal8PT09PTF27FgYGBj8xdUpunz5Mk6cOIGYmBjk5%2BfDz88PQ4cOhZubm0bretp//vMfhc/Ly8vx8OFDnDlzBhMnTsSbb76pocpksrKykJGRgWHDhuHw4cN4%2BtdpfHw8Zs%2BejWvXrmmoQpmnD87Q0dFRqLXy%2B1ZPTw%2B//fbbX1pbVdpSZ1XLly/HsWPHYG9vj%2BbNm1c7x2d9Oj/p06chi42NxePHjzF48OB6dRoy0hw2hS%2BxRYsW4eTJkzAxMUHnzp2hq6uLGzduoLi4GD179kR6ejquXr2KpUuXIjAwUGN1zpkzB6mpqZg0aRLs7e0ByPYt2rJlC5ydnTU%2Bham0e/dubN68Gb169UK3bt2gq6uLa9eu4X//%2Bx/Gjh2LvLw8HDx4EAMHDsT777%2Bv6XLlYmNjERMTg%2BPHj6O0tBRDhgxBYGAgbG1tNV1ajWJjY7FhwwZs27ZNo3Xs378fq1atQl5eXrWGsNI//vEPfPbZZ39xZYpKSkrkH58%2BfRrHjx/HpEmT0Lp1a0iShN9//x07duzAG2%2B8IT8jAeusnWe9uQaAlStX/kWVPN%2BQIUMwa9YsDBgwAE5OTvI3KxcvXsTHH3%2BM6OhoDVdIGveXnwSH6o3Vq1dLGzZskMrLy%2BXLysvLpY0bN0pbt26VJEmS/u///k/y9fXVVImSJEmSu7u79Mcff1RbnpycXK/OATd16lTpzJkz1Zb/8MMP0rx58yRJkqT79%2B/Xq/MAVvr111%2BlVatWSW5ublLfvn0ld3d3KSQkRMrIyNB0aUqVlZVJzs7Omi5DkiRZLW5ublJiYmK1f%2Bnp6Zour5qBAwdK2dnZ1Zb/8ccfko%2BPjwYqUk5b6tQm3bt3l/%2B%2Br3o%2BzbKyMql79%2B6aKovqkfq5IxH9Jfbt24dz584pbO7Q1dXFpEmT4OXlhcmTJ6Nv375ISUnRYJWyTUXKrhBibm6OgoICDVSk3I8//ojw8PBqy1955RWEhIQAAFq2bFntcliaEh8fj2%2B//RbR0dFIS0uDt7c31q5dCw8PDxQUFGD58uUIDQ3F559/rrEaz507V21ZUVERYmJi6s2Rknp6erh8%2BbLSdRUVFRgxYkS1zeCalJmZqfQyjBUVFfXqHJXaUOfp06fx%2B%2B%2B/w9vbG%2B3bt8fZs2exZ88eGBgYwNvbG6%2B//rqmS1SgLachI81hU/gSMzAwwNmzZzFgwACF5T/%2B%2BKN8M86ZM2fQvHlzTZQn5%2BrqiuXLl%2BO9996DpaUlANmpFdasWYNu3bpptLaqbGxsEB4ejqlTp6Jhw4YAgIKCAmzbtg3m5uaQJAnr16%2BHo6OjhisF3njjDdy6dQtubm6YPHky/Pz8YGZmJl9vZmaGjz76SONHTE%2BcOLHaMkNDQ9jb22Pp0qV/fUE1yMvLw6ZNm/Dbb7%2BhtLRUvjw9PV1pY6NJr776KsaNG4eRI0eiVatW8nPVRUVF1atzP9b3Or/44gusX78eDg4O2LFjB1atWoUFCxbA19cXurq6%2BOSTT5CVlYVx48ZpulS5UaNGYcKECQgMDER5eTkiIyMVTkNGxH0KX2Jff/01Fi9ejI4dO6Jly5bQ19fHo0ePEBsbi5CQEIwdOxaurq749NNPNboDckpKCqZOnYqbN2/Kz0uXk5MDBwcHREREyK/MoWnXrl3D1KlTkZmZCTMzM%2Bjr6yM7OxsmJiYIDw9Hnz594Ofnh/DwcHTu3FmjtUZERCAgIAAtW7astu7mzZvyScLPP/9c7w4%2BqY/mzp2L%2BPh4vPrqq9ixYwcmTZqEGzduID09HWFhYfXqWr2FhYXYvHkzYmJikJycjJKSElhbW6Nfv36YN2%2BewpsDTarvdfr6%2BmLFihVwd3fHsWPH8PHHH%2BPjjz%2BWX4/7ypUrWLBgAb777juN1vk0bTgNGWkOm8KX3I0bN3D27FmkpaWhoqICTZo0Qa9evbB3716sXbsWiYmJaNWqlabLBCBrVpKSklBSUgJbW9t6NSWsVFJSgtjYWKSnp8tfz65duyq9tJSmSZKER48eKezcn5ycjOnTp%2BPKlSsarExRRkYGfvjhB/lluOzt7eHl5aXxpqCqV155BcePH0ejRo0UduDfuXMncnJyMGPGDA1XSKK5uLjg6tWrAICysjL5/3vl6Z0qKirg6uqKX375RZNlKrhw4QJ69%2B79zNM80cuNm49fYuXl5bh69SoePHggbwxycnJw6dIl3LlzBwA01hCWlpbKT9tSWZuDg4PCJcMql2vypLHK6lTWrJaUlNSrk9tevnwZM2fORGZmZrV1T%2B9OoEmVVwoxMTGRHw29Z88efPzxx/jqq6%2BUXm9YEyRJku8yYGBg8P/t3WtYlWXWB/D/hgJRJMcpQRBUHCwTpY0byEIR0gA5SXqlIZYJ4wlRQNMulTh4ysIDmkjFqMiYmlQegeKsWxgBDxzmRU2QABVIFEFBkM3zfuDyyd3eqI3Gfe9cv0/5wIf/RYiL%2B1n3WmhubkbPnj3x7rvvwsnJiXlRmJiYKE4Q2L9//0M/l%2BVQaE3JCXTutS4pKYGlpSWee%2B45BAYGKs37LCgoENtdeBEQEAB9fX24u7vDw8OD%2BRsLwh86KXyGhYeHIzMzEzKZDCkpKXBzc0NpaSl0dHSwYsUKWFtbM8tmZWWFwsJCAJ2zy9T9ZisIAiQSCdP1cZqS8/feeecdvPXWW5g4cSI8PT2RlJSEkpISJCUlITQ0lJtLHO%2B88w48PDyU%2BrIUCgW2b9%2BO/Px8xMfHM0z3G39/fxgZGeGTTz7BzJkzIZPJ8OGHH%2BLcuXP4%2BOOPcerUKab5XFxckJKSAgAPHeUikUiYDgTXlJxA5wiqrVu3YtOmTRg9erTSxzZs2IC9e/fio48%2BYl68Pqi1tRUnTpxAamoqsrKy8Pe//x0eHh5wd3fnegQV6T5UFD7D7O3tkZiYCCMjI/HVhyAIiIqKwoABA/Dee%2B8xy1ZQUACZTAaA7/VxmpLz96RSKc6cOQOJRKL0uvPcuXOIjo7Gzp07GSfs9Nprr6GgoEBl40pbWxvs7e0f%2BTXvLpWVlQgNDcXXX3%2BNoqIizJkzB83NzdDS0kJISAj8/PxYRyR/grS0NBgZGcHS0lLpeUREBGxsbDBx4kRGyR6tvb0deXl5SE5ORkpKCoYMGYJ3330X7u7uXL3VIN2MxRwcwgeZTCb%2B92uvvSa0trYKgiAIDQ0NwpgxY1jFUrF8%2BXK1z5uamoT58%2Bd3c5o/rr29XZg6dSrrGEocHByE2tpaQRAE4c033xQqKysFQRCEtrY2bub/CULnrLqff/5Z5fmlS5cER0dHBokez61bt4SzZ88KNTU1rKM8tnv37ok/A3jGW85jx46xjvA/uXr1qvDVV18J3t7ewsiRI4WFCxcKvr6%2BgqOjo1BSUsI6HmGEegqfYUOHDsUXX3yBOXPmYPDgwThw4ACmT5%2BOa9eucTH/r6qqChUVFTh8%2BDAmTpyosjHil19%2BUTvHjpWHjSV58DIHD9zd3TF58mQkJydjzJgxCAwMhKenJ4qLi7m5WAQAXl5emD17NqZPnw5zc3MAQHl5Ofbs2QMXFxfG6X7j4eGBI0eOiH82MDBgvh7yj/Lw8EBFRQVXbQ7q8JYzIiIC48aN4/Iy2e/dunULKSkpOHLkCM6ePQsrKytMnToVEydOFHtiv/nmG3z88cdK38/k2UGvj59hxcXFCAkJwaFDh5CTk4OgoCDo6OigtbUV06dPx/Lly5nmS01NxZYtW/Dzzz%2Br/biuri6mTZv2yDVT3eXBsSRxcXGYPXs2t2NJAODgwYPw8vLCnTt3EBERgeLiYpiYmGDJkiUqw21ZEQQB//73v/Hdd9%2BhuroabW1tMDMzg6enJ/z8/KCtrc06IgBgxowZ8Pf3h4ODA%2Bso/7OioiLcvXuXqzYHdXjLuXfvXuTn52PSpEkwNjZW%2BZ4cPHgwo2SqLC0t0b9/f3h6emLSpEld9hE%2B2CtNni1UFBJReXk5SktLYWJiwtUph5eXFw4dOsQ6xiPRWJJn18qVK5GRkQETExMYGxur9EBu2LCBUTLNEhcXJw4sj42Nxdy5cxknerRXXnlF5ZlEIuHygtmDPdCEqEOvj4nI3NxcfEXHk64KwpaWFkyYMIGbV8gC52NJ/siJ6rp16/7EJA%2BnSWNJ7lMoFBp1Snjs2DEcPHgQv/76Kw4ePIi2tjYkJCRg1qxZTGfYxcTEwMLCAmZmZoiJiYGLi4tK28h9vJzAsb4F/UfIZDLs3bsXSUlJuHLlCiQSCczMzODt7Q1PT0/W8QgHqCgk3KutrcWaNWtQUlKi1Jt3584dbkanAJ3zCcPCwvDJJ5/g5ZdfRmxsrDiWpKOjg3U8pXVrHR0dyM7OxoABAzB48GB0dHSgrKwMtbW1cHV1ZZiy87ToflH45Zdfdvl5EomEm6KQZRH9R8XExGD//v2YOnUqYmNjAXTOJz148CCampoQFBTELNvkyZMxZ84csTD9fd8ojydw97cCXblyRanQMjQ0ZJxM1ebNm5GYmAgvLy94eHgA6HxDtGbNGjQ3N2PatGmMExLW6PUx4d7s2bMBdA5VjoyMRHh4OP773/%2BitLQU27Zt42aRuyaNJYmMjMTw4cMxefJkped79uzBpUuXEBYWxiiZsgeHg2ui%2Bxt4eDnVAgAHBwfExcXBwsJCqXesqqoK77//PjIzM5nmu3XrFm7fvq00s1AddSsaWbhy5QqCgoJQXFwsPpNIJHj99dexefNmvPDCCwzTKRs7diy%2B/PJLlZ7hoqIiLFu2DMnJyYySEV5QUUi4Z2tri%2BzsbOjp6Sn9I3b06FEUFBQgPDycbcAuNDY2ory8HP379%2Bfu1MDGxga5ublq5/%2B98cYbKCgoYJRMmY2NDSZMmICJEyfijTfegJaWFutIf4irqytXN2UBwNraWlxj%2BODfp9bWVtja2nJzwaCiooK7y1nqzJ07F7q6uli4cKG4h72srEwsCNevX8844W%2Bsra2Rl5en8vdeoVDA1tYWp0%2BfZpSM8IJeHxPuPffcc2IxoKuri4aGBvTp0wdvv/02IiIiuCoKGxoaIJfLUVdXB4lEAkNDQy77NHv37o3jx4%2BrbI3Izc0V%2ByJ5sHbtWqSlpWHx4sXQ0tLChAkT4ObmBltbW672t/7666946aWXVJ7v2rUL7e3tDBJ1bejQoTh8%2BLBKD9mOHTuU1kiypqOjg8jISJSVlSm1Pty3b98%2BBqlU5eXl4cSJE%2BjVq5f47JVXXsH69evh5ubGMJkqCwsLJCYmqrwm/v777zFw4EBGqQhPqCgk3JPJZFiwYAG2bNmCESNG4NNPP4Wvry/OnTsHXV1d1vFEaWlpCAoKgr6%2BPvr37w9BEHDt2jW0tLRgy5YtGDduHOuIorlz5yIgIACvvvoqBgwYAIVCgWvXrqG0tBQrVqxgHU80YcIETJgwAQqFAqdOnUJqaiqWLFkCQRDg6urKTda3334bBQUFKuNIeDshBoBFixYhICAA33zzDe7du4d58%2Bbh4sWLuHXrFmJiYljHE4WEhKClpQX29vbQ09NjHadLPXv2VJpL%2BiAeeokf9NFHH8Hf3x8JCQniLwDl5eWoqqrC1q1bGacjPKDXx4R7N27cQFRUFCIiIlBRUYE5c%2Bbg6tWr6NmzJyIiIsSGadYcHR3h7%2B8PHx8f8RRLEAQkJCRgx44dyMrKYhvwdy5fvoy0tDRcvXoVbW1t6N%2B/P8aOHYuRI0eyjtaltrY2nDx5Ev/6179w%2BvRpbl7Lrl%2B/Hnp6evDz81M6MeJVTU0Njh07hsrKSvTo0QNmZmZwc3NDnz59WEcTSaVSZGdnw8DAgHWUh1q6dCnq6%2BsRFBSkNGA9OjoaPXv2xJYtWxgnVHb9%2BnUcPXpU7Hc1MzODq6srjI2NWUcjHKCikGgcQRBw/fp19O3bl5vhxUDnP2L5%2Bfkq/Tr37t2Dra0tzp49yyiZqvr6eoSHhyM7O1u80a2np4dx48YhNDQUffv2ZZzwN42NjcjIyEB6ejrkcjleeuklODs7w8XFBcOHD2cdD0Dnlo26ujrcvn0bBgYGKt%2BXvIxNuq%2BxsREKhQJ/%2B9vfAADV1dXo1auX%2BGceeHt74%2Buvv8aLL77IOspDNTY2YsWKFUhLSxOfCYIAe3t7fPbZZ1z9XSLkUagoJNyTyWTIz8/nqodMneDgYHh7e2Ps2LFKz3Nzc7F//35s3ryZUTJVM2bMgEQiwcyZM8UTgurqasTHx0NLSwvx8fGME3Z6//33cebMGRgZGcHFxQWurq7cFIIP%2BuGHHx76cW9v725K8mj/%2Bc9/sGDBAkRGRmLixIkAOm%2Bdb9q0Cdu2bYOdnR3jhJ1ycnKwe/du%2BPj4wMTEROWSEU83uoHOW9P3T91NTU25KQanTp362D87eenTJOxQUUi4FxwcjNdff52bmXRd2bRpE/bt2wepVIrBgwdDoVCgsrISZ8%2Behaenp1JfVEhICMOknbdO5XK5yqWShoYGODg4cHMDNSoqCi4uLrC0tGQd5X%2B2YcMGLF68mHUMkbe3N95//32VQvXYsWOIi4t7ZIHbXTRpU0hdXR1ycnJQV1cHHR0dGBkZwd7eHvr6%2Bqyj4Ysvvnjsz12wYMGfmIRoAioKCffmzp2LwsJCaGtrw8jISOX1LC%2B/3c6YMeOxPk8ikWD37t1/cpqHc3d3x86dO1VuzNbX1%2BODDz7A0aNHGSVTVVNTg6NHj6K2tla8WFJUVMRd72NWVpbKgPXa2lqkpqaKI2B4IJVK1V6K4a3N4cqVKw/9OC9zCn/88UcsXrwY%2Bvr6MDExgSAIYr/e5s2bNWrTDSF0%2B5hwz9LSUiNOihISElhHeGwLFy7E4sWL4evri0GDBqGjowOVlZXYu3cv/Pz8cPnyZfFzWb6mS09PR3BwMKytrXH69GmsWLEC165dw4cffojIyEhuRn5s3boVO3bswMsvv4yioiJIpVKUlZXB0NAQa9asYR1PiZmZGVJTU1W2hRw8eJCrywaasilk48aNWLp0KXx9fcVX3B0dHdi7dy/WrVvHVVHY0tKC6OhoZGZmimOzjIyMxDWcPE1zIGzQSSEhT4lCoUBGRgYqKipU5qpJJBIEBAQwSqZK3au5B/Hyms7DwwOLFi3C%2BPHjMXLkSBQVFQHo7ItbvXo1NyeaD24JuZ%2BztbUVERERcHR0xIQJE1hHFMnlcgQGBmLgwIEYMGAAOjo6cPnyZVy7dg1xcXGQyWSsIwLQnE0hUqkUeXl5Kpt32tvbYWNjw83JKwAEBgaivLwc06ZNg7GxsXiq%2Be2332LIkCHc3ZQm3Y9OCgn3HtUTw0sfTFBQELKzs2Fubq7yGzdvRWF6ejrrCI%2BlqqpKHLD9YLO8jY0NqqurWcVS0dTUBAsLCwCAtrY2FAoFdHV1ERISgvfee4%2BrotDe3h4pKSlITk5GVVUVJBIJ7Ozs4O7uzs3KSABYtWoVjI2N8emnn6psClm7di03m0KcnJxw8uRJlTmk%2Bfn5XJ0SAsCJEyfw008/qeyMd3Z2hrOzM6NUhCdUFBLunThxQunPCoUCV65cgSAIsLa2ZpRKlVwux%2BHDhzViNRcv/ViPYmxsjAsXLqjsapXL5VwVMIMGDcL3338Pb29vGBsbIy0tDc7Ozmhvb0d9fT3reCoMDQ0xc%2BZM1jEeiudNIRs3bhT/u2/fvli2bBlGjhyJf/zjH5BIJLh8%2BTLOnDnD3eW4F198ET179lR5rq%2Bvr3YjD3n2UFFIuLd//36VZx0dHYiNjYWOjg6DROqZmZlxNfz3r8DHxwd%2Bfn6YMmUKFAoFdu3ahQsXLiApKQlLly5lHU8UEhKChQsX4u2338YHH3yAkJAQmJubo6amBo6OjqzjYcaMGWLP66NGlPBycYvnTSG/fyU8dOhQ3L17FyUlJUrPzp07193RHmrlypUIDQ2Fv7%2B/Ui/xzp07sWTJEqVLUjz9bCXdh3oKica6d%2B8eHBwckJOTwzoKAKCkpATbt2%2BHi4sL%2BvXrpzJXzcbGhlEyzfbTTz/hu%2B%2B%2BE7dvmJqaYtq0aXjjjTdYR1PS0tIijh3Kzc1FcXExTExM4OzsrHJjvrtt374d8%2BbNA6A57RiatilEE1haWqK9vV3ll4L7/cMP4mnkD%2Bk%2BVBQSjZWTk4OgoCDk5eWxjgIAiI6ORmxsLNT9lWJ9YUNTFRYWwsrKinWMRwoPD4e7uzs3lzQeJikpSRxazTOeN4UkJiZiypQpANS/ybhPIpHg3Xff7a5Yj/RHflba2tr%2BiUkIr6goJNyzt7dXeXb37l3cvn0bM2fOxMcff8wglSpra2uEhYXByclJ7WgHeh3zx8lkMuTk5HD/tZs3bx5ycnLwwgsvwMXFBW5ubtwWs3Z2dsjMzFTbW8YjHjeFuLi4ICUlBQDEi1DqSCQSjbnURQhARSHRAOo2LOjq6mLgwIFcrTxzcnJCcnIyzfp6iuLj41FZWQkfHx8YGxurDFzmqVhsaWnB8ePHkZaWhqysLPTu3Ruurq5wc3PDq6%2B%2ByjqeaO/evcjPz8ekSZPUfk15Wh934MABvPrqq%2BLf86ysLNTW1nJ3gUNTyOVyfPbZZ/jll1%2BU%2Bgfvo7cZhIpCwiUnJ6fH3tfJy2/iGRkZyM3Nha%2BvL4yMjFTy81TAaAqpVIr29nbcu3dP6evJwwzFh2lvb0dOTg6%2B%2BuornD59mqucmrI%2BbtOmTThy5Ag2bdoknrqeOnUKoaGhcHV1RXBwMOOEnWQyGQoKCljHeCyOjo4YM2YMxo4dq/aX1zFjxjBIRXhCRSHh0oM3IOvr6/Htt99iwoQJ4o25S5cuISsrC7NmzeJmtMaoUaPQ0tKitqcQoN/C/xf3e6A6OjrQ0NAAiUSCPn36iAUib31PbW1tkMvl4mmhtrY2nJ2dsXLlStbRRJqyPm7MmDHYt2%2BfSp6rV6/ivffeQ3Z2NqNkyjRlNzvQedktNzeX%2BcUnwi8qCgn3/Pz8sGjRIpVdtwUFBYiJicGOHTsYJVP2qCZu3goYTVBfX4/w8HAcP35cfN3Vo0cPjBs3DqGhoVz0lwGdK%2BLS09Mhl8uhr68PZ2dnuLi4YNSoUY994t3d6urqlFad8TT3Eeg8gTt%2B/LhK7%2BOtW7cwbtw4bjaFaMpudgBYs2YNhg8fjkmTJrGOQjhFRSHhnlQqxalTp1Rev7a1tcHOzo6bfxweJiQkRGngLXk8M2bMgEQiwcyZM8W9vNXV1YiPj4eWlhbi4%2BMZJ%2Bxkb28PZ2dnuLq6cl0IAsDly5cREhKC8%2BfPi6faEokEI0aMQFRUlLg9hLWFCxdCS0sLc%2BfOVVrHt23bNvTo0YObkTSaMuIH6Px/P2vWLDz//PMwNDRU%2BT7dvXs3o2SEF1QUEu55eXlh7NixmDNnDvT19QEAt2/fRlxcHNLT03HkyBHGCTspFArs27cPJSUlSk3cdXV1uHjxIk6dOsUwnWaysrKCXC5H7969lZ43NDTAwcEBhYWFjJIpUzfnjVfvvPMOhgwZAn9/f5iYmIj7b7/%2B%2BmuUl5ervdjFwo0bN7By5UpkZWUpFa/jx4/H2rVrxZ8F5PF5eHhAW1sbtra2ansKFy9ezCAV4Qk1FhDuRUZGYtGiRdixYwcMDAygUChw%2B/ZtGBgYYNu2bazjiVatWoXMzEzIZDKkpKTAzc0NpaWl0NHRwfbt21nH00impqa4e/euSlGoUChgamrKKJWqrgrC9evXo66uDhs2bOjmRF0rKyvDnj17xEHbQOflk8jISK4Ggvft2xcxMTFoaGhAdXU1tLS0YGJighdeeIF1NCWadFJYXV2NkydPasw4ItL9qCgk3LOyskJGRgZKSkpQU1ODtrY29OvXD1ZWVlyNf0lLS0NiYiKMjIyQmpqKzz77DIIgICoqChcuXOBqT7OmWLhwIRYvXgxfX1%2BltVx79%2B6Fn58fLl%2B%2BLH4uT6NU7nv%2B%2Bee5u3U%2BdOhQ1NTUqHy96uvrMXToUEaputanTx%2Bu10dqym52oLPNoby8HJaWlqyjEE7R62NCnhIbGxvk5%2BcDUO6DvHXrFjw8PHD8%2BHHGCTWPuvEpD%2BJxlArvfvjhB%2BzcuRPe3t4YNGgQFAoFqqqqcOjQIUyZMgWDBg0SP1fd4HjWpk%2Bfjl9%2B%2BQVyuZx1lC49uJvd39%2BfdRzR9u3bceDAAUilUvTv319lFWdISAijZIQXVBQS8pRMnz4do0ePxpw5czB16lRMnjwZ06dPx/nz5%2BHr66sxs8x48qjxKQ9iPUqloaEBcrlcvNFraGgIe3t7GBgYMM31e48qtO/jtdBOS0tDU1MTvL29WUd5KN52swOdF7e6IpFI6KIJoaKQkKeluLgYISEhOHTokLiXWUdHfQljkgAACnVJREFUB62trfDx8cGKFStYRyR/krS0NAQFBUFfXx/9%2B/eHIAi4du0aWlpasGXLFowbN451RNLNeNvNTsjjoKKQkKcoJycHRkZGMDc3R1lZGZKTk6Gjo4PZs2ezjkb%2BRI6OjvD394ePj4946UQQBCQkJGDHjh3IyspiG1ADNTQ0IDY2VtxtvmfPHuzfvx8DBw5EaGgo%2BvXrxzhhp652s9%2B5cwczZ87EsmXLGKTqWk1NDY4ePYra2lrxF9WioiKVObDk2URFISFPSUJCAqKjo7F161aMHj0aQOcKvuXLlyMwMBC%2Bvr6ME5I/i1QqRX5%2Bvsrg4nv37sHW1lYjZmny1qsXGBgIhUKBmJgYFBcXY8aMGQgPD0dJSQnq6uq4mVN4f4SPIAi4efMm%2Bvbty%2BVudqDz51FwcDCsra1x%2BvRpFBcX49q1a3B3d0dkZCTc3NxYRySsCYSQp8LR0VEoLS1VeX7hwgXBycmJQSLSXYKCgoTs7GyV5zk5OcKiRYsYJPrjUlNThe%2B//551DJGtra3Q2NgoCIIgrF27Vli8eLEgCILQ0tIijB49mmU0JU1NTUJoaKhgZWUlDB8%2BXBAEQbh586Ywe/Zsob6%2BnnE6Ze7u7kJqaqogCIIwYsQI8Xlubq7g5ubGKhbhCI2kIeQpuXnzJszNzVWeDxgwADdu3GCQiHQXMzMzfPTRR5BKpRg8eDAUCgUqKytx9uxZeHp6Km2z4fWG5/jx41lHUNLR0SEOqD558iQCAgIAdI75aWlpYRlNyerVq1FbW4u4uDjMmjULQGdGfX19rF69mqtNRlVVVXBycgKgPFvTxsYG1dXVrGIRjlBRSMhTYm1tjY0bNyIgIEActnz9%2BnVs3rwZVlZWjNORP9OZM2cwdOhQ3LlzByUlJeLzoUOH4vz58%2BKfedh6oim9epaWlti2bRt0dXVRV1cnXtZJSkriaiZlZmYmkpOT0bdvX/H/b69evRAWFgZnZ2fG6ZQZGxvjwoULGDZsmNJzuVzO3e5rwgYVhYQ8JeHh4QgMDMTu3buhr6%2BPjo4O3LlzB8OGDUNsbCzreORPlJCQwDrCYwsNDYVCoQDQeWP%2B888/F3v1Vq9ezU2vXlhYGFatWoXGxkZ8/vnn0NPTQ0NDA1cZgc5CX93KPYVCgdbWVgaJuubj4wM/Pz9MmTIFCoUCu3btwoULF5CUlISlS5eyjkc4QBdNCHnKSktLUVlZCS0tLZiamj72XDiiuRQKBTIyMlBRUaFSCEgkEvHVJw/s7OyQlpaG3r17Y926daivr0dUVBTu3r0LJycnrubqqdPa2srVJqN58%2BbBxMQES5YsgZ2dHQoLC3HlyhWsWbNGHGLNk59%2B%2BgnfffcdKisr0aNHD5iammLatGlcrTgk7FBRSAghTygwMBDZ2dkwNzdXKVgkEgn27dvHKJkqGxsb5OXlQSKRwN3dHQEBAXB1dYVCoYBMJuPmpnRTUxO%2B/fZblJWVqT1x42Wf9NWrVzF//nxcunQJ7e3t6NWrF5qbmyGVSrFhwwb079%2BfdURCHhu9PiaEkCckl8tx%2BPBhpRVxvNKUXr3g4GBcuHABo0aNgp6eHus4XTI2NsbBgwdRXFyMqqoq6OrqwszMDBYWFqyjPbYzZ86gubmZy7WGpHvRSSEhhDwhLy8vxMfHo0%2BfPqyjPFJFRYXYq7dgwQI4ODigoaEBzs7O2LJlC%2Bzs7FhHBNA5%2BzElJQWGhoaso/zlubq6oqKigsu1hqR7UVFICCFPqKSkBNu3b4eLiwv69esHLS0tpY/b2NgwSvb4eOvVc3V1xYEDB9Re4iD/m8LCQpqEQB6KikJCCHlC0dHRiI2NhbofpxKJhKsTGE3p1Ttx4gSSkpLg7%2B%2BPAQMGqIzz0dHRYZRMc8lkMuTk5NDXjnSJikJCCHlC1tbWCAsLg5OTk9rTNp7%2BEfb3939or966desYpFIlk8nQ0tKCjo4OtR/nqdDWFPHx8aisrISPjw%2BMjY2hra2t9HGevk8JG1QUEkLIE3JyckJycjJXr1%2B7oim9enl5eQ/9uK2tbTcl%2BeuQSqVob29He3u72o9ToU2oKCSEkCeUkZGB3Nxc%2BPr6wsjIiOtXnZrWq3fv3j3U1dVBIpHA0NBQ5XSLPD4qtMmjUFFICCFPaNSoUeKrzgcLQkEQuOsp1JRevcbGRoSFhSEtLU082dLV1YW7uztCQ0M14lSWEE1DRSEhhDyh%2BycwgiDg5s2b6Nu3r9LHeTqB0ZRevZCQENTV1eGf//wnBg4cCAAoKytDbGwsXnvtNaxYsYJxQs1z/vx5bNq0CWVlZbh7967Kx%2BVyOYNUhCdUFBJCyBO6c%2BcO1q9fj8OHD6O9vR0lJSVoaGjAsmXLsG7dOpUikSVNeYVoY2ODH3/8UeVrV1tbi2nTpiEzM5NRMs3l4eEBQ0NDODk5qb1k5O3tzSAV4QltNCGEkCe0atUq1NbWIi4uDrNmzQIAPP/889DX18eaNWu4GfMC/Fb08d6rp62trbZwMTAwQHNzM4NEmq%2B6uhqJiYn06p10SevRn0IIIeRhMjMzsWHDBshkMrFHr1evXggLC0NOTg7jdMoaGxsRHBwMa2trjB8/Hm%2B99RZGjRqFlStXqp1byIq1tTUiIiJw48YN8dmNGzewatUqjBgxgmEyzTVs2DDU1NSwjkE4RieFhBDyhCQSidrbvAqFgqtCCwDCw8Px66%2B/4osvvlDp1YuKiuKmVy8sLAzz58/Hm2%2B%2BCQMDAwCdBe2QIUMQExPDOJ1m%2BvDDD7Fs2TJ4eXnBxMREZfMO7T4m1FNICCFPaN68eTAxMcGSJUtgZ2eHwsJCXLlyBWvWrEFHRwdiY2NZRxRpUq9ebm4u9PT0cP36dbS1teGll17C3bt3MWbMGNbRNNIrr7zS5cd4uyVP2KCTQkIIeUKhoaGYP38%2BZDIZ2tvbMWrUKDQ3N0MqlXLVTwhoTq9eQkICoqOjsXXrVowfPx4AkJ6ejuXLlyMwMBC%2Bvr6ME2qe8%2BfPs45AOEcnhYQQ8pQUFxejqqoKurq6MDMzg4WFBetIKubPnw8DAwMsXbpUPC28ceMGoqKiUFdXh7i4OMYJOzk5OSEmJkbldOvixYuYN28e0tPTGSUj5K%2BLikJCCHmG1NbWYv78%2Bfi///s/tb16ZmZmjBN2kkqlOHXqlMow7ebmZrz55ps4e/Yso2SE/HVRUUgIIc8YTejV8/Pzg4WFBQICAtC7d28AwPXr17F582ZUV1dj165dbAMS8hdERSEhhDxDHuzVGz16NAA%2Be/WqqqoQGBiIixcvQl9fHx0dHbhz5w6GDRuG2NhY9OvXj3VEQv5yqCgkhJBniKb16pWWlqKyshJaWlowNTV96A1aQsiTodvHhBDyDLl58ybMzc1Vng8YMEBpUDQvhg0bhmHDhrGOQcgzgTaaEELIM8Ta2hobN25EU1OT%2BOz69etYu3YtrKysGCYjhLBGr48JIeQZQr16hJCuUFFICCHPIOrVI4T8HhWFhBBCCCGEegoJIYQQQggVhYQQQgghBFQUEkIIIYQQUFFICCGEEEJARSEhhBBCCAEVhYQQQgghBFQUEkIIIYQQAP8PmMwzpHoKwjcAAAAASUVORK5CYII%3D\" class=\"center-img\">\n",
              "    <img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoUAAAI6CAYAAABRtTJ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD%2BnaQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy%2BWH4yJAAAgAElEQVR4nOzdeVxVdf7H8ddlJ1AQATWXNA3cFTdyGRfKkTRxakyTXMjK3MO0dJomtanJyUxzaVHLLNOhmpmsrJ%2BK5ThN5tS4hChaSuKWoHJFEGS7vz%2BIm1dRsfu9ydX38/HgceF7z/nc7/ne7cPnnO85FpvNZkNERERErmseV7sDIiIiInL1KSkUERERESWFIiIiIqKkUERERERQUigiIiIiKCkUEREREZQUioiIiAhKCkVEREQEJYUiIiIigpJCEREREQG8rnYHRMR9fPvtt/zjH/9g27ZtHDp0iPz8fHx9fQkPD6dly5b07duXmJgYLBbL1e6qiIhcIYuufSwil2Oz2Xj22Wd5%2B%2B23AQgKCiIiIoLAwEByc3PZvXs3ubm5APTq1Yv58%2Bfj4%2BNzNbssIiJXSEmhiFzWO%2B%2B8w9NPP42fnx8zZ86kf//%2BeHp62u8vKCjgb3/7G3/9618pLS1l1KhRTJ48%2BSr2WERErpSOKRSRy1q1ahUAw4cP53e/%2B51DQgjg5%2BdHQkICDz30EAArVqygoKDgV%2B%2BniIj8ckoKReSyMjIyAIiMjLzkcgkJCSxdupT169fj5%2BcHwLRp04iMjOSvf/0reXl5PPfcc8TExNCqVStuvfVWxo8fz969ey8ac/PmzYwfP55u3brRsmVLbr31VhISEvjoo48uuo7VamXu3Ln87ne/IyoqihYtWtClSxdGjx7Nli1bKlwnJiaGyMhINm3axPr16%2Bnbty%2BtWrXif//7HwALFiwgMjKSadOmUVhYyIIFC%2BjTpw%2BtW7emR48e/PnPf%2BbMmTMAfPfdd0ycOJGuXbvSqlUr%2Bvfvz4cffljh4%2Bbn57NkyRIGDRpEhw4daN68OdHR0dx///2sX7%2B%2BwnWGDRtGZGQkq1atIi8vj7lz59r70rFjRx566CF27tx50fEREamIkkIRuayQkBAAtm/fftnlfvOb3xAaGnrBfXl5eSQkJLBq1Spq1KhBx44dKSkpYf369dxzzz0VJjFz584lISGB9evXExgYSHR0NCEhIWzevJkpU6aQmJhISUmJwzpZWVncfffdvPrqq%2Bzfv5/mzZtz66234uPjw%2Beff86IESP4%2B9//ftFt2L9/P5MmTcLb25tbb73Vntye67HHHuONN96gbt263HLLLWRmZrJixQqmTp3K3r17GTJkCGlpaTRt2pSwsDD27t3LY489xsaNGx3i5OfnEx8fzwsvvEBqaiqNGzemc%2BfOBAUF8eWXXzJ%2B/HgWLFhw0b4WFhaSkJDA8uXLqVOnDi1atKCkpIRNmzYxbNgwezIvIlIpNhGRy5g6daotIiLCFhkZafvrX/9qO3bs2BWv265dO9udd97psG5OTo5t4MCBtoiICNvAgQMd1lu3bp0tIiLC1rFjR9uWLVsc7vvvf/9r69Kliy0iIsK2bNkyh/ueeeYZW0REhK1Pnz6248eP29uLi4ttzz77rC0iIsLWvn17W25ursN6vXr1skVERNhiYmJsr7zyygXbMX/%2BfFtERIStW7dutri4OFtWVpb9vvXr19vHJzY21rZgwQJbaWmpzWaz2UpKSmzjxo2zRURE2B544AGHmMuWLbNFRETYbr31Vlt6enqF9zVr1syWkZHhcN/QoUNtERERtp49e9ri4%2BNtJ06csN%2BXmZlp69q1qy0iIsL2l7/85YLtEBG5GFUKReSyEhMTqVOnDjabjddff52ePXsydOhQ5s2bx5dffkl%2Bfv5lY%2BTm5jJjxgzCw8PtbdWqVWPatGlA2elufvjhB/t9r7zyCgBTp06lU6dODrE6duzI448/DsDy5csd7gsPD6dfv36MHz%2BemjVr2ts9PT2ZNGkSHh4enD59%2BqJVz/KJMheTmZnJzJkzHaqht99%2BO2FhYdhsNry8vBg3bpz9tDweHh4MHjwYgF27djnECggIoH///owePZqGDRs63DdixAjCwsIoKSlh8%2BbNFfYlKyuLF1980V7JBQgLC6N///4A7Nix46LbISJyPp2nUEQuq3bt2rz33nvMnj2bjz/%2BmJKSEr7%2B%2Bmu%2B/vprXnnlFby9vYmOjiYuLo5%2B/frh5XXhR0t4eDjt27e/oD0qKoqAgADy8vLYvn07DRs25NixY6SmpgLQu3fvCvt0%2B%2B23Y7FYOHLkCOnp6TRq1AjAPtmlIv7%2B/tSsWZOsrCyysrIqXObWW2/Fw%2BPi/y%2BHhYXRtm3bC9rr1q1LVlYWvXr1uuA8jXXr1gXg1KlTDu333HMP99xzT4WPY7FYqFev3iX72qlTJ2rVqnVBe4MGDQDIzs6%2B6HaIiJxPSaGIVEpYWBjPP/88jz/%2BOOvXr2fz5s188803nDhxgqKiIr744gu%2B%2BOILlixZwssvv2xPTMpFRERUGNfDw4O6deuyd%2B9eDh06BOAw8aS8klgRLy8vioqK%2BOGHH%2BxJIZQda/evf/2LnTt3kpmZSU5ODrafzr51%2BvRpoKwiWJGKkqxz1atXr8L28vMylieAFd1XXFx8wX2lpaX85z//Ydu2bWRmZmK1Wu19S09Pv2Rf69evX2G7r68vAEVFRZfaFBERB0oKReSKhIaGMmTIEIYMGQKUTczYsmULq1evZtu2bXz33XeMGjWKjz76CG9vb/t6QUFBF41ZrVo1oGwyCjhW1DZs2HDZPpUnegBpaWmMGzfOnmBeqUv1E3DYpl9y/7mOHDnCuHHjLtitXFk6QbiImKSkUESccvPNN3PzzTczZMgQli9fzl/%2B8hfS09P55JNPGDBggH25S%2B2SLa%2BElS9TvvvV29ublJSUSl82r6CggDFjxnDkyBEaNmzI2LFj6dy5MzVq1LAnazExMRw%2BfPiiMX7NS/RNnDiRXbt2ERoayoQJE%2BjevTuhoaH2ZG/YsGH897///dX6IyLXNyWFImLMiBEjWLFiBRkZGXz33XcO95VfBq8i5feVVwyDg4OBst2f2dnZDhMpLmXTpk0cOXIEi8XC4sWLuemmmy5YpqqcVDs1NZWUlBQAXnjhBTp37nzBMlWlryJyfdDsYxG5pPXr1/PUU0%2BxePHiSi1fvvu1/Li2ct9//32Fy5eUlNgrd%2BXHyJ17/OH5yeWllM9ebtCgQYUJYUZGBidOnKh0PFc6cOAAgP18iOfLy8u75Em9RURMU1IoIpeUkpJCUlISixcv5siRI5dc9tixY6SlpQFcMEP34MGD7N69%2B4J1/ve//9mvBBIVFQWUTWpp0aIFAO%2B%2B%2B26Fj5Wens6AAQPsp66BnyuNZ8%2BerXCdRYsW2X8//6TXv7bAwECgbNd5RRNCXn/9dXulsKIJKiIipikpFJFLuv/%2B%2BwkLC%2BP06dOMGDGiwsvE2Ww2vvrqKx588EGKiopo27YtXbt2dVimWrVqPPnkkw6VOqvVyqxZs4CyU8HceOON9vtGjx4NwMcff8ybb75pnz0MZVW2iRMnkpaWxtGjR%2B3tTZs2BeDHH38kOTnZ3p6fn88zzzxDSkqKPfHcv3//Lx4TEyIiIvD09KSkpISVK1fa24uLi3nttddISkqiV69ewM%2BzkEVEXEnHFIrIJdWoUYM33niDCRMm8MMPPzB8%2BHDCwsK4%2BeabueGGG7BarRw6dMh%2BLr327duzcOHCCyaWxMTEcPDgQW677TZatWqFt7c3O3bsIDc3l8DAQJ566imH5X/7298yatQoFi9ezHPPPcdbb73FzTffTHZ2Nrt376akpITmzZszZcoU%2BzpRUVF069aNL774ggkTJtCmTRt8fX3ZuXMnXl5eLFu2jDVr1rBt2zbeeust9uzZw5gxY%2BjYsaPrB/I8tWvXZuDAgSQlJfHcc8/x4YcfEhwczO7du8nLy2PhwoUcOnSIzz//nLVr1zJs2DDi4%2BO54447fvW%2Bisj1QUmhiFxWREQEH330EWvWrOGzzz4jLS2Nb7/9lsLCQvz8/KhVqxadO3emb9%2B%2B9OzZs8IZvB4eHrzxxhu8/PLLrFu3jiNHjhAQEECfPn2YNGmSw3kGy02ePJkuXbqwYsUKtm/fzubNm/H19aVly5b07duX%2BPj4C07LMm/ePF544QU2bNjAzp07CQ8Pp0%2BfPowePZoGDRpQu3Ztdu3axdatW/nuu%2B9%2B1dnG53vyyScJCgri448/Zu/evdSsWZPo6GgefvhhmjVrRkFBAVu2bGHTpk189913DtVSERHTLDZ9yoiIC02bNo1//vOf3HXXXfZdxSIiUvXomEIRERERUVIoIiIiIkoKRURERAQlhSIiIiIu8%2B9//5suXbowadKkSy5XWlrK3Llzue222%2BjYsSMPPPAABw8etN9vtVpJTEykS5cudOvWjT/%2B8Y/Gr3qkiSYiIiIiLrBkyRLef/99QkJCqF27NnPnzr3osm%2B//TbLli1jyZIl1KpVi7lz5/L111%2BzevVqLBYLEyZMoLCwkOeee46ioiIeeeQRWrZsyZNPPmmsv6oUioiIiLiAr68v77//foWX3TxfUlISCQkJNG7cmMDAQCZNmsS%2BffvYsWMHx48fJzk5mUmTJhESEkKtWrUYO3Ysf//73yu8ItIvpfMUioiIiFQgMzPTfmL%2BcmFhYYSHh1dq/eHDh1dquYKCAr7//nuaN29ubwsMDOSmm24iJSWF06dP4%2BnpSWRkpP3%2BFi1acObMGfbv3%2B/Q7gwlhe7K9Al3GzWC776DW24Bg5fUspW6z9EJFtyjrzau3smWr1Rpqdl4Hh7mYwJ4FuSZDWixgL8/5OdDVT5Cx5X9PHnSXCxPT6hTB44eBdPXrA4ONhvPYoGAAMjLMz%2BmHgZ37rnyuQ8IMBuvslxwIvqk%2BfNZuHChQ9v48eOZMGGC0cc5deoUNpuNoKAgh/agoCCys7MJDg4mMDDQ4WT75ctmZ2cb64eSQikTHFz2wWv6A9IFLJaq/T3rwE066ybddMVnvmtYLD//VOWBdZd%2BeniU9dHDw3xSaJq7jKm79PMqGzx4MDExMQ5tYWFhLnu8S03z%2BDWmgCgpFBEREfdnspL6k/Dw8ErvKnZGcHAwHh4eWK1Wh3ar1UrNmjUJCQkhNzeXkpISPD097fcB1KxZ01g/NNFERERE5Cry9fXllltuITU11d6Wk5NDRkYGrVu3plmzZthsNtLS0uz3p6SkUL169QqvG/9LKSkUERER9%2BfhYf7HhY4dO0ZsbKz9XIRDhgzhrbfeYt%2B%2BfeTm5vLCCy/QrFkzWrVqRUhICH369GHevHmcPHmSH3/8kUWLFjFw4EC8vMzt9NXuYxEREXF/Lk7ifolWrVoBUFxcDEBycjJQVuUrKioiPT2dwsJCAO69916ysrIYNmwYeXl5REdHO0xyefrpp5k%2BfTq33XYb3t7e3HnnnZc9IfaV0smr3ZXpI%2B6jomDrVmjXDrZtMxbWFbOPXXVctEtmH7ugs66YfeyqMTU9U9jT0zXzDIzPPvbw%2BHlmpyumS5viyn6anH3s7Q21a8OPP4LBc7IB5ifXeXj8PPvYFdPvTcZy1XN/tWYf%2B/qaj3n2rPmYVZgqhSIiIuL%2BqmCl0N1oBEVERERElUIRERG5BqhS6DQlhSIiIuL%2BlBQ6TSMoIiIiIqoUioiIyDVAlUKnaQRFRERERJVCERERuQaoUug0JYUiIiLi/pQUOk0jKCIiIiKqFIqIiMg1QJVCpykpFBEREfenpNBpSgorKSUlheeee469e/fi4%2BND7969efLJJ/H29ua9995j7ty5FBYWMnjwYKxWKyUlJcyaNQuAFStW8M4773DkyBHq1avHpEmTuP3226/yFomIiIj8TElhJU2aNIm4uDjefvttjh07xr333kuTJk1o164df/rTn3jppZfo2bMnS5Ys4b333iMmJgaAdevWsXDhQpYuXUrTpk357LPPSExMZN26ddx4442VeuzMzEyysrIc2sIaNSI8ONjcBjZt6ngrcj0xXWGwWH6%2BrcrVC1f209vbXCwvL8dbk0xvd3k8VzzvJmO66rkvLTUX60pV5feam7DYbDbb1e6EO8jNzcXHxwcfHx8AJk%2BejJeXFw0bNuT//u//WL16NQAlJSXExMTQuXNnZs2axahRo4iIiGDKlCn2WCNGjKBr166MGjWqUo%2B9YMECFi5c6NA2fuxYJjzyiKGtExERMSAvDwICrs5j16ljPubRo%2BZjVmGqFFbSV199xaJFi/jhhx8oLi6muLiY2NhYsrKyqFu3rn05T09Pmjdvbv87IyOD//znPyxfvtzeZrPZaNKkSaUfe/DgwfbKY7mw/v3hnJhOa9oUVq6E%2BHhISzMW1va/rcZilbNYwBX/ylhwRVDznbVhMRoPXDemposGnp5QUmI2JoBnYb7ZgBYL%2BPlBQYFrBtYUV/bz1Clzsby8IDQUjh%2BH4mJzcQGqVTMbz8MD/P0hP9/8G8B0pdAdXqNXQpVCpykprIR9%2B/bxyCOPMHXqVAYNGoSfnx%2BPPfYYxcXFlJaW4nXeLg2Pc16Yfn5%2BTJ48mZEjR/7ixw8PDyc8PNyxMT39F8e7pLQ02LbNNbFFqipXfXnbbFd3d9rluLKfRUVm40FZQmg6rquen9LS6/e5v1qUFDpNI1gJu3fvxsfHh%2BHDh%2BPn54fNZmP37t0A1KxZkyNHjtiXLSkpYdeuXfa/GzRowJ49exziHTlyBO21FxERkapESWEl1K1bl4KCAnbv3s2pU6eYPXs2Pj4%2BZGZmEh0dzc6dO9m4cSOFhYW88sorFBQU2NcdPHgwn3zyCRs3bqS4uJivvvqKO%2B%2B8kx07dlzFLRIREbnGeHiY/7nOaPdxJURFRXHfffcxdOhQ/P39GTNmDE888QRjxoxh5cqVJCYmMmXKFLy9vRkxYgTR0dFYfprZ1bVrV6ZOncrTTz/N8ePHqVevHjNmzKBt27ZXeatEREREfqbZxwYUFhbaZyUDDB06lA4dOpCYmOi6B7UYnmwQFQVbt0K7dkaPKbSVmn95aaKJJpqY5lmQZzagKycbmOTKfp48aS6WtzfUrg0//mj%2BmEKTp/aCsjENCCibhVuVJ5q48rm/WrOPGzUyH9NVx%2B9XUddfbdSwgwcPEhUVxWeffUZpaSlffPEF27Zto3v37le7ayIiItcP7T52mnYfO6l%2B/frMmjWL2bNn8%2Bijj1KrVi2mT59Ou3btrnbXRERERCpNSaEB/fv3p3///le7GyIiItev67CyZ5pGUERERERUKRQREZFrgCqFTlNSKCIiIu5PSaHTNIIiIiIiokqhiIiIXANUKXSaRlBEREREVCkUERGRa4AqhU5TUigiIiLuT0mh0zSCIiIiIqJKoYiIiFwDVCl0mpJCN2UrtRmPaQFs/9tqNqaHxWg8oqJg61Ys7dvBtm1GQ2dlmh1TLy%2BoUQOyrRaKi42GNqq8n1YrxvsZdkOeuWAeHuDvj2dhPpSWmosLZBcGGI3n6QnV/SGnyJ%2BSEnNxawSbf98D4OdnPGSJn%2BExBUrCahuNCXDypNl4Xl5QI6DsNWX6/WQynpcXhPlDVq6/8X7WMfvUy69ISaGIiIi4P1UKnaakUERERNyfkkKnaQRFRERERJVCERERuQaoUug0jaCIiIiIqFIoIiIi1wBVCp2mpFBERETcn5JCpykpFBEREXGBw4cPM3PmTHbs2MENN9xA3759mTx5Mh7nJbAjR47k66%2B/dmgrLi5m3LhxjB8/nmHDhrF161aH9Ro1asSHH35otL9KCkVERMT9VcFK4YQJE2jRogXJycmcOHGChx9%2BmNDQUO6//36H5d544w2Hv3Nycujbty%2B9e/e2t/35z3/m7rvvdml/q94IioiIiLi5lJQU0tLSmDJlCtWqVaNhw4YkJCSQlJR02XXnzZtH7969iYyM/BV6%2BjNVCkVERMT9uaBSmJmZSVZWlkNbWFgY4eHhl103NTWVunXrEhQUZG9r0aIF6enp5ObmEhgYWOF6Bw4c4IMPPiA5Odmh/ZNPPmHp0qUcPXqUNm3a8PTTT9OgQYNfsFUXp0qhiIiIuD8PD%2BM/SUlJ3H333Q4/lan0AVitVqpXr%2B7QVp4gZmdnX3S9xYsX8/vf/56QkBB7W%2BPGjbnllltYuXIlGzZsICQkhAcffJDCwsJfMFAXp0qhiIiISAUGDx5MTEyMQ1tYWFil17fZbFf0eFarldWrV/Ppp586tM%2BYMcPh76effpro6Gj%2B97//0blz5yt6jEtRUigiIiLuzwW7j8PDwyu1q7giISEhWK1Whzar1YrFYnGoAp5rw4YNNGrUiPr1618ydmBgIEFBQRw7duwX9e1itPtYRERExLCWLVty9OhRTp48aW9LSUmhSZMmBAQEVLjOhg0b6Nq1q0Nbbm4uM2bMcEgAT548ycmTJy%2BbPF4pJYUiIiLi/lxwTKEzmjdvTqtWrZgzZw65ubns27ePZcuWMWTIEABiY2P55ptvHNbZvXs39erVc2gLDAxkx44dPPPMM1itVk6dOsXMmTOJjIwkKirKqT6e75pJCletWnXBfn9X%2BPrrr2nVqpXxgztFRETECVUsKQSYP38%2BmZmZdO3aleHDh/O73/2O%2BPh4ANLT0zlz5ozD8llZWYSGhl4QZ9GiRdhsNvr06UPPnj0pKipi8eLFF5wE21k6prAS1q1bR2RkJDfddBMdO3YkJSXlandJREREqrjatWuzZMmSCu/bs2fPBW07d%2B6scNkbb7yRhQsXGu1bRa6ZSqErzZ8/nwMHDlztboiIiMjFVMFKobtx2y3esWMHcXFxtG3blvvvv58TJ04A8I9//OOCgzQHDRrEggULAFiwYAEPP/wwiYmJtGvXDig7YHPixIl07tyZDh068NBDD3H06FEA4uLi%2BO677xg7dix/%2BMMf2LJlC5GRkZw9exaAH3/8kTFjxhAdHU379u2ZNGmSfbbRli1baN%2B%2BPZs2bSI2Npa2bdvywAMPcOrUqV9ljERERK4bSgqd5pa7j0tKSpg4cSL9%2BvXjkUceIS0tjQkTJuDlVbnN2b59O4888ghz5swBYPbs2eTl5bFhwwZsNhuJiYn85S9/YcGCBXz44YdERkby8ssv0717d7Zs2eIQa%2BzYsTRp0oQNGzZQUFDAI488wvTp03nppZcAyM/PZ82aNSQlJZGfn8/AgQN59913eeihhyq9vRWdUT00tHJnVL/qDB8ES9OmjrcGVfLlU2meno63VZVL%2B2nyQ9Vi%2BfnW8Ie16W0v7951%2BJ3idq7X9335dpve/uJis/Hk1%2BWWSeHOnTvJzMxkzJgx%2BPr60qZNG3r37s3nn39eqfU9PT0ZMmQIlp%2B%2BZGbOnElxcTE33HADALfffjuvvvrqZePs3r2b1NRUXnvtNQIDAwkMDGTUqFGMGzfOPhGlpKSEBx98kKCgIIKCgmjfvj379%2B%2B/ou1NSkq64FiCcePGM3HihCuKUxnl37vGbN1qOOBPVq40HrKG8YhlzjuhfZXlmn76mw/p52c8ZHUXdBPgIlexcoLpN2h5WPNxXZEUuSJmDRe98d3lfW96%2B3/ayXZ16L8wp7llUvjjjz9SvXp1qlWrZm9r2LBhpdevXbu2PSGEsusMzpo1i2%2B//ZaCggJKS0sJDg6%2BbJxDhw4RFBTkcHbzBg0aUFRU5HA%2BoXOnl/v7%2B1NQUFDpvkLFZ1QPDQ3jCk%2BUflkWC%2BZjtm9nNmDTpmUJYXw8pKUZDZ29wWwC6%2BlZ9sWQkwMlJUZDG%2BXKftbwyzcXzGIpSwgLCoy/UHOKzGaFHh5lCWFuLpSWmotbvZrhNyi45o0PlJSaTTQ9PV3zPsrJMRvPle8nk1U4L6%2ByhDA7W9U9%2BZlbJoWFhYWUnPduK73EJ%2B/5y567m7m0tJSHH36Y9u3bs3btWkJCQnjvvfeYN29epfpxMecmnc5OGa/ojOou%2BAx3jW3bXBM3Lc14bFd9MJaUuMeHrkv6aTIjKn8f2Wxm4%2BK6pL20tGr/QyDu9b53RV%2BLi93j86lSVCl0mluOYHh4OLm5uZw%2Bfdretm/fPgB8fX3Jz/%2B5OlFSUsLhw4cvGuv48eMcPnyYYcOG2S87s2vXrkr1o379%2Bpw6dYrjx4/b2/bv34%2Bvry%2B1atW6om0SERERJ2iiidPccovbtGlDUFAQS5cupbCwkG%2B%2B%2BcZ%2BPOFNN91EXl4eX3zxBYWFhbz22muXvCB1SEgIN9xwA9u3b%2Bfs2bN89NFH7N69m9zcXPLy8oCyRPPAgQPk5uY6rNuqVSsaN27MnDlzOHPmDMeOHeOVV16hX79%2BeHt7u24ARERERAxzy6TQz8%2BPRYsWsWHDBjp27MjChQsZOXIkUHatwYSEBCZNmkT37t3x8vK65GVgvLy8mDFjBosXL6ZLly58/fXXLFiwgNq1a/Pb3/4WgHvvvZfnn3%2Bexx57zGFdi8XCyy%2B/TGZmJj179mTQoEG0adOGp556ynUbLyIiIhdSpdBpFtulymhSZbniWXPJRBMPw7Mao6LKZjS3a2f8mMKsTLMb7y4Hcruyn2E35JkL5uEB/v6Qn2/8mMLswoovTv9LuWqyQY1gN3nj4z4TTU6eNBvPle8n0xNNwsIgK8t8P%2BvUMRuv0n7/e/Mx//538zGrMLecaCIiIiLi4Dqs7JmmpFBERETcn5JCp2kERURERESVQhEREbkGqFLoNI2giIiIiKhSKCIiItcAVQqdpqRQRERE3J%2BSQqdpBEVERERElUIRERG5BqhS6DSNoIiIiIioUigiIiLXAFUKnaakUERERNyfkkKnaQRFRERERJVCERERuQaoUug0JYVuyoLNJVFNx83KNBvPywtqANkbtlJcbDQ0YeEWswGjomDrVmrc1g62bTMX12o1Fwt%2B%2BiCtRg2v0%2BBRajT0sdwgY7G8vKCmP5w442/8ua91crfZgH5%2BUL0R1U%2BkQ0GBubgFweZiQdmghoXB8eOYHlSP2nWMxgPXfOeH5ewzG9DHB2rUp0buQSgsNBu71OD709cXaEBYfgacPWsuLgC3GI4nvxYlhSIiIuL%2BVCl0mpJCERERcX9KCp2mERQRERERVQpFRETkGqBKodM0giIiIiKiSqGIiIhcA1QpdJqSQhERETncVMMAACAASURBVHF/SgqdphEUEREREVUKRURE5BqgSqHTNIIiIiIiokqhiIiIXANUKXSakkIRERFxf0oKnaYRFBERERFVCkVEROQaoEqh0zSCIiIiIqJKoYiIiFwDVCl0mkbwCn399de0atWKwsLCq90VERERKefhYf7nOnP9bbGTOnbsSEpKCj4%2BPle7KyIiIlKFHT58mFGjRhEdHU2vXr2YPXs2paWlFyy3YMECmjVrRqtWrRx%2Bjh8/DsDZs2d56qmn6N69O9HR0UycOJHs7Gzj/VVSKCIiIu6vClYKJ0yYQK1atUhOTmbZsmUkJyezfPnyCpcdMGAAKSkpDj%2BhoaEAzJ07l9TUVJKSkli7di02m40//OEPTvfvfFUuKTx48CAjR44kKiqKXr168dZbbwGQkpJCfHw8HTp0oEuXLkyfPp2ioiIAtmzZQrt27diwYQMxMTFERUUxb948UlJSiIuLIyoqivHjx9uXHzZsGC%2B%2B%2BCKJiYm0bduWHj16sH79ensfMjIyeOCBB4iOjiY6OppHH32UnJwc%2B2NFRkZy9uxZAL799lv69OlDmzZtGD16NCtWrCAmJsa%2BbPv27dm0aROxsbG0bduWBx54gFOnTv1q4ykiIiK/vpSUFNLS0pgyZQrVqlWjYcOGJCQkkJSUdEVxiouLef/99xk7dix16tQhODiYxMRENm7cyLFjx4z2ucpNNBk/fjydOnVi0aJF/PDDD9x33300btyY6dOnExcXx9tvv82xY8e49957adKkCcOGDQMgPz%2BfzZs3s2bNGtauXcu0adPYs2cPb775JqdOnSIuLo7PPvuMPn36APC3v/2N559/nueff553332XSZMmsWnTJkJCQnjyySepW7cu//73v8nNzeWBBx7g5ZdfZtq0aQ59LSwsZPTo0cTFxZGYmMiWLVt44okn8PX1tS%2BTn5/PmjVrSEpKIj8/n4EDB/Luu%2B/y0EMPVXpMMjMzycrKcmgLCw0lPDz8lw7zr8bL8CvM09Px1qioKLPxmjZ1vDXF9HEu5fFccPyMyeffpc%2B9n5/ZeOWHl5g%2BzMT0G6o8num47sT0c%2BTt7Xhrks1mLpar%2BvlTweSqcMFnWIXfv2Fhlfr%2BTU1NpW7dugQFBdnbWrRoQXp6Orm5uQQGBjosv2fPHu6991727t1LnTp1%2BMMf/kC3bt3IyMjg9OnTtGjRwr5s48aN8fPzIzU1lVq1ajm5lT%2BrUp8Eu3btYs%2BePSxfvhx/f3%2BaNWvGwoULqVWrFh988AE%2BPj54enpy44030rFjR3bu3Glft7S0lPj4ePz9/YmJicFms9GnTx9CQkIICQnh5ptv5sCBA/bl27ZtS8%2BePQGIj49nwYIFfPHFF8TFxbF48WIsFgs%2BPj6EhITwm9/8hq1bt17Q35SUFE6ePMmYMWPw8/OjR48e3HrrrWzbts2%2BTElJCQ8%2B%2BCBBQUEEBQXRvn179u/ff0XjkpSUxMKFCx3axo8bx4SJE68oTqVYLEbD1ahhNJxd9eouCFrBc2zEypWuiWtaQIDxkDWNR4TgYBcErdnIBUGBunVdE9c0F7xRzX6S/BTTFUHr13dBUKB2bdfENa1OHbPxvvvObLwr4YKksMLv3/HjmTBhwmXXtVqtVD/vy6o8QczOznZICmvXrk39%2BvWZPHky4eHhJCUlMXr0aD788EOsVivABbGqV69u/LjCKpUUZmRkEBgYSPA5n/pdunQBIDk52V49LC4upri4mNjYWIf16/z04i6v1J2bPfv6%2Btp3%2BQI0avTzl4CHhwd16tQhMzMTgJ07dzJnzhz27NlDUVERJSUltGzZ8oL%2BZmVlERgY6PBfQKtWrRySQoB69erZf/f396egoKCSI1Jm8ODB9l3S5cJCQ83%2B1whln7iGY2ZbzX6Ke3qWJYQ5OVBSYjQ0NW5rZzZg06ZlCWF8PKSlmYv7r3%2BZiwVlH6QBAZCXBxUcAO2ME4XVjMXy9CxLCK1W8899zZx0swF9fMoSwsOHweSZCs6rLDjNy6ssIczOhuJio6FtoWFG47ng46ks7qGDZgN6e5clhD/%2BCD8dsmSM6UphnTpw9Kj5fl5DKvz%2BDav8a9tWyefsnnvu4Z577rH/nZCQwJo1a/jwww/p3r37FcVyRpVKCj08PCqclbNv3z4eeeQRpk6dyqBBg/Dz8%2BOxxx6j%2BLwPMY/z/ks4/%2B9zlZz3rWKz2bBYLJw6dYpRo0YxZMgQlixZQmBgIPPmzePLL7%2B8IEZpaSle5%2B12sVTwr%2Byl%2BlEZ4eHhF5aqf4UXhwmGv2fsSkpcEPu8ZN6YtDSzsQ0nbg5xDcd2xfPvkuf%2BCv9Rq7TCQrOxTe/mLldc7Lo3a1XnqtOLFRWZj%2B2K935R0dXd5WuSCyqFFX7/VlJISIi9ylfOarVisVgICQm57Pp169YlMzPTvqzVaiXgnD06p06domZNs/tjqtREk/r165OXl2ev2EFZhfDTTz/Fx8eH4cOH4%2Bfnh81mY/fu3U491sGDP/93WFpayo8//kjt2rXZv38/eXl5PPDAA/bS7q5duyqMUbNmTU6dOkVubq69LSUlxal%2BiYiIyC9QxWYft2zZkqNHj3Ly5El7W0pKCk2aNHFI7gBefvllNm/e7NC2b98%2B6tevT/369QkKCiI1NdV%2B3969eyksLKxwL6YzqlRS2KxZM5o3b868efPIy8tj7969/PGPf8Tb25uCggJ2797NqVOnmD17Nj4%2BPmRmZv7icuq2bdv48ssvKSwsZMWKFeTl5dG1a1duvPFGPDw82LZtG2fOnOHNN9/k%2BPHjHD9%2B/ILKZMuWLfH392fJkiUUFhayadMm/vvf/5oYChEREXFjzZs3p1WrVsyZM4fc3Fz27dvHsmXLGDJkCACxsbF88803QFkVcObMmezfv5%2BzZ8/yxhtvkJGRwV133YWnpyeDBg3i1Vdf5ejRo2RnZ/Piiy/Su3dv%2BylrTKlSSSHAq6%2B%2ByuHDh%2BnSpQujR49m7NixPPzww9x3330MHTqUfv36UbduXZ544gn27t3LpEmTftHjxMXFkZSURKdOnVi6dCkvvfQSwcHB1KpVi0cffZQnnniCXr16cerUKV544QUKCwuJj493iBEQEMC8efP44IMPiI6OZvXq1SQkJFS4C1lERERcqIpVCgHmz59PZmYmXbt2Zfjw4fzud7%2Bz5xLp6emcOXMGgMmTJ9O9e3cSEhLo2LEjH3/8MW%2B%2B%2BSa1f5qwNHHiRNq0acOAAQO47bbbCAgI4Nlnn3W6f%2Bez2H6NIxermGHDhtGmTRumTJnidKzyYxM9fzpPxvz58/nqq69Y6eoZpy454tr8kdxZx80myC48Lp6wcMPJfFRU2Yzmdu3MHlN43jEqTvPwgGrV4PRp48csHSsIuvxCleTlBTVrwokT5p/7WiedOxzlAn5%2B0KgRpKebPabQ9NRrLy8IC4OsLPMTTWqbndXqsokm%2B/eZDejjUzaj%2BeDBqn1Moa8vNGgAGRnmjym85Raz8SrruefMx3TBCaKrsipXKXQnNpuN2NhY5s6dS1FREQcOHOCDDz6gR48eV7trIiIi15cqWCl0N1Vq9rG7sVgszJ07l2effZZOnTpRrVo1%2BvTpw/3333%2B1uyYiInJ9uQ6TONOuy6Tw7bffNharZcuWrFq1ylg8ERERkavhukwKRURE5BqjSqHTNIIiIiIiokqhiIiIXANUKXSakkIRERFxf0oKnaYRFBERERFVCkVEROQaoEqh0zSCIiIiIqJKoYiIiFwDVCl0mpJCERERcX9KCp2mERQRERERVQrdlQ2L8ZgWF8V1G1ar2Xjl/7X%2B619QWmoubnCwuVgAUVGwdSv06AHbthkNvfBJm7FYderA2LGQlARHjxoLC8Cf%2B5wwGzAgoOzWaoW8PHNxb7zRXCz4%2BTXq52f2NQqcPGkulqdn2cv%2B1CkoKTEXF6Dm99%2BbDVitGtSvDwcPwunTZmMXFZmLVb06NGgAP/wAOTnm4gLccovZeJWlSqHTNIIiIiIiokqhiIiIXANUKXSakkIRERFxf0oKnaYRFBERERFVCkVEROQaoEqh0zSCIiIiIqJKoYiIiFwDVCl0mpJCERERcX9KCp2mERQRERERVQpFRETkGqBKodM0giIiIiKiSqGIiIhcA1QpdJqSQhEREXF/SgqdphEUEREREVUKRURE5BqgSqHTNIIiIiIiokrhpUyaNAlfX19mzZp1tbsiIiIil6JKodOUFIqIiIj7U1LoNI2giIiIiFxfSeGhQ4eIjIxk7dq19OvXj9atWzN06FCysrIAePfdd4mJiaF9%2B/bMnDmT0tJS%2B7o2m40XXniBHj16EBUVxV133cXXX38NwKJFi7j77rsdHuubb76hdevW5ObmsnHjRvr3709UVBTdunVj9uzZDrFFRETESR4e5n%2BuM9fl7uMVK1bwxhtv4Ofnx/jx45kxYwaTJ0/mqaeeYuHChXTv3p0PP/yQZ555htjYWABWr17NBx98wPvvv09YWBivvPIKEydO5IsvvmDAgAEsWLCAffv20bhxYwDWrl1Lr1698PX1ZdKkSSxatIjOnTtz4MABHnzwQaKiorj99tsr1d/MzEx74louNDSM8PBwswPjAl6GX2Geno63Rpn%2BACiPZzpuVJTZeE2bOt4aVKeOuVihoY63RgUEmI3n7%2B94a4q7vEYx%2Bx516fu%2BWjWz8cpfS6ZfUwDFxeZiBQY63pqSk2M2nvyqrsukMD4%2Bnlq1agGQkJBAYmIibdq0oXnz5vZEbeDAgSxfvty%2BTv/%2B/bntttuo9tMHSL9%2B/ViwYAFHjhyhfv36dOjQgY8%2B%2BojExEQAkpOT%2BeMf/8jZs2cpKCjghhtuwGKx0LBhQ9atW4fHFXwIJyUlsXDhQoe2cePGM3HiBKfGoSIWi9l4NWqYjVeuenVXRDX85VDO9JfD1q1m45VbudJ4yLHGI8KgQS4IiuFEu5wLEm2XcEECE2w8ovn8DYAuXVwQFGjTxjVxTWvXzmy8jz82G%2B9KXIeVPdOuy6SwUaNG9t/r1q1LYWEhhw8fpl69eg7LNWzY0P57fn4%2Bf/nLX9i0aROnTp2ytxcWFgIwYMAAXnvtNRITE0lJSSEvL4/u3bvj4%2BPDuHHjGDp0KK1bt6Zr167cfffd1LmCEsrgwYOJiYlxaAsNDcNmu5KtvjyLBeMxrVaz8Tw9yxLCnBwoKTEbu4bXabMBPTzKvmzz8sDk4QI9epiLBWWJy8qVEB8PaWlGQ7/8oLkENjS0LCF89104ftxYWADGdt5mNqC/f9m4pqVBfr65uE2amIsFrnuNAtYScxmcp2dZQnj6tPn3ffCuL80GDAgoSwh37CgbV5NMVwrbtSv7JzM311zcq0lJodOuy6Tw/GMFoSy5Kz7vDXfucjNnzmTPnj2888473HTTTRw8eJDevXvb77/jjjt45pln2L59O59//jmxsbH4%2BPgAMH78eO655x6Sk5NJTk5m6dKlLF%2B%2BnNatW1eqv%2BHh4RfsKjadvLmKyc%2Bwc5WUuCC2h4uO8ywtNfuFu81wAlMuLc147KNHjYYDyhJC43FNf3mXy883G9tVxyKbfo1iPnkrj2k87mnD/wyWy8szH7uoyGw8KEsItctXfnJdptUZGRn23w8fPoyfnx916tThxx9/dFhu37599t%2B//fZb4uLiaNiwIRaLhdTUVIdlAwMDue222/i///s/Pv30U%2BLi4uz3Wa1WatWqxX333ceyZcuIjY1l9erVLto6ERGR65Ammjjt%2BttiYNWqVRw/fhyr1cry5cvp0aMHPXr0YNeuXWzcuJHCwkLeeecdjh07Zl%2BnXr16pKSkUFhYyPbt21mzZg1QNgmk3IABA3jvvfcoKiqiffv2AGzbto077riDb7/9FpvNxokTJ0hPT6dBgwa/7kaLiIhcy5QUOu263H0cFxfHiBEjyMjIoG3btkyfPp2aNWvy5JNPMmPGDHJycujfvz%2BxsbH23cuTJ0/m8ccfp1OnTrRp04bnn38egLFjx7JixQpatGhBt27d8Pf3584778Ty04yNqKgoxowZQ2JiIsePHyc4OJg77riD%2B%2B6776ptv4iIiMj5rsuksG3btvZK37mGDh3K0KFDK1ynefPmfHzerKrzZwTn5%2BeTn5/PwIEDHdqHDx/O8OHDney1iIiIXNR1WNkzTSNoyNmzZ3n66afp1q0bN91009XujoiIiFxlhw8fZtSoUURHR9OrV69LXrxi1apV9OnTh6ioKAYMGEBycrL9vmnTptG8eXNatWpl/%2BnQoYPx/l6XlULTvvnmG0aOHEnHjh2ZPXv21e6OiIjI9acKVgonTJhAixYtSE5O5sSJEzz88MOEhoZy//33Oyy3du1a5syZw2uvvUbr1q354IMPSExM5NNPP6V%2B/foAjBkzhgkTzJ%2Bf%2BFxVbwRdqF69euzZs8d%2B1RFTOnTowLfffsvrr79OSEiI0dgiIiJSCVVsoklKSgppaWlMmTKFatWq0bBhQxISEkhKSrpg2YKCAh599FHat2%2BPt7c399xzDwEBAWzfvt2pPlwpVQpFREREKlDRZWbDwip3mdnU1FTq1q1LUFCQva1Fixakp6eTm5tL4DmXGBwwYIDDujk5OeTl5dmvvgbw1VdfsWHDBg4cOEDjxo2ZMWMGLVu2/KWbViElhSIiIuL%2BXLD7uKLLzI4fP75Su3GtVivVz7sma3mCmJ2d7ZAUnstms/Hkk0/Spk0bOnXqBED9%2BvXx8PDgkUceISAggIULFzJy5EjWrl1LDYPXk1VSKCIiIlKBii4zGxYWVun1bVd4%2BbGioiKmTZvG999/z1tvvWVvHzdunMNyjz32GB9//DHJycncc889V/QYl6KkUERERNyfCyqFFV1mtrJCQkKwWq0ObVarFYvFUuH8g4KCAsaOHUt%2Bfj7vvPPOJSuAnp6e1KlTx%2BECGiZcVxNNRERE5BpVxSaatGzZkqNHj3Ly5El7W0pKCk2aNCEgIMBhWZvNxqRJk/Dy8uLNN990SAhtNhvPPfccaWlp9rbCwkIyMjLsM5NNUVIoIiIiYlj5eQXnzJlDbm4u%2B/btY9myZQwZMgSA2NhYvvnmGwA%2B%2Bugjvv/%2Be1566SV8fX0d4lgsFg4dOsTMmTM5duwYeXl5vPDCC3h7e3P77bcb7bN2H4uIiIj7q4LnKZw/fz5/%2BtOf6Nq1K4GBgdx7773Ex8cDkJ6ezpkzZwD4%2B9//zuHDh%2B0TS8oNGDCAZ555hmeffZa//vWv3H333eTm5tK6dWuWL1/ODTfcYLS/SgpFREREXKB27dosWbKkwvv27Nlj/3358uWXjBMcHMxzzz1ntG8VUVIoIiIi7q8KVgrdjZJCERERcX9KCp2mERQRERERVQrdVWmp%2BZienubjht2QZzaghwfgTw2/fOOdPZYbdPmFroCXF9QEThRWo7jYXNyFT17ZyVAvp04dGAu8/OBWjh41Gpo/P2MxFywqCsZuZezSdrBtm7m4wEuhZsc0LAzio2Dl7ijOu0KWUyJOm4sFUL06dO0K//m2Gjk5ZmObvPqWt3fZbUEBFBWZiwvwxrd9jMYLD4cRXWD5d10wfAo5vAx%2BY4eHw33AOwe7G%2B/nJLPhKk%2BVQqdpBEVERERElUIRERG5BqhS6DQlhSIiIuL%2BlBQ6TSMoIiIiIqoUioiIyDVAlUKnaQRFRERERJVCERERuQaoUug0JYUiIiLi/pQUOk0jKCIiIiKqFIqIiMg1QJVCp2kERURERESVQhEREbkGqFLoNCWFIiIi4v6UFDpNIygiIiIiqhSKiIjINUCVQqdpBC%2BiT58%2BvPfee1e7GyIiIiK/ClUKL2Lt2rVXuwsiIiJSWaoUOk1JoYiIiLg/JYVOc6sRPHToEJGRkaxdu5Z%2B/frRunVrhg4dSlZWFlu2bCEqKoo333yTdu3asW3bNgBWrFjBHXfcQZs2bejXrx/JyckAzJ49m2HDhjnEX79%2BPR06dKCwsJCYmBhWrVoFQGlpKYsWLaJ37960bt2au%2B66i82bN9vXi4yMZNOmTfa/V61aRUxMjH3dWbNm0a1bN9q2bUtcXBz//ve/XTpOIiIiIlfKLSuFK1as4I033sDPz4/x48czY8YMhg8fTlFREQcOHODLL7/E19eXdevWsXDhQpYuXUrTpk357LPPSExMZN26dcTGxrJs2TKsVivBwcFAWVJ4%2B%2B234%2BPj4/B477zzDu%2B99x6vvfYajRo1YsWKFYwdO5bk5GRq1qx5yb6uWbOGL7/8kg8//JCgoCA%2B%2BOADpk6dyr/%2B9S%2B8vb0rtb2ZmZlkZWU5tNWsGUZYWPgVjNpVYvo/N4vl51vDsb0Mvxs8PR1vTalTx2y80FDHW6OioszFatrU8dagsDCz8WrUcLw1pXp1s/ECAhxvTarkx1ullL83Tb9HAcINf4yGhDjemmRy%2B131Gs3MNBvviqhS6DS3TArj4%2BOpVasWAAkJCSQmJjJ06FCKioqIj4/Hz88PgPfff5%2BBAwfSsmVLAH7729/Svn17Pv74Y0aNGkWdOnX4/PPPueuuuyguLmbjxo3Mnj37gsd7//33iY%2BPJzIyEoCRI0eydOlSNm7cyO9///tL9jUnJwcvLy/8/f3x9PTk97//PXfddRceV/DiTUpKYuHChQ5t48aNZ%2BLECZWOUVmmExj8/Q0H/MlPz7FJNV3U1Z/%2B5zBm7Fiz8coNGuSCoGO3mo%2B5cqXxkPHGI5a54w4XBTasbdur3YPKccU/LiNGmI8J0L%2B/a%2BKa1rev2Xhz55qNJ78ut0wKGzVqZP%2B9bt26FBYWYrVaAbjxxhvt92VkZPCf//yH5cuX29tsNhtNmjQBIDY2luTkZO666y7%2B%2B9//YrFY6Nq16wWPd%2BjQIRo3buzQ1qBBAw4fPnzZvvbr14/Vq1fTvXt3unbtSs%2BePenXr98VJYWDBw%2B2744uV7NmGCUllQ5RKZ6emI9ZmG82oMVSlhAWFIDNZjT0iTNms0JPz7KE0Go1O65JSeZiQdkX7aBB8O67cPy42dhjl7YzF6xp07KEMD4e0tLMxQVWTjGbvNaoUZYQfvopZGebi3vTTeZiQVmFsG1b2L4d8vLMxj7vI9MpXl5lr9Pjx6G42FxcANNzCkNCyhLCjz6CkyfNxjZdKezbFz75xOxr9KpSpdBpbpkUlpaW2n%2B3/ZQYWH7areh1zrvGz8%2BPyZMnM3LkyArj3HHHHQwdOpSCggLWrVvHb3/7W4f1yxUWFla4fvljXqp/wcHBvPvuu2zdupXPP/%2Bc%2BfPns2rVKt55550KH6si4eHhhJ%2B3j8N08uYy54yFEeVvepvNeGzTXzblSkrMxj561Fyscx0/7oLYPx3ba1RamvG45x2dYUx2ttnYpnf1lcvLg5wcszGLiszGg7L3kem4rtrdefKk%2Bdiu2H2enX2Vd/mapKTQaW45ghkZGfbfDx8%2BjJ%2Bfn/24wHM1aNCAPXv2OLQdOXLEnki2bNmS0NBQvvzyS5KTk%2Bl7kTp6gwYN2L9/v/3v4uJiDhw4QP369QHw8fGhoKCgwv6dPXuW/Px82rVrx%2BTJk/n444/Zu3cvaYYrHSIiIiLOcMukcNWqVRw/fhyr1cry5cvp0aNHhVW7wYMH88knn7Bx40aKi4v56quvuPPOO9mxY4d9mdjYWF5//XVsNhudOnWq8PEGDBjAypUr2bdvH4WFhbz66quUlJTYd%2Bk2bNiQ5ORkiouLSUlJYePGjfZ1n332WaZOncrJkyex2WykpqZSWlrqsJtbREREnOThYf7nOuOWu4/j4uIYMWIEGRkZtG3blunTp/P9999fsFzXrl2ZOnUqTz/9NMePH6devXrMmDGDtuccVR0bG8uSJUsYOnQonheZZTFy5Eiys7N56KGHyMnJoVmzZrz11ltU/2kq4BNPPMFTTz1Fhw4d6NChAyNHjuS1114DYPLkyUyfPp0%2BffpQXFzMTTfdxJw5cwhxxdQ0ERGR69V1mMSZ5pZJYdu2bVmzZo1DW82aNS/YVQwwdOhQhg4detFYLVu2rHC9zz77zP67l5cXU6dOZerUqRXG6Ny5M%2BvXr3doGzx4MABBQUHMmzfv4hsjIiIiUgW4ZVIoIiIi4kCVQqdpBEVERETEvSqF9erVq3BXr4iIiFznVCl0mlslhSIiIiIVUlLoNI2giIiIiKhSKCIiItcAVQqdphEUEREREVUKRURE5BqgSqHTlBSKiIiI%2B1NS6DSNoIiIiIioUigiIiLXAFUKnaYRFBEREXGBw4cPM2rUKKKjo%2BnVqxezZ8%2BmtLS0wmXfeust%2BvTpQ7t27RgyZAg7d%2B6033f27FmeeuopunfvTnR0NBMnTiQ7O9t4f5UUioiIiPvz8DD/46QJEyZQq1YtkpOTWbZsGcnJySxfvvyC5T777DMWLFjA888/z5dffkmvXr0YPXo0Z86cAWDu3LmkpqaSlJTE2rVrsdls/OEPf3C6f%2BdTUigiIiLur4olhSkpKaSlpTFlyhSqVatGw4YNSUhIICkp6YJlk5KSuPvuu2nTpg1%2Bfn48%2BOCDAHz%2B%2BecUFxfz/vvvM3bsWOrUqUNwcDCJiYls3LiRY8eOOdXH8%2BmYQjflWZBnNqCHB/j741mYDxcpbf8S2YUBxmIBeHpCdX/IKfKnpMRoaGqd3G02oJ8f1GxEzZx0KCgwFvbPfU4YiwVAQAAQxdjO2yDP7OvqpVCbsVhhYRAPrJyylawsY2EBeCTRYjZgVBTEbyX%2BhXawbZu5uAMHmosF0KgRdH2erqsfh/R0s7EffdRcrIAAqN2a2pnfGn%2BNPvZMrNF4tGkDIzYx4vXusGOH2dgmtWkD923ivtdc0M9Jp8zGu4oyMzPJOu8DJywsjPDw8Muum5qaSt26dQkKCrK3tWjRgvT0dHJzcwkMDHRYtm/fvva/PTw8aNasGSkpKTRr1ozTp0/TokUL%2B/2NGzfGz8%2BP1NRUatWq5cwmOlBSKCIiIm7PhuF/7iir4C1cuNChbfz48UyYMOGy61qtVqpXr%2B7QCKKVfgAAIABJREFUVp4gZmdnOySFVqvVIXksXzY7Oxur1QpwQazq1asbP65QSaGIiIhIBQYPHkxMTIxDW1hYWKXXt9kqv7fkcsteSaxfSkmhiIiIuD2DRz7ZhYeHV2pXcUVCQkLsVb5yVqsVi8VCSEiIQ3uNGjUqXPaWW26xL2u1WgkI%2BPmQrFOnTlGzZs1f1LeL0UQTERERcXulpeZ/nNGyZUuOHj3KyZMn7W0pKSk0adLEIbkrXzY1NdX%2Bd0lJCbt27aJNmzbUr1%2BfoKAgh/v37t1LYWEhLVu2dK6T51FSKCIiImJY8%2BbNadWqFXPmzCE3N5d9%2B/axbNkyhgwZAkBsbCzffPMNAEOGDOGDDz5g%2B/bt5Ofn88orr%2BDj40PPnj3x9PRk0KBBvPrqqxw9epTs7GxefPFFevfuTWhoqNE%2Ba/exiIiIuD1X7D521vz58/nTn/5E165dCQwM5N577yU%2BPh6A9PR0%2B3kIu3fvzqOPPkpiYiInTpygVatWLF68GD8/PwAmTpxIXl4eAwYMoLi4mF69ejFjxgzj/VVSKCIiIuICtWvXZsmSJRXet2fPHoe/4%2BPj7Qnj%2BXx8fJg%2BfTrTp0833sdzKSkUERERt1cVK4XuRkmhiIiIuD0lhc7TRBMRERERUaVQRERE3J8qhc5TpVBEREREVCkUERER96dKofOUFIqIiIjbU1LoPO0%2BFhERERFVCkVERMT9qVLoPFUKRURERERJoWnvv/8%2BJ0%2BevNrdEBERua6Ulpr/ud4oKTSopKSEWbNmkZ2dfbW7IiIicl1RUug8o0nh4cOHGT16NNHR0XTs2JHHH3%2Bc3NxctmzZQrt27diwYQMxMTFERUUxb948UlJSiIuLIyoqivHjx1NUVATAsGHDePHFF0lMTKRt27b06NGD9evXX/B4paWldOvWjX/%2B858O7WPGjOGpp54C4IsvvuDuu%2B8mKiqK3/zmN8yfP9%2B%2B3D/%2B8Q/uvPNOZs2aRdu2bTl27JhDnGnTpvHHP/6RYcOGceeddwKQkZHBAw88QHR0NNHR0Tz66KPk5OQA0KlTJ06fPs2AAQNYuHAhAJs3/z97dx4XVb3%2BAfwDyCYIigEqghopUsquXpVk0wuKgvlDcStNwQpxwQzTLJcyrdxIwbUuWurFm14x3NHMXLJMb%2BC%2BlkCyySL7en5/jEyMDArOl4bJz/v14nWYc2aeeThzmHnm%2BZ7lDIKDg%2BXPHx0dLWhtExEREYkj7EATSZIQFhYGFxcXrFq1CsXFxZg1axY%2B%2BeQTDB06FCUlJThz5gz27duHQ4cO4d1338W1a9cQGxuL/Px8BAQE4NixY/D19QUA/Pvf/8ann36KTz/9FDt37kRERAROnDgBMzMz%2BXNqa2vjn//8JxITE/HKK68AAIqLi3Hq1Cls2rQJxcXFmDZtGubNm4egoCBcv34do0ePRo8ePeDt7Q0AyMzMhL6%2BPn7%2B%2BWfo6urW%2BbuOHj2KpUuXwtPTEwAwf/58WFlZ4YcffkBhYSEmT56MmJgYvPvuu4iPj4ePjw/i4%2BNha2uL9PR0hIWFYcGCBRg2bBhu3ryJkJAQ2NjYYNiwYQ1et5mZmcjKylKYZ25sDAsLi0a9Ro%2BlpfXnVFvcdwUdHWGhAPyZmsAU/2RgIDaenp7iVBQjI7HxDA0VpwKZm4uL1aaN4lQoZ2ex8bp3V5yK0qWL2HhWVopTkURup024jcLRUWy8bt0Up81VU%2BX5669i4zXCs9jZE01YUZicnIwbN25gx44dMDQ0hKGhIaZNm4bJkyfD398f1dXVGDt2LAwNDeHt7Q1JkuDr6wszMzOYmZnh%2Beefx%2B%2B//y6P5%2BTkJC/Exo4dizVr1uDkyZMICAhQeN7BgwcjNDQUpaWlMDAwwA8//AATExP06tUL2traOHHiBIyMjKClpQU7OzvY2dnh4sWL8qKwoKAAoaGhSgtCALCysoKXl5f89saNG6GlpQU9PT2YmZnh5Zdfxvnz55U%2BNiEhAV27dsXw4cMBAHZ2dhg9ejTi4%2BMbVRTGxcXJO481wqdOxbTp0xsco8EEF0YmTfAeDgDGxk0Q1ETwB26NpvjAbQqiCxgAYwXXWgAweLD4mBir/H9YZdu3N01c0WbMUHcGDdO1q/iYJ06IjwkAmzc3TVzRROdpaio2Hv2lhBWFKSkpqKqqQp8%2BfRTmV1VVyfexa9%2B%2BPQBAX18fAGBpaSm/n76%2BPsrKyuS3u9T6RqytrY327dsjMzOzzvO6urrC2NgYJ0%2BexMCBA3HkyBH4%2BflB%2B2Er6cCBA4iNjUVaWhqqq6tRUVEBNzc3%2BeNNTExg/JgKw%2BqRD/SLFy9ixYoVuHbtGioqKlBVVYUePXoofezdu3eRnJyMnj17yudJkqTwtzVEcHCwvIitYW5sDJSUNCrOY2lpyQrC0lJAkoSFfVAhtirU1pYVhIWF4r8Vmty/Izagnp6sIExLA8rLxcXNyxMXC5B1X7p3B65eFbtNAdh%2BRVxV2KaNrCA8cAAQvdvu2OUuYgN27y4rCMeOla1XUQYOFBcLkG2fM2YAUVGy7VSk8ePFxTI0lBWEN24I30YRHi42XrduskIrJAS4fl1sbJE0Jc9GYKdQdcKKQn19fbRs2RIXLlyos%2Bzs2bMAIC/Uajx6u7aqqiqF25IkQatmiPORGL6%2Bvjh69Cg8PDxw/PhxbNy4EYBsf76FCxdi%2BfLlGDRoEHR1dTF27FiFx7do8fhVoFNr/DM/Px9TpkzBmDFjsGnTJhgbG2P16tU4ffq00scaGBjAw8MD69evf%2BxzPImFhUXdoeKiIrH/ATWvhSQJjfvIyyhMdXUTxC4tFRzwofJysbGLisTFqq2kRHjsR/Z6ECI3twniKnnfEuLqVbGxbW3FxaotLQ24I/hLUVNsp02wjTbZcOf162odSm0wTcmzAVgUqk7Ynlk2NjYoLi5GSkqKfF5hYeFTH4lbO051dTXS09PRrl07pff18/PD8ePHcfr0abRq1QrOD/cPSkpKQpcuXTBkyBDo6uqirKwMt27deqp8AOD27dsoKirC5MmT5d3Fy5cv13t/GxsbXL9%2BHVKtzltWVhbKRXaNiIiIiAQQVhR269YNzs7OWLJkCXJycvDgwQMsWLAAkZGRTxXvwoULOH36NMrLy/H111%2BjqKgI/fv3BwCsWLECy5Ytk9/X1dUVOjo62LhxI/z8/OQdRSsrK6Snp%2BPevXvIzs7GwoULYWFhUeco4xoZGRnw8/NTKEhr69ChA7S1tXHhwgUUFxcjNjYW2dnZyM7ORmVlJQwe7o/322%2B/obCwEP7%2B/sjLy0NMTAxKS0uRkpKCSZMmYcuWLU%2B1ToiIiEg5npJGdUKP4VyxYgUkSYKPjw8GDRokP2/f0wgICEBcXBx69%2B6NzZs3IyoqCq1btwYg67bVLuxqhpDPnTsHf39/%2BXxfX18MGDAAQ4YMQXBwMDw9PfHWW28hMTERn332WZ3nrKiowJ07d%2Brt5FlaWmLWrFmYN28evLy8kJ%2Bfj%2BXLl6O8vBxjx47Fc889B19fX8yYMQOrV69GmzZtEBMTg6NHj6JXr14YP348vLy8MGnSpKdaJ0RERERNRUuSBB5VIMirr74KR0dHzJ49W92pNF%2Bi96vR1pbtzF1SIvTrUW652NOn6OgAJibAgwfi9ylsk35FbEADA9kpRO7cEbtP4f374mIBslOHODvL9n0TvF1F/eIuLJa5uey4je3bxe9TOGNm3f2VVeLsDJw/D7i4iN2nMChIXCxAtn1%2B%2BikQGSl%2Bn8JZs8TFMjICHByApCTx731%2BfmLjOTrKjmgeMKB576vXlHnm54uN10CiN2FA/FmgmjthB5oQERERqcuzONwrGi9zR0RERETNs1P41VdfqTsFIiIi0iDsFKquWRaFRERERI3BolB1HD4mIiIiInYKiYiISPOxU6g6dgqJiIiIiJ1CIiIi0nzsFKqORSERERFpPBaFquPwMRERERGxU0hERESaj51C1bFTSERERETsFBIREZHmY6dQdSwKqUm1aS01QVQtmLRqgrilrcXGa/Hw38vYGDAwEBe3QwdxsQBA%2B%2BGAwQsvCH9X7VYgLpaJiWzaqRPQpo24uACAoCCx8bp0kU0HDgRsbcXF/eYbcbEAwNlZNk1MBC5cEBs7PFxcLD092bSiAigvFxcXAGbPFhuvXTvZ9NVXgUGDxMauWQ8iWFrKppMmARkZ4uKqEYtC1XH4mIiIiIjYKSQiIiLNx06h6tgpJCIiIiJ2ComIiEjzsVOoOhaFREREpPFYFKqOw8dERERExE4hERERaT52ClXHTiERERGRGuTl5WHmzJno168f3N3d8d5776G0tLTe%2Bx8%2BfBgBAQFwdnaGr68vdu7cKV%2B2Zs0a2Nvbo2fPngo/2dnZDc6HnUIiIiLSeJrYKXz//fdRXl6OhIQEVFRUYMaMGVi%2BfDnmz59f575JSUmYPXs2Vq5cCU9PT5w6dQpTp07F888/Dzc3NwBAYGAgli1b9tT5sCgkIiIijdcURWFmZiaysrIU5pmbm8PCwkLl2NnZ2UhMTMR///tfmJmZAQDCwsIwY8YMzJkzB7q6ugr3z8vLwxtvvIGBAwcCADw8PNCtWzecO3dOXhSqikUhERERkRJxcXFYu3atwrzw8HBMmzZN5dhXrlyBjo4O7Ozs5PNeeuklFBcX4/bt2wrzAWDAgAEYMGCA/HZlZSWysrJgWXPJQgDXrl3D6NGjcf36dbRv3x5z586Fu7t7g3NiUUhEREQaryk6hcHBwfD29laYZ25uLiR2Xl4ejI2NoaWlJZ9namoKAMjNzX3i45cvX46WLVtiyJAhAIB27drB2toab7/9NiwsLBAXF4c333wTe/fuxfPPP9%2BgnFgUEhERESlhYWGh0lBxfHw8IiMjlS6LiIiAJEmNjilJEpYvX46EhARs3boV%2Bvr6AICRI0di5MiR8vtNnDgR%2B/btw969ezFz5swGxWZRSERERBqvOR5oEhgYiMDAQKXLTp06hcLCQlRVVUFHRweArHsIAG3btlX6mOrqasydOxdJSUnYsWMHrK2tH/v8VlZWyMzMbHC%2BPCUNERERabzqavE/Tcne3h6SJOHq1avyecnJyTAxMUGXLl2UPubjjz/GjRs3lBaEMTExOHPmjMK8W7duPbFwrI1FIREREdFfzMzMDL6%2Bvli9ejVycnKQnp6O6OhoBAUFoUUL2UDuhAkTsH//fgDAL7/8gr1792Ljxo1o3bp1nXh5eXlYtGgRbt%2B%2BjbKyMnz55Ze4e/cuXnnllQbn9LcvCmNiYjB%2B/HjhcX19ffGf//xH6bKysjLY2dnh7Nmzwp%2BXiIiI6tK0TiEALF68GK1atYKPjw8CAgLg4OCAiIgI%2BfKUlBTk5%2BcDAHbt2oWCggJ4eXkpnJx60qRJAIC3334bAwYMwMSJE9GrVy8kJCQgNjYW7dq1a3A%2Bf/t9CsPCwhAWFiY87qFDh4THJCIiomdHq1atsHLlynqXHzt2TP77xx9/jI8//rje%2B%2Brr62PevHmYN2/eU%2Bfzty8KiYiI6O%2BvOR5oomkaPXyckpKCSZMmwdnZGV5eXti6dSsAID09HW%2B99Rb69OkDV1dXREREyI%2BiOXv2LFxdXXHixAn4%2BfnByckJkydPlrdE79y5g4kTJ8LNzQ29evVCeHh4vefosbOzw%2B7duxEUFAQHBwcMHz4ct2/fBgCkpqbCzs4O27dvR%2B/evZGQkIA1a9Zg1KhR8sefPHkSAQEBcHJyQmBgoMJOmWfOnEFwcDCcnZ3x8ssvIzo6ut714O3tjR07dgAAiouLMWvWLLi5uWHgwIEKlf3Jkyfh6uoqPyN6WVkZfHx8EBsb29hVT0RERPXQxOHj5qbRncLw8HD07t0b0dHR%2BO233zBu3DjY2tpixYoVeOGFF3D06FGUlpZixowZWLBgAaKiogAAJSUl2LdvH%2BLi4lBSUoKgoCDs3LkToaGh%2BPDDD%2BHi4oLNmzejqKgIc%2BbMwbp16%2Bptgf7rX//C6tWr0a5dOyxYsACzZs3Cnj175Mt/%2BuknHDt2DEZGRgpnIs/IyMC0adOwZMkSDBo0CN9%2B%2By2mTp2KY8eOobS0FGFhYViwYAGGDRuGmzdvIiQkBDY2Nhg2bNhj18n69etx9epV7Nu3D/r6%2BliwYIF8mbu7O7y9vbF8%2BXJ88skn%2BOKLL9C2bVu89tprDV7nSi%2BzY2ws5DI7cjUnz9TSArT/9ruaKtdCcOO8Jp7ouKJfn5p4TfC6m5iIi2VkpDgVqp4j/Z6alZXiVBRnZ7HxundXnIpkbCwulqGh4lSkRuxv1SA1pxKp55QiKnnksmcqeXhZNflUlIwMsfHoL9WoT6vLly/j2rVr2LJlCwwNDWFvb4%2B1a9fC0NAQly5dwoYNG2BsbAxjY2NMmTIFU6dORXl5OQCgqqoKISEhMDU1hampKVxdXeUdvgcPHsDAwAAtWrSAqakpYmJioP2YD6jAwEDY2toCAEJCQhAYGIiMWhvi8OHDYazkDenAgQOwtraWn/17xIgR0NfXR3V1NRISEtC1a1cMHz4cgKwjOXr0aMTHxz%2BxKDxy5AjGjh0rv9RMaGgoDh48KF8%2Bd%2B5c%2BPv748CBA/jXv/6Fbdu2Pfbve5TSy%2BxMnYpp06c3OEaDGRiIj9kUap0BXhhBZ6mvo02bpokrWhNUW/37Cw8JJyfxMdH/0yYICmDGjKaJK9r27erOoGFefFF8TFdX8TEBYMSIpokrWkCA2HiffCI2XiM8i5090RpVFN69exfGxsYKh0L369cPR44cgampqcKlX2xsbFBRUaFQrHXs2FH%2Bu6GhIUpLSwHIuo/vvPMO9uzZA3d3dwwdOhQODg715lH7/D1WD7%2BJZ2RkyC8o3aFDh3rzr50DAPj7%2B8uXJScno2fPnvJlkiTVe66g2tLT0xXidu7cWWG5mZkZ5syZg4iICLzxxhvo1q3bE2PWpvQyO8bGQElJo%2BI8lpaWrCAsLQWe4gzr9WqKIlNLS2yONbKzxcZr0UJWEObmApWV4uKKXqfa2rKCsKhI%2BLvqqaRWwmIZGckKwv/9T5aqSP3jlV9x4KlZWckKwqgoIC1NXNzERHGxAFmHcPt2YOxYoNa50oTYtElcLENDWUF4%2BbLY9z0AOH9ebLy2bWUF4e7dwP37YmOL7hQGBAB79wI5OeLikkZrVFGora2NaiUfGjXdQGVqX9Ovvu6Yp6cnjh8/ju%2B//x5Hjx7F%2BPHjERkZWe%2BpZGrnUHOJmNrPU3Nm8IbmDwAGBgbw8PDA%2BvXr6/1b6lNRUYGqqqo6OdWWmpoKQ0ND/Pbbb42Or/QyO6I/wGteG0l6dr9uiSzcHo0rMnZTvT5NsBPNgwdCwwGQbfrC4965IzjgQ2lpYmNfuCAuVm1Xr4qPXVgoNh4gKwhFx01PFxuvxv374mPr6YmNB8gKwr/JkO%2Bz%2BtElUqN2IrK2tkZRUZHCJVMSExNhbm6O/Px8ZNfqtNy%2BfRv6%2BvryIdXHyc3NhZGREYYMGYIVK1Zg0aJFiIuLq/f%2Bd%2B/elf/%2Bxx9/AECDzsPTsWNH3HnkDfrrr79GSkoKbGxscP36dYWCLisr67EFbw0LCwvcu3dPfvvmzZsKy2/duoXY2Fhs374dv/zyCxJFf9snIiJ6xvFAE9U1qii0t7fHiy%2B%2BiNWrV6OoqAjXr1/He%2B%2B9h5KSEvnBJsXFxcjIyMC6devg7%2B8P3Se0u0tLS%2BHr64v4%2BHhUVlaitLQUly5dgo2NDYA/99erLT4%2BHr///juKioqwadMm9OjRQ2Houj5Dhw7FvXv3sHPnTpSXl2Pfvn1YuXIljIyM4O/vj7y8PMTExKC0tFR%2BlPWWLVsAAElJSfDz81NaJL788svYuXMnsrKykJOTg82bN8uXSZKEDz74ABMnToS9vT0iIyOxaNEiFBQUPDFfIiIior9Kow83XL9%2BPdLS0tCvXz%2B8%2BeabCAsLg4eHB2JiYpCZmQlPT0%2BMGjUKjo6O%2BOCDD54Yz8DAAFFRUYiNjYWbmxs8PT2Rnp4uf2xBQQF%2B//13hccEBQXh7bffRt%2B%2BfXHz5k2sWLGiQbk/99xz%2BOKLLxAbG4tevXph48aNiI6OhpmZGdq0aYOYmBgcPXoUvXr1wvjx4%2BHl5SU/U3hJSQnu3LmjdGj4nXfeQZcuXeDn54egoCC88sor8kvUxMXFISMjA6GhoQCAgIAAdO7cGZ999lmDciYiIqInY6dQdVqSsiqnGbOzs8OmTZswYMAAdaeiXqL3tNfWlu3MXVIi9j%2BhZUtxsWo01YEmovf/adFCdkRzVpbYfQpFr1NtbaBVK6CgQPi74IHTpsJimZjIjmY%2BdUr8PoWDvxwpNmCXLsCnnwKRkWL3KfzmG3GxANkpbs6fB1xcxO9TePy4uFjGxrKjhH/5Rfw%2BhSdOiI3Xrh0QGio70KY571NoaQlMnAjExorfp3DOHLHxGmjXLvEx/%2B//xMdsznhFEyIiItJ4z2JnTzQWhURERKTxWBSqTuOKwmvXrqk7BSIiIqK/HY0rComIiIgexU6h6lgUEhERkcZjUai6Rp%2BShoiIiIj%2BftgpJCIiIo3HTqHq2CkkIiIiInYKiYiISPOxU6g6FoVERESk8VgUqo7Dx0RERETETiERERFpPnYKVcdOIRERERGxU0hERESaj51C1bEo1FQ5OWLj6eoChoZAfj5QUSEsbJWBkbBYNXR0gKpqLeFxtdu1Fx5TC4D0nLnQmKJfeh0doDWAvKpWqKoSG7tHD3GxdHVlU1tboZuozKxZYuMZPdzux48HiorExQ0PFxcLAIyNZdNNm4DCQrGxPT3FxXJ2Bs6fB0JDgQsXxMUFgBMnxMaree1dXcW%2B9gDQsaO4WHp6sumgQUB5ubi4asSiUHUcPiYiIiIidgqJiIhI87FTqDp2ComIiIiInUIiIiLSfOwUqo5FIREREWk8FoWq4/AxEREREbFTSERERJqPnULVsVNIREREROwUEhERkeZjp1B1LAqJiIhI47EoVB2Hj4mIiIiInUIiIiLSfOwUqo6dQiIiIiJip5CIiIg0HzuFqmNRSERERBqPRaHqOHz8GEuWLIGzszM2btxYZ5mvry/%2B85//KH1cWVkZ7OzscPbs2aZOkYiIiEgIdgrrkZeXh61bt2LdunXw9vaus/zQoUNqyIqIiIiU0cROYV5eHhYuXIiffvoJ2tra8PDwwPvvvw8DA4M69929ezfmzZsHXV1dhfnbtm2Dg4MDqqurERUVhYSEBDx48AAODg5YuHAhrK2tG5wPO4X1KCoqAgB06tRJzZkQERHR39H777%2BPkpISJCQkYNeuXbh16xaWL19e7/179eqF5ORkhR8HBwcAsuLw22%2B/xcaNG/Hdd9%2Bhc%2BfOmDp1KiRJanA%2BKheFKSkpmDRpEpydneHl5YWtW7cCANLT0/HWW2%2BhT58%2BcHV1RUREBPLy8gAAZ8%2BehaurK06cOAE/Pz84OTlh8uTJyM/PBwDcuXMHEydOhJubG3r16oXw8HDk5uYqff6cnBxMnz4drq6ucHd3x8qVK%2BUrID8/H5GRkXB3d4ezszOmTJmC1NRUAEBqairs7Oxw6tQpDB8%2BHE5OThg9ejRSU1Nx584d%2BPr6AgACAwMRExNT53m9vb2xY8cOAEBxcTFmzZoFNzc3DBw4EMeOHZPf7%2BTJk3B1dUVWVhYA2dCyj48PYmNjVV31RERE9FB1tfifppSdnY3ExERERETAzMwMlpaWCAsLw65du1BRUdHoeHFxcZg4cSJsbW1hbGyMiIgI3Lp1C7/%2B%2BmuDY6g8fBweHo7evXsjOjoav/32G8aNGwdbW1usWLECL7zwAo4ePYrS0lLMmDEDCxYsQFRUFACgpKQE%2B/btQ1xcHEpKShAUFISdO3ciNDQUH374IVxcXLB582YUFRVhzpw5WLduHebNm1fn%2BefPn48WLVrg%2B%2B%2B/R15eHsaPH4%2BOHTti1KhRmD9/PgoLC7F3717o6elh3rx5mDlzJr755hv547du3YoNGzZAX18fr732GjZv3oyFCxfi4MGD8PHxQXx8PGxtbR%2B7DtavX4%2BrV69i37590NfXx4IFC%2BTL3N3d4e3tjeXLl%2BOTTz7BF198gbZt2%2BK1115r8DrOzMyUF5U1zKurYWFu3uAYT9SiheKUmi0dnaaJJzouADwyyqGSJt1EjYzExjM0VJyKoqcnNl5T5QkAzs7iYnXvrjgVSVNee0Ds61/zzynynxQAysvFxmuEpijilH7%2BmpvDwsJC5dhXrlyBjo4O7Ozs5PNeeuklFBcX4/bt2wrza9y7dw%2Bvv/46Ll68CBMTE0yfPh2BgYEoLS3FzZs38eKLL8rva2xsjE6dOiE5ORlOTk4Nykmlt9fLly/j2rVr2LJlCwwNDWFvb4%2B1a9fC0NAQly5dwoYNG2BsbAxjY2NMmTIFU6dORfnDDaaqqgohISEwNTWFqakpXF1dcfv2bQDAgwcPYGBggBYtWsDU1BQxMTHQ1q7b1MzNzcV3332HXbt2yZ9n1apVaNGiBfLy8nDkyBHExcXBzMwMADB9%2BnT4%2B/sjJSUFWlpaAIAxY8bA0tISgKyAS05ObvR6OHLkCMaOHSuPExoaioMHD8qXz507F/7%2B/jhw4AD%2B9a9/Ydu2bUr/nvrExcVh7dq1CvPCp07FtOnTG53rEz33nNBwTVBnyOI2VeAm8HBTE6Z1a7HxarRq1TRxRRO8icq0c2iCoAC6dm2auKLV%2BiAR5vx58TG3bxcfs6nY26s7g4YRUNwouHNHbDw1U/r5Gx6OadOmqRw7Ly8PxsbG8noEAExNTQFA6eiomZkZOnfujFmzZuGFF17AkSNHEBkZCQsLCzz//POQJEn%2B%2BNrx6htpVUalovDu3bswNjZG61qfUv369cORI0dgamoK81qdLBsbG1RUVCAjI0M%2Br2PHjvLfDQ0NUVpaCkC2wt955x3s2bMH7u7uGDp0qHzMvLbU1FRUV1crxHF%2B%2BO308uXLkCRJoctnY2MDAEhLS5M/5tGgjCvUAAAgAElEQVQcysrKGr0e0tPTFeJ07txZYbmZmRnmzJmDiIgIvPHGG%2BjWrVuj4gcHB9c52MW8uhpIT290rvVq0UL2aZudDVRWCgtbZd5OWKwaOjpAVZXwsGhEnd5gWlpAI3bnaJCHe1kIo6MjKwgLCsSv14f/0kI00SYKAGiXmSQ2oKGhrCC8cQMoKREX9ymGlB7L0FBWEF6%2BLDZPAAgNFRere3dZQTh2LHD1qri4ALB5s9h4hoaygvDKFfHr9GHjQQhdXVlBmJkpfrtSk6boFCr9/G3EKF18fDwiIyOVLouIiGjU/n6enp7w9PSU3/b398eRI0ewe/duzJ49GwAaFU8ZlYpCbW1tVCt5Fcof0z6uXRHX1y3z9PTE8ePH8f333%2BPo0aMYP348IiMjMX78%2BDrPD0ClHLQEtHEqKipQVevTVNmLkpqaCkNDQ/z222%2BNjm9hYVG3VZ2S0jT/yJWVf5s3iL%2BrpiiIa%2BKKjq0xm%2BjDA8uEKykRG7uphuZKSoDCQrExL1wQGw%2BQFYSi42rKaw80zetfUaHWId/mTunnbyMEBgYiMDBQ6bJTp06hsLAQVVVV0Hk4/FVz7EXbtm0bFN/KygoXL15E69atoa2tLX98jby8vAbHAlQ80MTa2hpFRUXIzMyUz0tMTIS5uTny8/ORnZ0tn3/79m3o6%2BvLh1gfJzc3F0ZGRhgyZAhWrFiBRYsWIS4urs79rKysoK2tjTu12tU//vgjjh07Jj8Eu2ZIuvbvNR1DUSwsLHDv3j357Zs3byosv3XrFmJjY7F9%2B3b88ssvSExMFPr8REREzzpNO9DE3t4ekiThaq3ud3JyMkxMTNClS5c699%2BxYwf279%2BvMO/WrVuwtraGvr4%2BunbtikuXLsmXPXjwAHfv3lU60loflYpCe3t7vPjii1i9ejWKiopw/fp1vPfeeygpKZEfbFJcXIyMjAysW7cO/v7%2Bdc6v86jS0lL4%2BvoiPj4elZWVKC0txaVLl%2BSFXM3%2BewDQunVr%2BPj4IDo6Gnl5efjjjz/w/vvvIyMjA23btoW7uzuioqKQl5eH/Px8rF69Gn369EH79u0b/bcmJSXBz89PaQfy5Zdfxs6dO5GVlYWcnBxsrjUcIUkSPvjgA0ycOBH29vaIjIzEokWLUFBQ0OgciIiISDlNKwrNzMzg6%2BuL1atXIycnB%2Bnp6YiOjkZQUBBaPDyibsKECfJCsLy8HB9%2B%2BCGSk5NRUVGBhIQEnDhxAqNHjwYgO0Zi69atuHXrFgoLC7F8%2BXLY29ujZ8%2BeDc5J5b2o1q9fj7S0NPTr1w9vvvkmwsLC4OHhgZiYGGRmZsLT0xOjRo2Co6MjPvjggyfGMzAwQFRUFGJjY%2BHm5gZPT0%2Bkp6fLH1tQUIDff/9dfv%2BlS5eiZcuW8PLyQnBwMPz8/BAcHAwA%2BOSTT9CyZUsMHjwYQ4YMgbGxsfzo58YqKSnBnTt3lA4Nv/POO%2BjSpQv8/PwQFBSEV155Rf6CxsXFISMjA6EP968JCAhA586d8dlnnz1VHkRERPT3sHjxYrRq1Qo%2BPj4ICAiAg4MDIiIi5MtTUlLkp%2Bt77bXX8Oqrr2LGjBlwcXFBdHQ0oqOj0aNHDwDA6NGj8corr%2BDVV19F//79kZ6eXucgmSfRklTdK5HUIyVFbDxdXaBdO9nBKwJ32Krq0PAzqTfUs36gSU6O2Hg6OrIjmvPyxK/X4mJxsZpoEwUAWKeeERvQyAhwcACSkpr3PoXGxoCrK/DLL%2BL3Kay1Q7zKnJ1lRzO7uIjfp/DECbHxjIxkeZ4/L36fwloHNKpMTw%2BwsgLS0sRvV0qGPv8Kc%2BeKj7l0qfiYzRmvaEJEREREvPYxERERaT5NvPZxc8OikIiIiDQei0LVcfiYiIiIiNgpJCIiIs3HTqHqWBQSERGRxmNRqDoOHxMRERERO4VERESk%2BdgpVB07hURERETETiERERFpPnYKVceikIiIiDQei0LVcfiYiIiIiNgpJCIiIs3HTqHq2CkkIiIiInYKNVbr1mLjaT/8ftCqldCvWzk5wkIBAFq0ANq0AR48ACorxcY2f3BLbEA9PcDaGlqpKUB5ubCwbW/eFBYLgOw179cPrS%2BfBgoKhIb%2BMslXWCwLC2DCBODQISAzU1hYAMA7H/mJDejoCJw4AYSHA7/%2BKi7u7NniYgFAu3aAqytw/jyQni429okT4mIZGcmmmzcDRUXi4gLAgAFi4zk7y9ZnSAhw4YLY2G3aiIvl4AAcPw6MGwckJYmLC4h/428gdgpVx6KQiIiINB6LQtVx%2BJiIiIiI2CkkIiIizcdOoerYKSQiIiIidgqJiIhI87FTqDoWhURERKTxWBSqjsPHRERERMROIREREWk%2BdgpVx04hEREREbFTSERERJqPnULVsSgkIiIijceiUHUcPiYiIiIidgqJiIhI87FTqDp2ComIiIiInUIiIiLSfOwUqo5FIREREWk8FoWq%2B1sOH/v6%2BuI///kPAODVV1/F8uXL1ZwRERERUfP2t%2BwUHjp0qEnjb9u2DVu3bkVGRgbMzc0xevRoTJ48uUmfk4iIiOrHTqHq/pZFYVNKTEzE559/jk2bNqFHjx44f/48Jk2ahE6dOmHgwIHqTo%2BIiIjoqTSb4eO0tDS8%2Beab6NOnD3r16oXIyEgUFhZi9%2B7d6N%2B/v8J9R40ahTVr1gAA1qxZgzfeeAMzZ86Ei4sLAMDb2xs7duyQ37%2B0tBRvv/02nJ2dMWjQIBw8eFC%2BLD8/H5GRkXB3d4ezszOmTJmC1NRUAEBqairs7Oywfft29O7dGwkJCbCwsMCqVavg4OAAbW1tuLm5wdbWFjdu3AAAvPvuu1i0aBE%2B%2BOADODs7w8fHB%2BfPn8fGjRvRt29f9O3bF7t3727SdUlERPSsqa4W//OsaRadQkmSEBYWBhcXF6xatQrFxcWYNWsWPvnkEzg7Oz/x8f/73/8wY8YMrFixQuny%2BPh4LFu2DEuXLsWOHTswe/ZsODs7w9LSEvPnz0dhYSH27t0LPT09zJs3DzNnzsQ333wjf/xPP/2EY8eOwcjICFpaWvL5FRUVSExMREpKCry8vOTz9%2B/fj2XLluG9995DeHg4Zs2ahZEjR%2BL777/H5s2b8fHHH2P48OHQ1m5YTZ6ZmYmsrCyFeeYtW8LCwqJBj2%2BQmlwamFNDtRC8henoKE6F0tMTG09XV3EqSqtWYuMZGSlOBRK5iZqZKU6FcnQUG69bN8WpKO3aiY3Xtq3iVCSR25OhoeJUpAZ8xjRK9%2B6KU5FMTMTF6tpVcSpKUpLYeI3wLBZxojWLojA5ORk3btzAjh07YGhoCENDQ0ybNg2TJ0%2BGk5PTEx%2Bvo6ODMWPGKBRstTk4OMDHxwcAMH78eMTExOD06dPw8vLCkSNHEBcXB7OHnzTTp0%2BHv78/UlJS5PGGDx8OY2NjhZgxMTFYs2YNWrdujWXLlqF7rTeAzp07y4vE/v374%2BzZswgNDYWenh68vLwQFRWF%2B/fvw9zcvEHrJy4uDmvXrlWYFz51KqZNn96gxzeK4DfdNuLrDABi3xvl2lg3QVCI/yC3bqI8RRdGACb0Ex4Sw4aJj4kJJ5ogKIDNm5smrmgjRqg7g4axtxcf8/x58TEBYPv2pokr2qZNYuM1ybc2%2Bqs0i6IwJSUFVVVV6NOnj8L8qqoq5ObmPvHx7dq1q7cgBIAXXnhB/ruOjg6srKyQkZGBP/74A5IkwdbWVr7cxsYGgGw4u2PHjgCADh061IkZFhaGkJAQnDx5EnPnzoWuri48PDzk%2BdTQ19eHmZkZ9B52oWqmZWVlT/y7agQHB8Pb21thnnnLlkBRUYNjPJG2tqwgLCkR%2BnUrt1xsVaijIysIHzwAqqqEhkabwhSxAXV1ZQVhejpQUSEuborgPI2MZAXhr7%2BK3aYAbLkhrio0M5MVhN9%2BC%2BTkCAsLAJjwxQCxAbt1kxWEISHA9evi4r76qrhYgKxDOGIEsHs3cP%2B%2B2NiuruJiGRrKCsIrV2TvUSKFhIiN1727rCAcOxa4elVsbNGdwk2bgNBQ4OHuT5qOnULVNYuiUF9fHy1btsSFCxfqLFO2/13VI9VAiyeMUSobptXX10d5eXm9j6ldZOrUM1app6cHb29v%2BPr6Yvv27fKi8NHna%2BgwcX0sLCzqDhUXFDTNf4DgHSkqK4WFUlBV1QSxH7M9qKSiQmzsggJxsWorKhIeOzNTaDgAsoJQeNxffxUc8KHr18XGHjRIXKza7t%2BXfXkRSfAXDACyglB0XCWfO0JcvSo%2Bdps2YuMBsoJQjUO%2B1Lw0iwNNbGxsUFxcjJRaHZDCwkLk5uZCX18fJbW%2BGVZVVSEtLa1R8e/cuVPn8ZaWlrB%2BOAx3%2B/Zt%2BfKa32s6ho9auHBhnfMeamlpPbEwJSIioqbDA01U1yyKwm7dusHZ2RlLlixBTk4OHjx4gAULFiAyMhKdOnVCUVERTp48ifLycmzYsAGSJDUq/vnz53Hq1ClUVFTg3//%2BN0pLS%2BHu7o62bdvC3d0dUVFRyMvLQ35%2BPlavXo0%2Bffqgffv2SmP17t0b27dvx9mzZ1FVVYXz589j3759CgeaEBER0V%2BLRaHqmk17a8WKFVi8eDF8fHygp6eHvn37YtmyZWjbti0mTpyIiIgI6OjoYNKkSQ06Irm2UaNGYefOnQgPD5efUsbk4b4Zn3zyCRYtWoTBgwdDW1sbffv2xdKlS%2BuNNWTIEOTn52Pu3LnIzs5Gu3bt8OabbyIoKEilv5%2BIiIhInZpNUWhlZYUNGzYoXTZ37lzMnTtX6bJp06Zh2rRpCvOOHTsm//2rr7567PM%2B99xz8nMePqpjx464du1anfljxozBmDFjlD5m2bJlj72vra2t0phERET09DSxs5eXl4eFCxfip59%2Bgra2Njw8PPD%2B%2B%2B/DwMCgzn3nz5%2BP%2BPh4hXlVVVUIDAzE0qVL8e6772Lv3r0Kx0Ho6%2Bvj3LlzDc6nWQwfExERET1r3n//fZSUlCAhIQG7du3CrVu36hy3UOOjjz5CcnKy/OfChQt4/vnn4efnJ7/PW2%2B9pXCfxhSEAItCIiIi%2BhvQtH0Ks7OzkZiYiIiICJiZmcHS0hJhYWHYtWsXKhpwGrMtW7agQ4cO8jOfiNBsho%2BJiIiInlZTFHFKryhmbi7kimJXrlyBjo4O7Ozs5PNeeuklFBcX4/bt2wrzH/XgwQOsX78e2x85SfqPP/6Io0eP4vfff4etrS0WLlyIHj16NDgnFoVERERESii9olh4eJ1jGZ5GXl4ejI2NFc6LbGpqCgBPvHDH119/jV69eqFrrcsUWltbQ1tbGzNmzICRkRHWrl2LSZMm4dChQ2jTwHNcsigkIiIijdcUnUKlVxRr4CVqASA%2BPh6RkZFKl0VERDT6FHuA7OCSbdu2YcWKFQrzp06dqnD7nXfeQUJCAhITEzFy5MgGxWZRSERERKSE0iuKNUJgYCACAwOVLjt16hQKCwtRVVUlP2I4Ly8PANC2bdt6Y/78888oLy%2BHm5vbY59bR0cH7du3R2YjLgHFA02IiIhI42nagSb29vaQJAlXa10jOzk5GSYmJujSpUu9jzt69Cj%2B8Y9/KFxJTZIkLF26VCFWeXk57t69K796W0OwKCQiIiKNp2lFoZmZGXx9fbF69Wrk5OQgPT0d0dHRCAoKkhd8EyZMwP79%2BxUed%2BXKFXTs2FFhnpaWFlJTU7Fo0SJkZGSgqKgIy5cvh66uLgYOHNjgnFgUEhEREanB4sWL0apVK/j4%2BCAgIAAODg6IiIiQL09JSUF%2Bfr7CY7KysvDcc8/VibVkyRJ07twZI0aMQL9%2B/XDlyhVs2bIFLVu2bHA%2B3KeQiIiINJ4mXtGkVatWWLlyZb3La1%2BhrcahQ4eU3rd169aPvUxvQ7AoJCIiIo2niUVhc8PhYyIiIiJip5CIiIg0HzuFqmOnkIiIiIjYKdRY2oLr%2BZp4guNWVgoNpxBXeGzRXzNrzlQvSWJjN%2BBC6Y1SsyIrK4XHbiHwHaYmVosWYuNqFD09sfF0df%2Bcio79yCkzVFKTm6UlUF4uLi4ANPDyXw1mYvLnVHTsJ1z6rFEePPhzKjKuGrFTqLpn9a2ViIiI/kZYFKqOw8dERERExE4hERERaT52ClXHTiERERERsVNIREREmo%2BdQtWxKCQiIiKNx6JQdRw%2BJiIiIiJ2ComIiEjzsVOoOnYKiYiIiIidQiIiItJ87BSqjkUhERERaTwWharj8DERERERsVNIREREmo%2BdQtWxU0hERERE7BQSERGR5mOnUHXPbKdwzZo1GDVq1F/%2BvLdu3YKdnR1SU1P/8ucmIiL6u6quFv/zrHlmi0IiIiIi%2BhOHj4mIiEjjPYudPdGEdwrt7Oxw%2BPBhjBkzBk5OThg2bBguX74MADh79iycnZ0RGxsLFxcXXLhwoc7jv/76awwePBiOjo7w9/dHYmKifNmrr76KmJgYhIeHw8nJCUOHDsXt27fx0Ucfwc3NDR4eHjhx4oT8uV566SV899138PHxgYODA8LDw1FUVKQ073PnzmHUqFFwdnaGu7s7Vq1aherqapw7dw49evRAbm6u/L6lpaVwdnbGyZMnn5jz/fv3ERISAmdnZ/j7%2ByMpKUn1lUxEREQkWJN0Cjdv3oxly5ahffv2CA8Px6pVq7Bp0yYAQEVFBX7//XecPn0a%2Bvr6Co87fPgw1q5di82bN6N79%2B44duwYZs6cicOHD6NDhw4AgJ07d%2BLzzz/HsmXLMG7cOEyaNAnTp09HZGQkPvzwQ3z22WcYMGAAAKCyshJ79uzB7t27UVpaitdffx1RUVGYN2%2BewvNmZ2dj8uTJiIyMxMiRI3Hz5k2EhobCwsICY8eOhaWlJQ4ePIgxY8YAAE6ePAkjIyP07dv3iTl//PHHKCsrw/Hjx1FaWorZs2c3en1mZmYiKytLYZ65sTEsLCwaHateWlp/TrXFfVdoIXgLq4knOi4A4JHtUWW6uopTUUxMxMYzNlacCiRyE23TRnEqlKOj2HjduilORbG0FBvPzExxKpKenrhYTfW/BAAODmLjde2qOBXpwQNxsbp3V5yKoqTZ81dhp1B1TVIUBgYG4vnnnwcAeHt744svvpAvq6iowNixY2FgYFDncd988w2CgoLQo0cPAMA///lPuLq6IiEhAVOmTAEAuLi4wOHhP3Hv3r3x3XffYcSIEQAADw8P7NmzRyHm5MmTYWpqClNTU4wePRpff/11naIwISEBHTp0wLhx4wAAL774IgIDA3HgwAGMGzcOgYGB%2BPbbb%2BVF4eHDhzFkyBDo6Og8MefExESsWrVKnsP48ePx008/NWp9xsXFYe3atQrzwqdOxbTp0xsVp0GUvC6qMDcUGk6uSQoD2DRFUKB9e7HxbJooTxcX4SHHCY8IDBnSBEHHnWiCoAA2b26auKIFBKg7g4YR%2BS2jxvHj4mMCwMNGSLO3fbvYeDUNBjVgUai6JikKO3bsKP/d0NAQZWVlCstrun6Punv3Lk6dOoUtW7bI50mShBdeeEF%2Bu127dvLf9fX1YVnrm7Oenh7Ky8sVYtYUpzXPm5mZWed5U1NTYWtrqzCvU6dOOHDgAABg%2BPDhWLduHdLS0mBhYYHjx4/LC93H5Zybm4vS0lKF9dG5c2elf/vjBAcHw9vbW2GeubExUFLS6Fj10tKSFYSlpYAkCQubVSi2KmzRQlYQ5uYClZVCQ8O85K7YgLq6soLw3j2gokJc3N9%2BExcLkHUIXVyA8%2BeBwkKhobelDBAWq00bWUG4f7/s9Rdp3AZxeQKQdQg3bwZCQoDr18XFnTRJXCxA1iEMCAD27gVycsTGHjRIXCxdXVlBmJkp9n8JAMYJ/urStausIAwNBW7cEBtbdKdw%2B3Zg7Fjg6lVxcUmjNUlRqPWEbwot6hn7MzAwwNtvv41Jj3nj035kaPPR24%2Bqqqp6Ym6PFpKP3tfGxgaOjo7Yt28fXnrpJZiZmaFnz55PzDkjI6NODtJTFFwWFhZ1h4qLisR%2BLapZj5IkNK7owq12XOGxH/nyIkxFhdjYIj8YaissFB5byXcwleXmNkHcX38VHPCh69fFxn74niJcTo742PW8r6qkokJ83Kbaz/vGDfGxRX8bAmQFoRqHfEVip1B1zeqUNDY2Nrh27ZrCvD/%2B%2BOOpCqkad%2B/%2B2f1JS0tT6CzWft7bt28rzLt9%2Bzasra3lt4cPH46DBw/iwIEDGDZsWINyNjMzg66uLu7duydfdvPmzaf%2BW4iIiIiaitqLQj8/P5w7dw6AbJh0//79OH78OCorK/Hjjz9i6NCh%2BFWFb9qxsbEoKChAeno64uLi4OXlVec%2BgwcPRkpKCuLi4lBZWYmkpCT897//xSuvvCK/z5AhQ3Dz5s06ReHjctbV1cU//vEPbN26FQUFBUhLS8O2bdue%2Bm8hIiIi5XjyatWp/TyFd%2B7cQXFxMQCgf//%2BmDNnDhYvXozs7Gx07NgRCxcuhJOT01PH9/HxwfDhw5GZmQkPDw9MV3JwhpWVFdauXYuoqCgsW7YMFhYWmDFjBoYPHy6/j4mJCTw9PZGRkQGbWjv6PynnJUuWYM6cORgwYADat2%2BP6dOn4/z580/99xAREVFdz2IRJ5rwovDRodQRI0bIjw7u06dPneWP3h4/fjzGjx%2BvNPZXX32lcPvR07sMGDCgTjwfHx8MUXK44rRp0zBt2jT5bQ8PD3h4eCh93ho5OTkICgqqM/9xOVtaWiI2NlZh3qM5EhEREamb2juFmkCSJOzYsQNpaWkKQ8dERETUPLBTqDoWhQ3g6OgIa2trREVFKT2/IhEREZGm%2B9sWhcqGqp8WL01HRETUvLFTqLq/bVFIREREzw4WhapT%2BylpiIiIiEj92CkkIiIijcdOoerYKSQiIiIidgqJiIhI87FTqDoWhURERKTxWBSqjsPHRERERMSikIiIiDRfdbX4n79CcnIyBg0ahFGjRj3xvlu3boWvry9cXFwwZswYXLx4Ub6srKwMH3zwAQYMGIA%2Bffpg%2BvTpyM3NbVQuLAqJiIhI42liUbh3715MmzYNnTp1euJ9jx07hjVr1uDTTz/F6dOn4eXlhTfffBPFxcUAgFWrVuHSpUuIi4vDoUOHIEkS5s6d26h8WBQSERERqUFZWRni4uLg6Oj4xPvGxcVhxIgRcHR0hIGBAUJCQgAA3333HSorK/HNN98gLCwM7du3R%2BvWrTFz5kwcP34cGRkZDc6HB5oQERGRxmuKzl5mZiaysrIU5pmbm8PCwkJI/JEjRzb4vpcuXcKQIUPkt7W1tWFvb4/k5GTY29ujoKAAL730kny5ra0tDAwMcOnSJVhaWjboOVgUaiojI6HhMjMzEbd5M4KDg4Vt7ADQXmyayMzMxJo1cQgODkb79uLylOkqNFpmZibi1qyRrVMbG3GBuzZhngJfewCIEBir9msvOk9E5AsNJ1%2Bn33wjPleBmvK1F0khTysrscFzcoSGk%2Be6c6dmrNODB5t1no0hSeJjrlkTh7Vr1yrMCw8Px7Rp08Q/2RPk5eXB1NRUYZ6pqSlyc3ORl5cHADAxMVFYbmJi0qj9Cjl8TACArKwsrF27ts43ouZGU/IENCdX5imepuTKPMXTlFw1JU91Cw4Oxu7duxV%2BgoODG/z4%2BPh42NnZKf3ZvXt3o/ORnlD5Pmn5k7BTSERERKSEhYWFSp3UwMBABAYGCsmlTZs28o5gjby8PHTt2hVmZmby20a1RhLz8/PRtm3bBj8HO4VEREREzVyPHj1w6dIl%2Be2qqipcvnwZjo6OsLa2hqmpqcLy69evo7y8HD169Gjwc7AoJCIiImqG/Pz8cO7cOQDAmDFjsGfPHvzvf/9DSUkJ1q1bBz09PXh6ekJHRwejRo3C%2BvXrce/ePeTm5mLlypUYNGgQnnvuuQY/n87ChQsXNtHfQhrGyMgIvXv3Vmg9N0eakiegObkyT/E0JVfmKZ6m5Kopef6d%2Bfr64tNPP8VPP/2E9PR0bNiwAevWrUNgYCBMTEzw4YcfYvDgwejUqRM6deoEY2NjLFmyBJ9//jnKy8uxYsUK%2BZHFbm5uuHHjBhYvXozY2Fh069YNH374IfT19Rucj5ak6l6JRERERKTxOHxMRERERCwKiYiIiIhFIRERERGBRSERERERgUUhEREREYFFIRERERGBRSERERERgUUhEREREYFFIRERERGBRSERERERAWih7gSIiB6nqqoKOjo6AIDq6mpcvXoV7du3R5s2bdScGdGf7t%2B/j7KysjrzO3TooIZs/vTHH380%2BL7qzpXUj9c%2Bfsalp6cjISEB6enpmD9/PgAgKSkJDg4Oas7sTwUFBdi5cydu3bql9E13xYoVasiqLk3Js8bRo0fh4%2BMDAEhOTkZ8fDw6deqEcePGQVu7eQwinDx5Eu%2B%2B%2By5OnjyJyspKjB8/HteuXQMArFy5El5eXmrO8E/79u3Dnj17kJWVhT179qC8vBxfffUVJk2aBC0tLXWnJ7dlyxZMmDChzvzCwkJ89tlnWLRokRqyqktT8vzvf/%2BLpUuXoqCgQGG%2BJEnQ0tLClStX1JSZTPfu3Ru8/ak7V1I/dgqfYUePHkVERARcXFzwyy%2B/YP78%2Bbh37x5ef/11LF68GP7%2B/upOEQAQERGBa9euwdXVFYaGhupOp16akicALF%2B%2BHIcPH4aPjw/S09MxYcIE%2BPr6Yv/%2B/fjjjz8wZ84cdacIQJbnrFmzAMiKruzsbJw%2BfRoXL17E0qVLm01RGBMTg7i4OAQHB2P9%2BvUAgAcPHmDPnj0oKCjAzJkz1Zzhn7766iscOHAAS5Ysga2tLQDg2LFjWLhwITp37qze5GrRlDyXL1%2BO119/HV5eXtDT01N3OnXs379f/ntSUhJ27dqFV199FZ07d0Z1dTVu3ryJ7du3Y%2BLEiepLkpoPiZ5ZQ4cOlY4cOSJJkiT17NlTPv/MmTOSv7%2B/utKqw8nJSUpPT1d3Gk%2BkKXlKkiS5u7tLv//%2Bux9v4xkAACAASURBVCRJkhQdHS29/vrrkiRJUnZ2tuTh4aHGzBQ5OTlJ1dXVkiRJ0syZM6WVK1fKlzk7O6srrToGDBggXb9%2BXZIkSXJwcJDPv3v3ruTp6amutJQqKyuToqOjpd69e0urV6%2BWZs6cKbm7u0t79%2B5Vd2oKNCXPvn37ShUVFepOo0GGDh0qZWRk1JmfmprarN7zSX3YKXyGpaSkwNvbGwAUhhd69eqF1NRUdaVVR7t27WBkZKTuNJ5IU/IEZENwNjY2AIBTp07Ju8Jt27ZFXl6eOlNTYGRkhAcPHkBfXx%2BnTp2SDyfm5uaiRYvm8/ZVUFCArl271plvYWGBnJwcNWRUPz09PYSFheH5559HREQEWrZsiW3btqF79%2B7qTk2BpuQ5ceJEbNq0CaGhoc1qm1QmLS0NLVu2rDPf1NQUaWlpasiImpvmvQVTk%2BrQoQOuXbsGe3t7hfknT55E27Zt1ZRVXfPmzcOSJUsQEhKCjh071tk/prkM2WhKngBgbW2NH3/8ES1btkRSUhJWrVoFQLZvYXN67YcOHYoJEyZAR0cHXbp0gZOTE0pLS7FgwQL0799f3enJdevWDXv37kVAQIDC/C%2B//FI%2B9Nlc3L9/H5999hl%2B%2BOEHLF26FKmpqXjttdcwceJEhISENJvtVFPy7NWrF2bNmoUNGzbAzMyszv/90aNH1ZRZXS4uLggLC8PkyZNhZWWFyspKpKenY%2BvWrXB2dlZ3etQM8ECTZ9jXX3%2BNmJgYBAUF4csvv8Ts2bNx7do17N%2B/H5GRkRg3bpy6UwQAuLm5oaSkBNXV1UqXN5edozUlTwA4fvw4IiIiUF5ejrfeegvh4eHIz8/H4MGDERYWhvHjx6s7RQCynfUTEhJQUFAAf39/mJqaory8HB999BFmz54NExMTdacIADhz5gymTp2Kbt26ISkpCR4eHrh%2B/Try8/MRExOD3r17qztFOTc3N/j4%2BODdd9%2BVH8F969YtLFiwAJmZmTh8%2BLCaM5TRlDwHDRqELl26oH///tDX16%2BzfPTo0WrISrmcnBwsWbIER48eRWlpKQCgRYsW6Nu3L5YsWQILCws1Z0jqxqLwGXf48GHs2rULd%2B/ehYGBAaytrTF69Gj069dP3anJ/fTTT49d3lw%2BcDUlTwAoKiqCvr4%2BysrKFIa8//e//8HJyUmNmWmujIwMJCQkyP%2BXbGxs4O/vj9atW6s7NQWnTp2qt8tac7BMc6Apebq4uODHH39sNp3LhsrLy0N5eTnMzMya/bA3/XVYFJLGqKioQGZmJrS0tGBpaSk/dx01nqOjI3x8fDBs2DC8/PLLzfZDwdvb%2B7Gn02guQ3PR0dGYOnVqnflFRUVYvXo13nvvPTVk9aeff/65wfft1atXE2byeJqSZ20ffPABfHx84OHhoe5UGqTmNGQZGRny7bK5nYaM1IdF4TNs7ty59S7T1taGpaUlBgwYoPbO0YMHD7BgwQIkJiaisrISAKCvr4%2BhQ4fi/fffVzpkow6PK2Bq1qeHhwcmTpwIXV3dvzg7RefOncPhw4eRmJiIoqIi%2BPn5ISAgAK6urmrN61H//ve/FW5XVVXh7t27OH78OEJCQjBy5Eg1ZSaTl5eHnJwcDB8%2BHHv37sWjb6d37tzBzJkzkZSUpKYMZR49OENLS0sh15rtVkdHBxcvXvxLc6tNU/KsbfHixThw4AA6deqE9u3b1znHZ3M6P%2BmjpyFLTk7GvXv3MHTo0GZ1GjJSHxaFz7D58%2BfjyJEjMDQ0xIsvvghtbW1cvnwZZWVl6N27N7Kzs3HhwgUsXLgQQUFBastz1qxZyMzMRGhoKDp16gRAtm/R%2BvXr4eTkpPYuTI1t27Zh3bp16NOnD3r27AltbW0kJSXh559/xsSJE1FYWIg9e/Zg0KBBePfdd9WdrlxycjISExNx8OBBVFRUYNiwYQgKCoK1tbW6U6tXcnIy1qxZg40bN6o1j127dmHZsmUoLCysUxDW%2BOc//4nPP//8L85MUXl5ufz3Y8eO4eDBgwgNDUXnzp0hSRJu3LiBL774AiNGjJCfkYB5NszjvlwDwNKlS/%2BiTJ5s2LBhmDFjBgYOHAgHBwf5l5Uff/wRH330ERISEtScIandX34SHGo2PvvsM2nNmjVSVVWVfF5VVZW0du1aacOGDZIkSdIPP/wg%2Bfr6qitFSZIkyc3NTbp//36d%2Benp6c3qHHBvvfWWdPz48Trzv//%2Be2n27NmSJEnSb7/91qzOA1jj119/lZYtWya5urpK/fv3l9zc3KSIiAgpJydH3akpVVlZKTk5Oak7DUmSZLm4urpKqampdX6ys7PVnV4dgwYNkvLz8%2BvMv3//vuTj46OGjJTTlDw1iaOjo/z9vvb5NCsrKyVHR0d1pUXNSPPckYj%2BEjt37sTJkycVhju0tbURGhoKLy8vTJkyBf3790dGRoYas5QNFSm7QoiJiQmKi4vVkJFyZ86cwerVq%2BvM/8c//oGIiAgAgJWVVZ3LYanLnTt38O233yIhIQFZWVnw9vbGypUr4e7ujuLiYixevBiRkZHYtGmT2nI8efJknXmlpaVITExsNkdK6ujo4Ny5c0qXVVdXY/To0XWGwdUpNzdX6WUYq6urm9U5KjUhz2PHjuHGjRvw9vZG165dceLECWzfvh26urrw9vbGK6%2B8ou4UFWjKachIfVgUPsN0dXVx4sQJDBw4UGH%2BmTNn5MM4x48fR/v27dWRnpyLiwsWL16Md955B2ZmZgBkp1ZYvnw5evbsqdbcarO0tMTq1avx1ltvoVWrVgCA4uJibNy4ESYmJpAkCVFRUbCzs1NzpsCIESNw9epVuLq6YsqUKfDz84OxsbF8ubGxMT788EO1HzEdEhJSZ56enh46deqEhQsX/vUJ1aOwsBDR0dG4ePEiKioq5POzs7OVFjbq9PLL/9/e3cfVfL9/AH%2BdUCLN1zalO%2BTL3EQ7OdVYbmqsUknjMSQbakiiG2ND64aYLRSTtjWkWYxtbqutqEh9KaL6LUykGypFQimdPr8/evh8nZ3jZl/m/T5zPf%2Baz%2BmP16O5uXp/rvd1jcDMmTMxdepUGBkZibPqdu3axdXsR95zbtmyBVFRUejTpw%2B%2B%2B%2B47fP755/j0009hb28PDQ0NrFq1CnV1dZg5cybrqCJ3d3d4enpi0qRJkMvl2LZtm8IYMkKop/Altnv3bnz22Wfo378/DA0N0b59e1y9ehUFBQXw9/fHjBkzYGFhgTVr1jBtQK6qqoK3tzeKiorEuXT19fXo06cPoqOjxc0crOXn58Pb2xs3b96Ejo4O2rdvj1u3bkFbWxuRkZEYPnw4HBwcEBkZiYEDBzLNGh0dDVdXVxgaGip9VlRUJJ4knDp1irvLJzwKDAzE5cuXMWLECHz33Xf46KOP8Pvvv6OmpgZr167laldvY2MjNm/ejNTUVFRWVqK5uRndu3fHyJEjsWjRIoUfDljiPae9vT3Cw8Mhk8mQlJSElStXYuXKleI%2B7tOnT%2BPTTz/Fr7/%2ByjTnn6nDGDLCDhWFL7nff/8dR48exfXr19Ha2opXX30V1tbWSEhIwLp161BeXg4jIyPWMQG0FSsVFRVobm6GsbExV6eEDzQ3N6OgoAA1NTXi99PMzEzlainWBEHA1atXFZr7Kysr4ePjg9OnTzNMpujGjRvIyMgQ13D17NkTtra2zIuCh7311ltITk5G165dFRr44%2BLiUF9fD19fX8YJyfMmlUqRl5cHAGhpaRH/vz8Y79Ta2goLCwucOXOGZUwFWVlZGDZs2GPHPJGXG70%2BfonJ5XLk5eXhypUrYmFQX1%2BPEydO4MKFCwDArCC8f/%2B%2BOLblQbY%2BffoorAx78Jzl0FhVOVUVq83NzVwNt83NzcWCBQtw8%2BZNpc/%2B3E7A0oNNIdra2uJt6B9%2B%2BAErV67E999/r3LfMAuCIIgtAx06dEBDQwM6deqE999/H3Z2dsyLwj179ogTBHbt2vXYr2U5FFpdcgJte60LCwthZmaG9u3bw9fXV2HeZ25urtjuwgsfHx/o6OjA2dkZLi4uzN9YEP7QSeFLLCQkBGlpaZDJZEhOToaTkxOKioqgqamJZcuWwcLCglk2c3NznD17FkDb7DJVP9kKggCJRMJ0fZy65Pyz9957D%2B%2B88w7GjRuH8ePHIzExEYWFhUhMTERQUBA3lzjee%2B89uLi4KPRlyeVybN68GTk5OYiLi2OY7r%2B8vLygr6%2BPzz77DDNmzIBMJsPMmTNx5swZfPLJJzhx4gTTfA4ODkhOTgaAx45ykUgkTAeCq0tOoG0E1caNG7F%2B/XoMGzZM4bO1a9ciISEBH3/8MfPi9WFNTU04duwYUlJSkJ6ejldffRUuLi5wdnbmegQVeXGoKHyJ2djYYM%2BePdDX1xdffQiCgIiICBgZGWHq1KnMsuXm5kImkwHge32cuuT8M6lUitOnT0MikSi87jxz5gyioqKwdetWxgnbvPnmm8jNzVXauNLc3AwbG5snfs9flNLSUgQFBeHbb79Ffn4%2B5syZg4aGBmhoaCAgIACenp6sI5K/QWpqKvT19WFmZqbwPDQ0FJaWlhg3bhyjZE/W0tKCkydPIikpCcnJyejTpw/ef/99ODs7c/VWg7xgLObgED7IZDLxv998802hqalJEARBqKurE0aMGMEqlpKlS5eqfH779m1h3rx5LzjNX9fS0iJMnjyZdQwFo0aNEqqqqgRBEIS3335bKC0tFQRBEJqbm7mZ/ycIbbPq/vjjD6XnFy9eFGxtbRkkejq3bt0S8vLyhMrKStZRntr9%2B/fFvwN4xlvOQ4cOsY7wP7l69arwzTffCG5ubsKQIUOEBQsWCB4eHoKtra1QWFjIOh5hhHoKX2L9%2BvXDV199hTlz5qB3797YvXs3pk2bhmvXrnEx/6%2BsrAwlJSXYv38/xo0bp7Qx4sqVKyrn2LHyuLEkD1/m4IGzszMmTpyIpKQkjBgxAr6%2Bvhg/fjwKCgq4uVgEAK6urpg9ezamTZsGU1NTAMClS5ewY8cOODg4ME73Xy4uLjhw4ID4a11dXebrIf8qFxcXlJSUcNXmoApvOUNDQzF69GguL5P92a1bt5CcnIwDBw4gLy8P5ubmmDx5MsaNGyf2xP7www/45JNPFH4/k5cHvT5%2BiRUUFCAgIAD79u1DVlYW/Pz8oKmpiaamJkybNg1Lly5lmi8lJQUbNmzAH3/8ofJzLS0tTJky5Ylrpl6Uh8eSxMbGYvbs2dyOJQGAvXv3wtXVFXfv3kVoaCgKCgpgaGiIRYsWKQ23ZUUQBHz//ff46aefUF5ejubmZpiYmGD8%2BPHw9PREu3btWEcEAEyfPh1eXl4YNWoU6yj/s/z8fNy7d4%2BrNgdVeMuZkJCAnJwcTJgwAQYGBkq/J3v37s0omTIzMzP06NED48ePx4QJEx7ZR/hwrzR5uVBRSESXLl1CUVERDA0NuTrlcHV1xb59%2B1jHeCIaS/LyWr58OY4cOQJDQ0MYGBgo9UCuXbuWUTL1EhsbKw4sj4mJwdy5cxknerL%2B/fsrPZNIJFxeMHu4B5oQVej1MRGZmpqKr%2Bh48qiCsLGxEWPHjuXmFbLA%2BViSv3Kiunr16r8xyeOp01iSB%2BRyuVqdEh46dAh79%2B7F9evXsXfvXjQ3NyM%2BPh6zZs1iOsMuOjoaffv2hYmJCaKjo%2BHg4KDUNvIALydwrG9B/xUymQwJCQlITExERUUFJBIJTExM4ObmhvHjx7OORzhARSHhXlVVFcLDw1FYWKjQm3f37l1uRqcAbfMJg4OD8dlnn%2BGNN95ATEyMOJaktbWVdTyFdWutra3IyMiAkZERevfujdbWVhQXF6OqqgqOjo4MU7adFj0oCr/%2B%2ButHfp1EIuGmKGRZRP9V0dHR2LVrFyZPnoyYmBgAbfNJ9%2B7di9u3b8PPz49ZtokTJ2LOnDliYfrnvlEeT%2BAebAWqqKhQKLT09PQYJ1MWGRmJPXv2wNXVFS4uLgDa3hCFh4ejoaEBU6ZMYZyQsEavjwn3Zs%2BeDaBtqHJYWBhCQkLwf//3fygqKsKmTZu4WeSuTmNJwsLCMGjQIEycOFHh%2BY4dO3Dx4kUEBwczSqbo4eHg6ujBBh5eTrUAYNSoUYiNjUXfvn0VesfKysrwwQcfIC0tjWm%2BW7du4c6dOwozC1VRtaKRhYqKCvj5%2BaGgoEB8JpFI8NZbbyEyMhKvvPIKw3SKRo4cia%2B//lqpZzg/Px9LlixBUlISo2SEF1QUEu5ZWVkhIyMD2traCv%2BIHTx4ELm5uQgJCWEb8BHq6%2Btx6dIl9OjRg7tTA0tLS2RnZ6uc/zd8%2BHDk5uYySqbI0tISY8eOxbhx4zB8%2BHBoaGiwjvSXODo6cnVTFgAsLCzENYYP/3lqamqClZUVNxcMSkpKuLucpcrcuXOhpaWFBQsWiHvYi4uLxYJwzZo1jBP%2Bl4WFBU6ePKn0514ul8PKygqnTp1ilIzwgl4fE%2B61b99eLAa0tLRQV1eHrl274t1330VoaChXRWFdXR0yMzNRXV0NiUQCPT09Lvs0u3TpgqNHjyptjcjOzhb7InmwatUqpKamIjAwEBoaGhg7diycnJxgZWXF1f7W69ev4/XXX1d6vm3bNrS0tDBI9Gj9%2BvXD/v37lXrItmzZorBGkjVNTU2EhYWhuLhYofXhgZ07dzJIpezkyZM4duwYOnfuLD7r378/1qxZAycnJ4bJlPXt2xd79uxRek38888/o2fPnoxSEZ5QUUi4J5PJMH/%2BfGzYsAGDBw/G559/Dg8PD5w5cwZaWlqs44lSU1Ph5%2BcHHR0d9OjRA4Ig4Nq1a2hsbMSGDRswevRo1hFFc%2BfOhY%2BPDwYOHAgjIyPI5XJcu3YNRUVFWLZsGet4orFjx2Ls2LGQy%2BU4ceIEUlJSsGjRIgiCAEdHR26yvvvuu8jNzVUaR8LbCTEALFy4ED4%2BPvjhhx9w//59eHt748KFC7h16xaio6NZxxMFBASgsbERNjY20NbWZh3nkTp16qQwl/RhPPQSP%2Bzjjz%2BGl5cX4uPjxR8ALl26hLKyMmzcuJFxOsIDen1MuHfjxg1EREQgNDQUJSUlmDNnDq5evYpOnTohNDRUbJhmzdbWFl5eXnB3dxdPsQRBQHx8PLZs2YL09HS2Af/k8uXLSE1NxdWrV9Hc3IwePXpg5MiRGDJkCOtoj9Tc3Izjx4/ju%2B%2B%2Bw6lTp7h5LbtmzRpoa2vD09NT4cSIV5WVlTh06BBKS0vRsWNHmJiYwMnJCV27dmUdTSSVSpGRkQFdXV3WUR5r8eLFqK2thZ%2Bfn8KA9aioKHTq1AkbNmxgnFBRTU0NDh48KPa7mpiYwNHREQYGBqyjEQ5QUUjUjiAIqKmpQbdu3bgZXgy0/SOWk5Oj1K9z//59WFlZIS8vj1EyZbW1tQgJCUFGRoZ4o1tbWxujR49GUFAQunXrxjjhf9XX1%2BPIkSM4fPgwMjMz8frrr8Pe3h4ODg4YNGgQ63gA2rZsVFdX486dO9DV1VX6fcnL2KQH6uvrIZfL8a9//QsAUF5ejs6dO4u/5oGbmxu%2B/fZbvPbaa6yjPFZ9fT2WLVuG1NRU8ZkgCLCxscEXX3zB1Z8lQp6EikLCPZlMhpycHK56yFTx9/eHm5sbRo4cqfA8Ozsbu3btQmRkJKNkyqZPnw6JRIIZM2aIJwTl5eWIi4uDhoYG4uLiGCds88EHH%2BD06dPQ19eHg4MDHB0duSkEH/bLL7889nM3N7cXlOTJ/vOf/2D%2B/PkICwvDuHHjALTdOl%2B/fj02bdoEa2trxgnbZGVlYfv27XB3d4ehoaHSJSOebnQDbbemH5y6Gxsbc1MMTp48%2Ban/7uSlT5OwQ0Uh4Z6/vz/eeustbmbSPcr69euxc%2BdOSKVS9O7dG3K5HKWlpcjLy8P48eMV%2BqICAgIYJm27dZqZmal0qaSurg6jRo3i5gZqREQEHBwcYGZmxjrK/2zt2rUIDAxkHUPk5uaGDz74QKlQPXToEGJjY59Y4L4o6rQppLq6GllZWaiuroampib09fVhY2MDHR0d1tHw1VdfPfXXzp8//29MQtQBFYWEe3PnzsXZs2fRrl076OvrK72e5eWn2%2BnTpz/V10kkEmzfvv1vTvN4zs7O2Lp1q9KN2draWnz44Yc4ePAgo2TKKisrcfDgQVRVVYkXS/Lz87nrfUxPT1casF5VVYWUlBRxBAwPpFKpyksxvLU5VFRUPPZzXuYU/vrrrwgMDISOjg4MDQ0hCILYrxcZGalWm24IodvHhHtmZmZqcVIUHx/POsJTW7BgAQIDA%2BHh4YFevXqhtbUVpaWlSEhIgKenJy5fvix%2BLcvXdIcPH4a/vz8sLCxw6tQpLFu2DNeuXcPMmTMRFhbGzciPjRs3YsuWLXjjjTeQn58PqVSK4uJi6OnpITw8nHU8BSYmJkhJSVHaFrJ3716uLhuoy6aQdevWYfHixfDw8BBfcbe2tiIhIQGrV6/mqihsbGxEVFQU0tLSxLFZ%2Bvr64hpOnqY5EDbopJCQ50Qul%2BPIkSMoKSlRmqsmkUjg4%2BPDKJkyVa/mHsbLazoXFxcsXLgQY8aMwZAhQ5Cfnw%2BgrS9u5cqV3JxoPrwl5EHOpqYmhIaGwtbWFmPHjmUdUZSZmQlfX1/07NkTRkZGaG1txeXLl3Ht2jXExsZCJpOxjghAfTaFSKVSnDx5UmnzTktLCywtLbk5eQUAX19fXLp0CVOmTIGBgYF4qvnjjz%2BiT58%2B3N2UJi8enRQS7j2pJ4aXPhg/Pz9kZGTA1NRU6Sdu3orCw4cPs47wVMrKysQB2w83y1taWqK8vJxVLCW3b99G3759AQDt2rWDXC6HlpYWAgICMHXqVK6KQhsbGyQnJyMpKQllZWWQSCSwtraGs7MzNysjAWDFihUwMDDA559/rrQpZNWqVdxsCrGzs8Px48eV5pDm5ORwdUoIAMeOHcNvv/2mtDPe3t4e9vb2jFIRnlBRSLh37NgxhV/L5XJUVFRAEARYWFgwSqUsMzMT%2B/fvV4vVXLz0Yz2JgYEBzp8/r7SrNTMzk6sCplevXvj555/h5uYGAwMDpKamwt7eHi0tLaitrWUdT4menh5mzJjBOsZj8bwpZN26deJ/d%2BvWDUuWLMGQIUPw73//GxKJBJcvX8bp06e5uxz32muvoVOnTkrPdXR0VG7kIS8fKgoJ93bt2qX0rLW1FTExMdDU1GSQSDUTExOuhv/%2BE7i7u8PT0xOTJk2CXC7Htm3bcP78eSQmJmLx4sWs44kCAgKwYMECvPvuu/jwww8REBAAU1NTVFZWwtbWlnU8TJ8%2BXex5fdKIEl4ubvG8KeTPr4T79euHe/fuobCwUOHZmTNnXnS0x1q%2BfDmCgoLg5eWl0Eu8detWLFq0SOGSFE9/t5IXh3oKidq6f/8%2BRo0ahaysLNZRAACFhYXYvHkzHBwc0L17d6W5apaWloySqbfffvsNP/30k7h9w9jYGFOmTMHw4cNZR1PQ2Ngojh3Kzs5GQUEBDA0NYW9vr3Rj/kXbvHkzvL29AahPO4a6bQpRB2ZmZmhpaVH6oeBB//DDeBr5Q14cKgqJ2srKyoKfnx9OnjzJOgoAICoqCjExMVD1R4r1hQ11dfbsWZibm7OO8UQhISFwdnbm5pLG4yQmJopDq3nG86aQPXv2YNKkSQBUv8l4QCKR4P33339RsZ7or/xdaWVl9TcmIbyiopBwz8bGRunZvXv3cOfOHcyYMQOffPIJg1TKLCwsEBwcDDs7O5WjHeh1zF8nk8mQlZXF/ffO29sbWVlZeOWVV%2BDg4AAnJydui1lra2ukpaWp7C3jEY%2BbQhwcHJCcnAwA4kUoVSQSidpc6iIEoKKQqAFVGxa0tLTQs2dPrlae2dnZISkpiWZ9PUdxcXEoLS2Fu7s7DAwMlAYu81QsNjY24ujRo0hNTUV6ejq6dOkCR0dHODk5YeDAgazjiRISEpCTk4MJEyao/J7ytD5u9%2B7dGDhwoPjnPD09HVVVVdxd4FAXmZmZ%2BOKLL3DlyhWF/sEH6G0GoaKQcMnOzu6p93Xy8pP4kSNHkJ2dDQ8PD%2Bjr6yvl56mAURdSqRQtLS24f/%2B%2BwveThxmKj9PS0oKsrCx88803OHXqFFc51WV93Pr163HgwAGsX79ePHU9ceIEgoKC4OjoCH9/f8YJ28hkMuTm5rKO8VRsbW0xYsQIjBw5UuUPryNGjGCQivCEikLCpYdvQNbW1uLHH3/E2LFjxRtzFy9eRHp6OmbNmsXNaI2hQ4eisbFRZU8hQD%2BF/y8e9EC1trairq4OEokEXbt2FQtE3vqempubkZmZKZ4WtmvXDvb29li%2BfDnraCJ1WR83YsQI7Ny5UynP1atXMXXqVGRkZDBKpkhddrMDbZfdsrOzmV98IvyiopBwz9PTEwsXLlTadZubm4vo6Ghs2bKFUTJFT2ri5q2AUQe1tbUICQnB0aNHxdddHTt2xOjRoxEUFMRFfxnQtiLu8OHDyMzMhI6ODuzt7eHg4IChQ4c%2B9Yn3i1ZdXa2w6oynuY9A2wnc0aNHlXofb926hdGjR3OzKURddrMDQHh4OAYNGoQJEyawjkI4RUUh4Z5UKsWJEyeUXr82NzfD2tqam38cHicgIEBh4C15OtOnT4dEIsGMGTPEvbzl5eWIi4uDhoYG4uLiGCdsY2NjA3t7ezg6OnJdCALA5cuXERAQgHPnzomn2hKJBIMHD0ZERIS4PYS1BQsWQENDA3PnzlVYx7dp0yZ07NiRm5E06jLiB2j7fz9r1ix06NABenp6Sr9Pt2/fzigZ4QUVhYR7rq6uGDlyJObMmQMdHR0AwJ07dxAbG4vDhw/jwIEDjBO2kcvl2LlzJwoLCxWauKurq3HhwgWcOHGCYTr1ZG5ujszMTHTp0kXheV1dHUaNGoWzZ88ySqZI1Zw3Xr333nvo06cPvLy8YGhoKO6//fbbb3Hp0iWVF7tYuHHjBpYvX4709HSFjnAU/gAADM5JREFU4nXMmDFYtWqV%2BHcBeXouLi5o164drKysVPYUBgYGMkhFeEKNBYR7YWFhWLhwIbZs2QJdXV3I5XLcuXMHurq62LRpE%2Bt4ohUrViAtLQ0ymQzJyclwcnJCUVERNDU1sXnzZtbx1JKxsTHu3bunVBTK5XIYGxszSqXsUQXhmjVrUF1djbVr177gRI9WXFyMHTt2iIO2gbbLJ2FhYVwNBO/WrRuio6NRV1eH8vJyaGhowNDQEK%2B88grraArU6aSwvLwcx48fV5txROTFo6KQcM/c3BxHjhxBYWEhKisr0dzcjO7du8Pc3Jyr8S%2BpqanYs2cP9PX1kZKSgi%2B%2B%2BAKCICAiIgLnz5/nak%2BzuliwYAECAwPh4eGhsJYrISEBnp6euHz5svi1PI1SeaBDhw7c3Trv168fKisrlb5ftbW16NevH6NUj9a1a1eu10eqy252oK3N4dKlSzAzM2MdhXCKXh8T8pxYWloiJycHgGIf5K1bt%2BDi4oKjR48yTqh%2BVI1PeRiPo1R498svv2Dr1q1wc3NDr169IJfLUVZWhn379mHSpEno1auX%2BLWqBsezNm3aNFy5cgWZmZmsozzSw7vZvby8WMcRbd68Gbt374ZUKkWPHj2UVnEGBAQwSkZ4QUUhIc/JtGnTMGzYMMyZMweTJ0/GxIkTMW3aNJw7dw4eHh5qM8uMJ08an/Iw1qNU6urqkJmZKd7o1dPTg42NDXR1dZnm%2BrMnFdoP8Fpop6am4vbt23Bzc2Md5bF4280OtF3cehSJREIXTQgVhYQ8LwUFBQgICMC%2BffvEvcyamppoamqCu7s7li1bxjoi%2BZukpqbCz88POjo66NGjBwRBwLVr19DY2IgNGzZg9OjRrCOSF4y33eyEPA0qCgl5jrKysqCvrw9TU1MUFxcjKSkJmpqamD17Nuto5G9ka2sLLy8vuLu7i5dOBEFAfHw8tmzZgvT0dLYB1VBdXR1iYmLE3eY7duzArl270LNnTwQFBaF79%2B6ME7Z51G72u3fvYsaMGViyZAmDVI9WWVmJgwcPoqqqSvxBNT8/X2kOLHk5UVFIyHMSHx%2BPqKgobNy4EcOGDQPQtoJv6dKl8PX1hYeHB%2BOE5O8ilUqRk5OjNLj4/v37sLKyUotZmrz16vn6%2BkIulyM6OhoFBQWYPn06QkJCUFhYiOrqam7mFD4Y4SMIAm7evIlu3bpxuZsdaPv7yN/fHxYWFjh16hQKCgpw7do1ODs7IywsDE5OTqwjEtYEQshzYWtrKxQVFSk9P3/%2BvGBnZ8cgEXlR/Pz8hIyMDKXnWVlZwsKFCxkk%2ButSUlKEn3/%2BmXUMkZWVlVBfXy8IgiCsWrVKCAwMFARBEBobG4Vhw4axjKbg9u3bQlBQkGBubi4MGjRIEARBuHnzpjB79myhtraWcTpFzs7OQkpKiiAIgjB48GDxeXZ2tuDk5MQqFuEIjaQh5Dm5efMmTE1NlZ4bGRnhxo0bDBKRF8XExAQff/wxpFIpevfuDblcjtLSUuTl5WH8%2BPEK22x4veE5ZswY1hEUtLa2igOqjx8/Dh8fHwBtY34aGxtZRlOwcuVKVFVVITY2FrNmzQLQllFHRwcrV67kapNRWVkZ7OzsACjO1rS0tER5eTmrWIQjVBQS8pxYWFhg3bp18PHxEYct19TUIDIyEubm5ozTkb/T6dOn0a9fP9y9exeFhYXi8379%2BuHcuXPir3nYeqIuvXpmZmbYtGkTtLS0UF1dLV7WSUxM5GomZVpaGpKSktCtWzfx/2/nzp0RHBwMe3t7xukUGRgY4Pz58xgwYIDC88zMTO52XxM2qCgk5DkJCQmBr68vtm/fDh0dHbS2tuLu3bsYMGAAYmJiWMcjf6P4%2BHjWEZ5aUFAQ5HI5gLYb819%2B%2BaXYq7dy5UpuevWCg4OxYsUK1NfX48svv4S2tjbq6uq4ygi0FfqqVu7J5XI0NTUxSPRo7u7u8PT0xKRJkyCXy7Ft2zacP38eiYmJWLx4Met4hAN00YSQ56yoqAilpaXQ0NCAsbHxU8%2BFI%2BpLLpfjyJEjKCkpUSoEJBKJ%2BOqTB9bW1khNTUWXLl2wevVq1NbWIiIiAvfu3YOdnR1Xc/VUaWpq4mqTkbe3NwwNDbFo0SJYW1vj7NmzqKioQHh4uDjEmie//fYbfvrpJ5SWlqJjx44wNjbGlClTuFpxSNihopAQQp6Rr68vMjIyYGpqqlSwSCQS7Ny5k1EyZZaWljh58iQkEgmcnZ3h4%2BMDR0dHyOVyyGQybm5K3759Gz/%2B%2BCOKi4tVnrjxsk/66tWrmDdvHi5evIiWlhZ07twZDQ0NkEqlWLt2LXr06ME6IiFPjV4fE0LIM8rMzMT%2B/fsVVsTxSl169fz9/XH%2B/HkMHToU2trarOM8koGBAfbu3YuCggKUlZVBS0sLJiYm6Nu3L%2BtoT%2B306dNoaGjgcq0hebHopJAQQp6Rq6sr4uLi0LVrV9ZRnqikpETs1Zs/fz5GjRqFuro62NvbY8OGDbC2tmYdEUDb7Mfk5GTo6emxjvKP5%2BjoiJKSEi7XGpIXi4pCQgh5RoWFhdi8eTMcHBzQvXt3aGhoKHxuaWnJKNnT461Xz9HREbt371Z5iYP8b86ePUuTEMhjUVFICCHPKCoqCjExMVD116lEIuHqBEZdevWOHTuGxMREeHl5wcjISGmcj6amJqNk6ksmkyErK4u%2Bd%2BSRqCgkhJBnZGFhgeDgYNjZ2ak8bePpH2EvL6/H9uqtXr2aQSplMpkMjY2NaG1tVfk5T4W2uoiLi0NpaSnc3d1hYGCAdu3aKXzO0%2B9TwgYVhYQQ8ozs7OyQlJTE1evXR1GXXr2TJ08%2B9nMrK6sXlOSfQyqVoqWlBS0tLSo/p0KbUFFICCHP6MiRI8jOzoaHhwf09fW5ftWpbr169%2B/fR3V1NSQSCfT09JROt8jTo0KbPAkVhYQQ8oyGDh0qvup8uCAUBIG7nkJ16dWrr69HcHAwUlNTxZMtLS0tODs7IygoSC1OZQlRN1QUEkLIM3pwAiMIAm7evIlu3bopfM7TCYy69OoFBASguroaH330EXr27AkAKC4uRkxMDN58800sW7aMcUL1c%2B7cOaxfvx7FxcW4d%2B%2Be0ueZmZkMUhGeUFFICCHP6O7du1izZg3279%2BPlpYWFBYWoq6uDkuWLMHq1auVikSW1OUVoqWlJX799Vel711VVRWmTJmCtLQ0RsnUl4uLC/T09GBnZ6fykpGbmxuDVIQntNGEEEKe0YoVK1BVVYXY2FjMmjULANChQwfo6OggPDycmzEvwH%2BLPt579dq1a6eycNHV1UVDQwODROqvvLwce/bsoVfv5JE0nvwlhBBCHictLQ1r166FTCYTe/Q6d%2B6M4OBgZGVlMU6nqL6%2BHv7%2B/rCwsMCYMWPwzjvvYOjQoVi%2BfLnKuYWsWFhYIDQ0FDdu3BCf3bhxAytWrMDgwYMZJlNfAwYMQGVlJesYhGN0UkgIIc9IIpGovM0rl8u5KrQAICQkBNevX8dXX32l1KsXERHBTa9ecHAw5s2bh7fffhu6uroA2graPn36IDo6mnE69TRz5kwsWbIErq6uMDQ0VNq8Q7uPCfUUEkLIM/L29oahoSEWLVoEa2trnD17FhUVFQgPD0draytiYmJYRxSpU69ednY2tLW1UVNTg%2BbmZrz%2B%2Buu4d%2B8eRowYwTqaWurfv/8jP%2BPtljxhg04KCSHkGQUFBWHevHmQyWRoaWnB0KFD0dDQAKlUylU/IaA%2BvXrx8fGIiorCxo0bMWbMGADA4cOHsXTpUvj6%2BsLDw4NxQvVz7tw51hEI5%2BikkBBCnpOCggKUlZVBS0sLJiYm6Nu3L%2BtISubNmwddXV0sXrxYPC28ceMGIiIiUF1djdjYWMYJ29jZ2SE6OlrpdOvChQvw9vbG4cOHGSUj5J%2BLikJCCHmJVFVVYd68efj9999V9uqZmJgwTthGKpXixIkTSsO0Gxoa8PbbbyMvL49RMkL%2BuagoJISQl4w69Op5enqib9%2B%2B8PHxQZcuXQAANTU1iIyMRHl5ObZt28Y2ICH/QFQUEkLIS%2BThXr1hw4YB4LNXr6ysDL6%2Bvrhw4QJ0dHTQ2tqKu3fvYsCAAYiJiUH37t1ZRyTkH4eKQkIIeYmoW69eUVERSktLoaGhAWNj48feoCWEPBu6fUwIIS%2BRmzdvwtTUVOm5kZGRwqBoXgwYMAADBgxgHYOQlwJtNCGEkJeIhYUF1q1bh9u3b4vPampqsGrVKpibmzNMRghhjV4fE0LIS4R69Qghj0JFISGEvISoV48Q8mdUFBJCCCGEEOopJIQQQgghVBQSQgghhBBQUUgIIYQQQkBFISGEEEIIARWFhBBCCCEEVBQSQgghhBBQUUgIIYQQQgD8P6FuR3y2u9O3AAAAAElFTkSuQmCC\" class=\"center-img\">\n",
              "</div>\n",
              "    <div class=\"row headerrow highlight\">\n",
              "        <h1>Sample</h1>\n",
              "    </div>\n",
              "    <div class=\"row variablerow\">\n",
              "    <div class=\"col-md-12\" style=\"overflow:scroll; width: 100%%; overflow-y: hidden;\">\n",
              "        <table border=\"1\" class=\"dataframe sample\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>job</th>\n",
              "      <th>marital</th>\n",
              "      <th>education</th>\n",
              "      <th>default</th>\n",
              "      <th>housing</th>\n",
              "      <th>loan</th>\n",
              "      <th>contact</th>\n",
              "      <th>month</th>\n",
              "      <th>day_of_week</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>poutcome</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56</td>\n",
              "      <td>housemaid</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.4y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>261</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>149</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>226</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40</td>\n",
              "      <td>admin.</td>\n",
              "      <td>married</td>\n",
              "      <td>basic.6y</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>151</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>services</td>\n",
              "      <td>married</td>\n",
              "      <td>high.school</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>telephone</td>\n",
              "      <td>may</td>\n",
              "      <td>mon</td>\n",
              "      <td>307</td>\n",
              "      <td>1</td>\n",
              "      <td>999</td>\n",
              "      <td>0</td>\n",
              "      <td>nonexistent</td>\n",
              "      <td>1.1</td>\n",
              "      <td>93.994</td>\n",
              "      <td>-36.4</td>\n",
              "      <td>4.857</td>\n",
              "      <td>5191.0</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "    </div>\n",
              "</div>\n",
              "</div>"
            ],
            "text/plain": [
              "<pandas_profiling.ProfileReport at 0x7f2b2198e160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6mW96HjkRFv0"
      },
      "source": [
        "data.drop_duplicates(inplace = True)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQXtt8zeRFsQ",
        "outputId": "8aada19d-30c6-480b-f078-642b1911924e"
      },
      "source": [
        "data['education'].value_counts()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "university.degree      12164\n",
              "high.school             9512\n",
              "basic.9y                6045\n",
              "professional.course     5240\n",
              "basic.4y                4176\n",
              "basic.6y                2291\n",
              "unknown                 1730\n",
              "illiterate                18\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tONJ0CZRFir"
      },
      "source": [
        "data[\"education\"].replace({'basic.9y':'Basic','basic.4y':'Basic', 'basic.6y':'Basic'}, inplace = True)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8rSDyUVRFfT",
        "outputId": "586cab94-008d-4801-85ab-b4d7e9b55dbf"
      },
      "source": [
        "data['education'].value_counts()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Basic                  12512\n",
              "university.degree      12164\n",
              "high.school             9512\n",
              "professional.course     5240\n",
              "unknown                 1730\n",
              "illiterate                18\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cA8yJpRRRFbp",
        "outputId": "4bcd5365-ecbd-4a6e-b4aa-70566fe64bdc"
      },
      "source": [
        "data['y'].value_counts()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "no     36537\n",
              "yes     4639\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIZI9NxDRFYM",
        "outputId": "79a856ce-29d3-47ac-ebf8-52c7088b829a"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
              "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
              "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
              "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNUKHgPmTOfc"
      },
      "source": [
        "data['y'] = np.where(data['y']=='yes',1,0)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "cQEGcvFaRFUl",
        "outputId": "dd74f389-cd29-42a4-a80d-84f1fdcad21f"
      },
      "source": [
        "data.groupby('job').mean()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>job</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>admin.</th>\n",
              "      <td>38.186870</td>\n",
              "      <td>254.315961</td>\n",
              "      <td>2.623572</td>\n",
              "      <td>954.306363</td>\n",
              "      <td>0.189078</td>\n",
              "      <td>0.016028</td>\n",
              "      <td>93.534250</td>\n",
              "      <td>-40.245331</td>\n",
              "      <td>3.550668</td>\n",
              "      <td>5164.136990</td>\n",
              "      <td>0.129667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>blue-collar</th>\n",
              "      <td>39.555820</td>\n",
              "      <td>264.557549</td>\n",
              "      <td>2.558630</td>\n",
              "      <td>985.158867</td>\n",
              "      <td>0.122555</td>\n",
              "      <td>0.248903</td>\n",
              "      <td>93.656619</td>\n",
              "      <td>-41.376354</td>\n",
              "      <td>3.771879</td>\n",
              "      <td>5175.613488</td>\n",
              "      <td>0.068951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>entrepreneur</th>\n",
              "      <td>41.723214</td>\n",
              "      <td>263.267857</td>\n",
              "      <td>2.535714</td>\n",
              "      <td>981.267170</td>\n",
              "      <td>0.138736</td>\n",
              "      <td>0.158723</td>\n",
              "      <td>93.605372</td>\n",
              "      <td>-41.283654</td>\n",
              "      <td>3.791120</td>\n",
              "      <td>5176.313530</td>\n",
              "      <td>0.085165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housemaid</th>\n",
              "      <td>45.500000</td>\n",
              "      <td>250.454717</td>\n",
              "      <td>2.639623</td>\n",
              "      <td>960.579245</td>\n",
              "      <td>0.137736</td>\n",
              "      <td>0.433396</td>\n",
              "      <td>93.676576</td>\n",
              "      <td>-39.495283</td>\n",
              "      <td>4.009645</td>\n",
              "      <td>5179.529623</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>management</th>\n",
              "      <td>42.362859</td>\n",
              "      <td>257.058140</td>\n",
              "      <td>2.476060</td>\n",
              "      <td>962.647059</td>\n",
              "      <td>0.185021</td>\n",
              "      <td>-0.012688</td>\n",
              "      <td>93.522755</td>\n",
              "      <td>-40.489466</td>\n",
              "      <td>3.611316</td>\n",
              "      <td>5166.650513</td>\n",
              "      <td>0.112175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>retired</th>\n",
              "      <td>62.037253</td>\n",
              "      <td>273.909779</td>\n",
              "      <td>2.478463</td>\n",
              "      <td>897.818393</td>\n",
              "      <td>0.327707</td>\n",
              "      <td>-0.697963</td>\n",
              "      <td>93.431084</td>\n",
              "      <td>-38.577474</td>\n",
              "      <td>2.769969</td>\n",
              "      <td>5122.261525</td>\n",
              "      <td>0.252619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>self-employed</th>\n",
              "      <td>39.949331</td>\n",
              "      <td>264.142153</td>\n",
              "      <td>2.660802</td>\n",
              "      <td>976.621393</td>\n",
              "      <td>0.143561</td>\n",
              "      <td>0.094159</td>\n",
              "      <td>93.559982</td>\n",
              "      <td>-40.488107</td>\n",
              "      <td>3.689376</td>\n",
              "      <td>5170.674384</td>\n",
              "      <td>0.104856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>services</th>\n",
              "      <td>37.925637</td>\n",
              "      <td>258.491303</td>\n",
              "      <td>2.588606</td>\n",
              "      <td>979.964457</td>\n",
              "      <td>0.155029</td>\n",
              "      <td>0.175548</td>\n",
              "      <td>93.634848</td>\n",
              "      <td>-41.289892</td>\n",
              "      <td>3.699441</td>\n",
              "      <td>5171.604159</td>\n",
              "      <td>0.081422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>student</th>\n",
              "      <td>25.894857</td>\n",
              "      <td>283.683429</td>\n",
              "      <td>2.104000</td>\n",
              "      <td>840.217143</td>\n",
              "      <td>0.524571</td>\n",
              "      <td>-1.408000</td>\n",
              "      <td>93.331613</td>\n",
              "      <td>-40.187543</td>\n",
              "      <td>1.884224</td>\n",
              "      <td>5085.939086</td>\n",
              "      <td>0.314286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>technician</th>\n",
              "      <td>38.508681</td>\n",
              "      <td>250.287431</td>\n",
              "      <td>2.577831</td>\n",
              "      <td>964.387595</td>\n",
              "      <td>0.153880</td>\n",
              "      <td>0.273898</td>\n",
              "      <td>93.561330</td>\n",
              "      <td>-39.926903</td>\n",
              "      <td>3.819722</td>\n",
              "      <td>5175.617258</td>\n",
              "      <td>0.108325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unemployed</th>\n",
              "      <td>39.733728</td>\n",
              "      <td>249.451677</td>\n",
              "      <td>2.564103</td>\n",
              "      <td>935.316568</td>\n",
              "      <td>0.199211</td>\n",
              "      <td>-0.111736</td>\n",
              "      <td>93.563781</td>\n",
              "      <td>-40.007594</td>\n",
              "      <td>3.466583</td>\n",
              "      <td>5157.156509</td>\n",
              "      <td>0.142012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unknown</th>\n",
              "      <td>45.563636</td>\n",
              "      <td>239.675758</td>\n",
              "      <td>2.648485</td>\n",
              "      <td>938.727273</td>\n",
              "      <td>0.154545</td>\n",
              "      <td>0.357879</td>\n",
              "      <td>93.718942</td>\n",
              "      <td>-38.797879</td>\n",
              "      <td>3.949033</td>\n",
              "      <td>5172.931818</td>\n",
              "      <td>0.112121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     age    duration  ...  nr.employed         y\n",
              "job                                   ...                       \n",
              "admin.         38.186870  254.315961  ...  5164.136990  0.129667\n",
              "blue-collar    39.555820  264.557549  ...  5175.613488  0.068951\n",
              "entrepreneur   41.723214  263.267857  ...  5176.313530  0.085165\n",
              "housemaid      45.500000  250.454717  ...  5179.529623  0.100000\n",
              "management     42.362859  257.058140  ...  5166.650513  0.112175\n",
              "retired        62.037253  273.909779  ...  5122.261525  0.252619\n",
              "self-employed  39.949331  264.142153  ...  5170.674384  0.104856\n",
              "services       37.925637  258.491303  ...  5171.604159  0.081422\n",
              "student        25.894857  283.683429  ...  5085.939086  0.314286\n",
              "technician     38.508681  250.287431  ...  5175.617258  0.108325\n",
              "unemployed     39.733728  249.451677  ...  5157.156509  0.142012\n",
              "unknown        45.563636  239.675758  ...  5172.931818  0.112121\n",
              "\n",
              "[12 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "Svx-s7Z7RFRN",
        "outputId": "7f4303e6-958b-4e51-8c7c-1c37a12f92be"
      },
      "source": [
        "data.groupby('marital').mean()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>divorced</th>\n",
              "      <td>44.898937</td>\n",
              "      <td>253.836044</td>\n",
              "      <td>2.613316</td>\n",
              "      <td>968.633268</td>\n",
              "      <td>0.168727</td>\n",
              "      <td>0.163717</td>\n",
              "      <td>93.606496</td>\n",
              "      <td>-40.706636</td>\n",
              "      <td>3.715333</td>\n",
              "      <td>5170.866233</td>\n",
              "      <td>0.103231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>married</th>\n",
              "      <td>42.307411</td>\n",
              "      <td>257.466996</td>\n",
              "      <td>2.573091</td>\n",
              "      <td>967.238754</td>\n",
              "      <td>0.155652</td>\n",
              "      <td>0.183656</td>\n",
              "      <td>93.597440</td>\n",
              "      <td>-40.271012</td>\n",
              "      <td>3.745829</td>\n",
              "      <td>5171.847029</td>\n",
              "      <td>0.101561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>single</th>\n",
              "      <td>33.156866</td>\n",
              "      <td>261.554912</td>\n",
              "      <td>2.534244</td>\n",
              "      <td>949.892598</td>\n",
              "      <td>0.211432</td>\n",
              "      <td>-0.167840</td>\n",
              "      <td>93.517363</td>\n",
              "      <td>-40.919068</td>\n",
              "      <td>3.317548</td>\n",
              "      <td>5155.203416</td>\n",
              "      <td>0.140090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unknown</th>\n",
              "      <td>40.275000</td>\n",
              "      <td>312.725000</td>\n",
              "      <td>3.187500</td>\n",
              "      <td>937.100000</td>\n",
              "      <td>0.275000</td>\n",
              "      <td>-0.221250</td>\n",
              "      <td>93.471250</td>\n",
              "      <td>-40.820000</td>\n",
              "      <td>3.313037</td>\n",
              "      <td>5157.393750</td>\n",
              "      <td>0.150000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age    duration  campaign  ...  euribor3m  nr.employed         y\n",
              "marital                                    ...                                  \n",
              "divorced  44.898937  253.836044  2.613316  ...   3.715333  5170.866233  0.103231\n",
              "married   42.307411  257.466996  2.573091  ...   3.745829  5171.847029  0.101561\n",
              "single    33.156866  261.554912  2.534244  ...   3.317548  5155.203416  0.140090\n",
              "unknown   40.275000  312.725000  3.187500  ...   3.313037  5157.393750  0.150000\n",
              "\n",
              "[4 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "6ACLRAM3RFN3",
        "outputId": "a801fbd3-7bcc-4988-8d84-4f31c3865606"
      },
      "source": [
        "data.groupby('education').mean()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>duration</th>\n",
              "      <th>campaign</th>\n",
              "      <th>pdays</th>\n",
              "      <th>previous</th>\n",
              "      <th>emp.var.rate</th>\n",
              "      <th>cons.price.idx</th>\n",
              "      <th>cons.conf.idx</th>\n",
              "      <th>euribor3m</th>\n",
              "      <th>nr.employed</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>education</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Basic</th>\n",
              "      <td>42.164162</td>\n",
              "      <td>263.054987</td>\n",
              "      <td>2.559623</td>\n",
              "      <td>974.876039</td>\n",
              "      <td>0.141065</td>\n",
              "      <td>0.191256</td>\n",
              "      <td>93.639905</td>\n",
              "      <td>-40.927957</td>\n",
              "      <td>3.729564</td>\n",
              "      <td>5172.012596</td>\n",
              "      <td>0.087036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>high.school</th>\n",
              "      <td>37.996951</td>\n",
              "      <td>260.949117</td>\n",
              "      <td>2.568860</td>\n",
              "      <td>964.347456</td>\n",
              "      <td>0.185976</td>\n",
              "      <td>0.032843</td>\n",
              "      <td>93.584890</td>\n",
              "      <td>-40.940317</td>\n",
              "      <td>3.556085</td>\n",
              "      <td>5164.988394</td>\n",
              "      <td>0.108389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>illiterate</th>\n",
              "      <td>48.500000</td>\n",
              "      <td>276.777778</td>\n",
              "      <td>2.277778</td>\n",
              "      <td>943.833333</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>-0.133333</td>\n",
              "      <td>93.317333</td>\n",
              "      <td>-39.950000</td>\n",
              "      <td>3.516556</td>\n",
              "      <td>5171.777778</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>professional.course</th>\n",
              "      <td>40.083969</td>\n",
              "      <td>252.566603</td>\n",
              "      <td>2.586832</td>\n",
              "      <td>960.744084</td>\n",
              "      <td>0.163168</td>\n",
              "      <td>0.172309</td>\n",
              "      <td>93.569755</td>\n",
              "      <td>-40.123893</td>\n",
              "      <td>3.709739</td>\n",
              "      <td>5170.122805</td>\n",
              "      <td>0.113550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>university.degree</th>\n",
              "      <td>38.876356</td>\n",
              "      <td>253.237340</td>\n",
              "      <td>2.563713</td>\n",
              "      <td>951.792174</td>\n",
              "      <td>0.192453</td>\n",
              "      <td>-0.027425</td>\n",
              "      <td>93.493711</td>\n",
              "      <td>-39.976726</td>\n",
              "      <td>3.530225</td>\n",
              "      <td>5163.248027</td>\n",
              "      <td>0.137208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unknown</th>\n",
              "      <td>43.485549</td>\n",
              "      <td>262.491329</td>\n",
              "      <td>2.597110</td>\n",
              "      <td>942.798266</td>\n",
              "      <td>0.226590</td>\n",
              "      <td>0.058324</td>\n",
              "      <td>93.658465</td>\n",
              "      <td>-39.876185</td>\n",
              "      <td>3.570291</td>\n",
              "      <td>5159.509884</td>\n",
              "      <td>0.145087</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           age    duration  ...  nr.employed         y\n",
              "education                                   ...                       \n",
              "Basic                42.164162  263.054987  ...  5172.012596  0.087036\n",
              "high.school          37.996951  260.949117  ...  5164.988394  0.108389\n",
              "illiterate           48.500000  276.777778  ...  5171.777778  0.222222\n",
              "professional.course  40.083969  252.566603  ...  5170.122805  0.113550\n",
              "university.degree    38.876356  253.237340  ...  5163.248027  0.137208\n",
              "unknown              43.485549  262.491329  ...  5159.509884  0.145087\n",
              "\n",
              "[6 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "9LE51VMrRFKL",
        "outputId": "128917f1-d5d9-4b9b-d6e1-038bc94b9c1a"
      },
      "source": [
        "pd.crosstab(data[\"job\"], data[\"y\"]).plot(kind = \"bar\", figsize=(20,10))\n",
        "plt.title('Purchase Frequency for Job title', fontsize = 30)\n",
        "plt.xlabel(\"Job\", fontsize = 30)\n",
        "plt.ylabel('Purchase', fontsize = 30)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Purchase')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "ch0h8MLmRFGS",
        "outputId": "43eda611-5b44-4368-e8ab-f0accc5b0650"
      },
      "source": [
        "pd.crosstab(data[\"marital\"], data[\"y\"],normalize = True)\n"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>y</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>marital</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>divorced</th>\n",
              "      <td>0.100423</td>\n",
              "      <td>0.011560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>married</th>\n",
              "      <td>0.543763</td>\n",
              "      <td>0.061468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>single</th>\n",
              "      <td>0.241500</td>\n",
              "      <td>0.039343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unknown</th>\n",
              "      <td>0.001651</td>\n",
              "      <td>0.000291</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "y                0         1\n",
              "marital                     \n",
              "divorced  0.100423  0.011560\n",
              "married   0.543763  0.061468\n",
              "single    0.241500  0.039343\n",
              "unknown   0.001651  0.000291"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "rjWs0nQsRFCm",
        "outputId": "a247252c-8c11-463c-9f1e-4b524e0624dd"
      },
      "source": [
        "table=pd.crosstab(data.marital,data.y)\n",
        "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, figsize = (20,10))\n",
        "plt.title('Stacked Bar Chart of Marital Status vs Purchase')\n",
        "plt.xlabel('Marital Status')\n",
        "plt.ylabel('Proportion of Customers')\n",
        "plt.savefig('mariral_vs_pur_stack')"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "DCVuRmAwRE_G",
        "outputId": "d9100627-1cac-434c-ceb6-632968dfb360"
      },
      "source": [
        "table=pd.crosstab(data.education,data.y)\n",
        "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, figsize = (20,10))\n",
        "plt.title('Stacked Bar Chart of Education vs Purchase')\n",
        "plt.xlabel('Education')\n",
        "plt.ylabel('Proportion of Customers')\n",
        "plt.savefig('edu_vs_pur_stack')\n"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "uR0yWxVRRE7C",
        "outputId": "9f65cbfb-33df-4658-957d-c0c1a535cb2b"
      },
      "source": [
        "pd.crosstab(data.day_of_week,data.y).plot(kind='bar', figsize = (20,10))\n",
        "plt.title('Purchase Frequency for Day of Week')\n",
        "plt.xlabel('Day of Week')\n",
        "plt.ylabel('Frequency of Purchase')\n",
        "plt.savefig('pur_dayofweek_bar')"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mW45QkXORExu"
      },
      "source": [
        "data_numberical_columns = data.select_dtypes(include=np.number).drop(columns='y').columns"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozGpQXgaX1LB"
      },
      "source": [
        "data_categorical_columns = data.select_dtypes(exclude=np.number).columns"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y06Y21X3X4jo"
      },
      "source": [
        "#Create dummy for Category\n",
        "\n",
        "data_encoded_category = pd.get_dummies(data[data_categorical_columns])"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CNFOMLTZLeM",
        "outputId": "0bf98df4-d391-4ba5-c1b5-01efcf7a14c9"
      },
      "source": [
        "data_encoded_category.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41176, 51)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVsFOI-7ZOtf"
      },
      "source": [
        "data_preprocessed = pd.concat([data[data_numberical_columns], data_encoded_category, data['y']], axis = 1)"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Ykk50qUZOpr"
      },
      "source": [
        "X = data_preprocessed.drop(columns='y')"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZLnv3_rZOmD"
      },
      "source": [
        "y = data['y']"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGqMpTXpZOiW"
      },
      "source": [
        "#Train Test split\n",
        "\n",
        "train_X, test_X, train_y, test_y = train_test_split(X,y, test_size=0.3, random_state = 24)"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7q11TasXZOa_",
        "outputId": "1e06617d-771b-4f9b-81a2-df746abfd4b2"
      },
      "source": [
        "train_X.shape, test_X.shape, train_y.shape, test_y.shape"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28823, 61), (12353, 61), (28823,), (12353,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TbBtv51-adxu",
        "outputId": "6c57a04a-55ef-4a00-e8ce-910326ad0368"
      },
      "source": [
        "data['y'].value_counts(normalize = True)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.887337\n",
              "1    0.112663\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uvfXqNvladum",
        "outputId": "b8b30013-da31-4072-966f-34d7b3c20371"
      },
      "source": [
        "train_y.value_counts(normalize = True)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.886514\n",
              "1    0.113486\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7gQZZYqadoY",
        "outputId": "eb260a84-13b3-4a6f-db80-c96a0eb6609d"
      },
      "source": [
        "test_y.value_counts(normalize = True)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.889258\n",
              "1    0.110742\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKrlCXIZfz8h"
      },
      "source": [
        "\n",
        "### Model Building\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cy9wSVDaadlR"
      },
      "source": [
        "def run_model_validate(model, train_X, train_y, test_X, test_y):\n",
        "  model.fit(train_X, train_y)\n",
        "  pred_train = model.predict(train_X)\n",
        "  pred_test = model.predict(test_X)\n",
        "  print(\"Train Accuracy : \",accuracy_score(train_y, pred_train))\n",
        "  print(\"Test Accuracy : \",accuracy_score(test_y, pred_test))\n",
        "\n",
        "  print(\"Train F1 Score : \",f1_score(train_y, pred_train))\n",
        "  print(\"Test F1 Score : \",f1_score(test_y, pred_test))\n",
        "\n",
        "  print(\"Train Confusion Matrix : \",confusion_matrix(train_y, pred_train))\n",
        "  print(\"Test Confusion Matrix : \",confusion_matrix(test_y, pred_test))\n",
        "\n",
        "  print(\"Train Classification Report : \",classification_report(train_y, pred_train))\n",
        "  print(\"Test Classification Report : \",classification_report(test_y, pred_test))\n",
        "\n",
        "  return model, accuracy_score(test_y, pred_test), f1_score(test_y, pred_test)"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dSgwQ52Fadd4",
        "outputId": "75fe3e1a-ad80-407d-87e6-d72ba38df682"
      },
      "source": [
        "\n",
        "model = LogisticRegression(max_iter= 10000)\n",
        "model_name, accuracy, f1score = run_model_validate(model, train_X, train_y, test_X, test_y)\n"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9103146792492107\n",
            "Test Accuracy :  0.9104670930138428\n",
            "Train F1 Score :  0.5114345114345115\n",
            "Test F1 Score :  0.4981851179673321\n",
            "Train Confusion Matrix :  [[24885   667]\n",
            " [ 1918  1353]]\n",
            "Test Confusion Matrix :  [[10698   287]\n",
            " [  819   549]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     25552\n",
            "           1       0.67      0.41      0.51      3271\n",
            "\n",
            "    accuracy                           0.91     28823\n",
            "   macro avg       0.80      0.69      0.73     28823\n",
            "weighted avg       0.90      0.91      0.90     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     10985\n",
            "           1       0.66      0.40      0.50      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.79      0.69      0.72     12353\n",
            "weighted avg       0.90      0.91      0.90     12353\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mjTSEfZPiY4e",
        "outputId": "919c383f-805b-429b-e40a-3b3b34cc1fa7"
      },
      "source": [
        "model = [LogisticRegression(penalty = 'none'), LogisticRegression(solver = 'liblinear',penalty='l1'), LogisticRegression(penalty='l2'), \n",
        "        LogisticRegression(l1_ratio=0.5, solver = 'saga', penalty = 'elasticnet'),\n",
        "         KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), AdaBoostClassifier(), GradientBoostingClassifier()\n",
        "]\n",
        "\n",
        "result = []\n",
        "for i in model:\n",
        "  model_name, accuracy, f1score = run_model_validate(i, train_X, train_y, test_X, test_y)\n",
        "  result.append([model_name, accuracy, f1score])\n",
        "\n"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.908545258994553\n",
            "Test Accuracy :  0.9094956690682425\n",
            "Train F1 Score :  0.49385560675883255\n",
            "Test F1 Score :  0.482886216466235\n",
            "Train Confusion Matrix :  [[24901   651]\n",
            " [ 1985  1286]]\n",
            "Test Confusion Matrix :  [[10713   272]\n",
            " [  846   522]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     25552\n",
            "           1       0.66      0.39      0.49      3271\n",
            "\n",
            "    accuracy                           0.91     28823\n",
            "   macro avg       0.80      0.68      0.72     28823\n",
            "weighted avg       0.90      0.91      0.90     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.98      0.95     10985\n",
            "           1       0.66      0.38      0.48      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.79      0.68      0.72     12353\n",
            "weighted avg       0.90      0.91      0.90     12353\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9112514311487354\n",
            "Test Accuracy :  0.9112766129685097\n",
            "Train F1 Score :  0.5193536264562195\n",
            "Test F1 Score :  0.5013648771610555\n",
            "Train Confusion Matrix :  [[24883   669]\n",
            " [ 1889  1382]]\n",
            "Test Confusion Matrix :  [[10706   279]\n",
            " [  817   551]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     25552\n",
            "           1       0.67      0.42      0.52      3271\n",
            "\n",
            "    accuracy                           0.91     28823\n",
            "   macro avg       0.80      0.70      0.74     28823\n",
            "weighted avg       0.90      0.91      0.90     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     10985\n",
            "           1       0.66      0.40      0.50      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.80      0.69      0.73     12353\n",
            "weighted avg       0.90      0.91      0.90     12353\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9099330395864413\n",
            "Test Accuracy :  0.9103051890229095\n",
            "Train F1 Score :  0.5092627599243856\n",
            "Test F1 Score :  0.5\n",
            "Train Confusion Matrix :  [[24880   672]\n",
            " [ 1924  1347]]\n",
            "Test Confusion Matrix :  [[10691   294]\n",
            " [  814   554]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     25552\n",
            "           1       0.67      0.41      0.51      3271\n",
            "\n",
            "    accuracy                           0.91     28823\n",
            "   macro avg       0.80      0.69      0.73     28823\n",
            "weighted avg       0.90      0.91      0.90     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     10985\n",
            "           1       0.65      0.40      0.50      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.79      0.69      0.73     12353\n",
            "weighted avg       0.90      0.91      0.90     12353\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9059778648995593\n",
            "Test Accuracy :  0.9060956852586416\n",
            "Train F1 Score :  0.43915562913907286\n",
            "Test F1 Score :  0.4285714285714286\n",
            "Train Confusion Matrix :  [[25052   500]\n",
            " [ 2210  1061]]\n",
            "Test Confusion Matrix :  [[10758   227]\n",
            " [  933   435]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.98      0.95     25552\n",
            "           1       0.68      0.32      0.44      3271\n",
            "\n",
            "    accuracy                           0.91     28823\n",
            "   macro avg       0.80      0.65      0.69     28823\n",
            "weighted avg       0.89      0.91      0.89     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.98      0.95     10985\n",
            "           1       0.66      0.32      0.43      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.79      0.65      0.69     12353\n",
            "weighted avg       0.89      0.91      0.89     12353\n",
            "\n",
            "Train Accuracy :  0.930645664920376\n",
            "Test Accuracy :  0.9070671092042419\n",
            "Train F1 Score :  0.6646535816138233\n",
            "Test F1 Score :  0.5437201907790143\n",
            "Train Confusion Matrix :  [[24843   709]\n",
            " [ 1290  1981]]\n",
            "Test Confusion Matrix :  [[10521   464]\n",
            " [  684   684]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96     25552\n",
            "           1       0.74      0.61      0.66      3271\n",
            "\n",
            "    accuracy                           0.93     28823\n",
            "   macro avg       0.84      0.79      0.81     28823\n",
            "weighted avg       0.93      0.93      0.93     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95     10985\n",
            "           1       0.60      0.50      0.54      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.77      0.73      0.75     12353\n",
            "weighted avg       0.90      0.91      0.90     12353\n",
            "\n",
            "Train Accuracy :  1.0\n",
            "Test Accuracy :  0.8827815105642354\n",
            "Train F1 Score :  1.0\n",
            "Test F1 Score :  0.48211731044349065\n",
            "Train Confusion Matrix :  [[25552     0]\n",
            " [    0  3271]]\n",
            "Test Confusion Matrix :  [[10231   754]\n",
            " [  694   674]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     25552\n",
            "           1       1.00      1.00      1.00      3271\n",
            "\n",
            "    accuracy                           1.00     28823\n",
            "   macro avg       1.00      1.00      1.00     28823\n",
            "weighted avg       1.00      1.00      1.00     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.93      0.93     10985\n",
            "           1       0.47      0.49      0.48      1368\n",
            "\n",
            "    accuracy                           0.88     12353\n",
            "   macro avg       0.70      0.71      0.71     12353\n",
            "weighted avg       0.89      0.88      0.88     12353\n",
            "\n",
            "Train Accuracy :  0.9999653054852028\n",
            "Test Accuracy :  0.9133004128551769\n",
            "Train F1 Score :  0.9998471181776487\n",
            "Test F1 Score :  0.5365642578970142\n",
            "Train Confusion Matrix :  [[25552     0]\n",
            " [    1  3270]]\n",
            "Test Confusion Matrix :  [[10662   323]\n",
            " [  748   620]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     25552\n",
            "           1       1.00      1.00      1.00      3271\n",
            "\n",
            "    accuracy                           1.00     28823\n",
            "   macro avg       1.00      1.00      1.00     28823\n",
            "weighted avg       1.00      1.00      1.00     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     10985\n",
            "           1       0.66      0.45      0.54      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.80      0.71      0.74     12353\n",
            "weighted avg       0.90      0.91      0.91     12353\n",
            "\n",
            "Train Accuracy :  0.9090309822017139\n",
            "Test Accuracy :  0.9089290050999758\n",
            "Train F1 Score :  0.4988532110091743\n",
            "Test F1 Score :  0.484181568088033\n",
            "Train Confusion Matrix :  [[24896   656]\n",
            " [ 1966  1305]]\n",
            "Test Confusion Matrix :  [[10700   285]\n",
            " [  840   528]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     25552\n",
            "           1       0.67      0.40      0.50      3271\n",
            "\n",
            "    accuracy                           0.91     28823\n",
            "   macro avg       0.80      0.69      0.72     28823\n",
            "weighted avg       0.90      0.91      0.90     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.97      0.95     10985\n",
            "           1       0.65      0.39      0.48      1368\n",
            "\n",
            "    accuracy                           0.91     12353\n",
            "   macro avg       0.79      0.68      0.72     12353\n",
            "weighted avg       0.90      0.91      0.90     12353\n",
            "\n",
            "Train Accuracy :  0.922874093605801\n",
            "Test Accuracy :  0.9162146846919776\n",
            "Train F1 Score :  0.6158631415241057\n",
            "Test F1 Score :  0.5749486652977412\n",
            "Train Confusion Matrix :  [[24818   734]\n",
            " [ 1489  1782]]\n",
            "Test Confusion Matrix :  [[10618   367]\n",
            " [  668   700]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.96     25552\n",
            "           1       0.71      0.54      0.62      3271\n",
            "\n",
            "    accuracy                           0.92     28823\n",
            "   macro avg       0.83      0.76      0.79     28823\n",
            "weighted avg       0.92      0.92      0.92     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.95     10985\n",
            "           1       0.66      0.51      0.57      1368\n",
            "\n",
            "    accuracy                           0.92     12353\n",
            "   macro avg       0.80      0.74      0.76     12353\n",
            "weighted avg       0.91      0.92      0.91     12353\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "aIe0K0oum_jD",
        "outputId": "513a77e5-6b3a-46b6-f9ae-e9f2dba4518a"
      },
      "source": [
        "pd.DataFrame(result, columns = [\"Model\", \"Accuracy\", \"F1 Score\"])"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.909496</td>\n",
              "      <td>0.482886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.911277</td>\n",
              "      <td>0.501365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.910305</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.906096</td>\n",
              "      <td>0.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
              "      <td>0.907067</td>\n",
              "      <td>0.543720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DecisionTreeClassifier(ccp_alpha=0.0, class_we...</td>\n",
              "      <td>0.882782</td>\n",
              "      <td>0.482117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.913300</td>\n",
              "      <td>0.536564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.908929</td>\n",
              "      <td>0.484182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>([DecisionTreeRegressor(ccp_alpha=0.0, criteri...</td>\n",
              "      <td>0.916215</td>\n",
              "      <td>0.574949</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               Model  Accuracy  F1 Score\n",
              "0  LogisticRegression(C=1.0, class_weight=None, d...  0.909496  0.482886\n",
              "1  LogisticRegression(C=1.0, class_weight=None, d...  0.911277  0.501365\n",
              "2  LogisticRegression(C=1.0, class_weight=None, d...  0.910305  0.500000\n",
              "3  LogisticRegression(C=1.0, class_weight=None, d...  0.906096  0.428571\n",
              "4  KNeighborsClassifier(algorithm='auto', leaf_si...  0.907067  0.543720\n",
              "5  DecisionTreeClassifier(ccp_alpha=0.0, class_we...  0.882782  0.482117\n",
              "6  (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.913300  0.536564\n",
              "7  (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.908929  0.484182\n",
              "8  ([DecisionTreeRegressor(ccp_alpha=0.0, criteri...  0.916215  0.574949"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yT_biJe8ncRr",
        "outputId": "7b523bc8-9b3b-48cb-963c-a61e8c906894"
      },
      "source": [
        "result"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                     intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                     multi_class='auto', n_jobs=None, penalty='none',\n",
              "                     random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                     warm_start=False), 0.9094956690682425, 0.482886216466235],\n",
              " [LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                     intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                     multi_class='auto', n_jobs=None, penalty='l1',\n",
              "                     random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
              "                     warm_start=False),\n",
              "  0.9112766129685097,\n",
              "  0.5013648771610555],\n",
              " [LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                     intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                     multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                     random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                     warm_start=False), 0.9103051890229095, 0.5],\n",
              " [LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                     intercept_scaling=1, l1_ratio=0.5, max_iter=100,\n",
              "                     multi_class='auto', n_jobs=None, penalty='elasticnet',\n",
              "                     random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
              "                     warm_start=False),\n",
              "  0.9060956852586416,\n",
              "  0.4285714285714286],\n",
              " [KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                       metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                       weights='uniform'),\n",
              "  0.9070671092042419,\n",
              "  0.5437201907790143],\n",
              " [DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                         max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                         min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                         min_samples_leaf=1, min_samples_split=2,\n",
              "                         min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                         random_state=None, splitter='best'),\n",
              "  0.8827815105642354,\n",
              "  0.48211731044349065],\n",
              " [RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                         criterion='gini', max_depth=None, max_features='auto',\n",
              "                         max_leaf_nodes=None, max_samples=None,\n",
              "                         min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                         min_samples_leaf=1, min_samples_split=2,\n",
              "                         min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                         n_jobs=None, oob_score=False, random_state=None,\n",
              "                         verbose=0, warm_start=False),\n",
              "  0.9133004128551769,\n",
              "  0.5365642578970142],\n",
              " [AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
              "                     n_estimators=50, random_state=None),\n",
              "  0.9089290050999758,\n",
              "  0.484181568088033],\n",
              " [GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                             learning_rate=0.1, loss='deviance', max_depth=3,\n",
              "                             max_features=None, max_leaf_nodes=None,\n",
              "                             min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                             min_samples_leaf=1, min_samples_split=2,\n",
              "                             min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                             n_iter_no_change=None, presort='deprecated',\n",
              "                             random_state=None, subsample=1.0, tol=0.0001,\n",
              "                             validation_fraction=0.1, verbose=0,\n",
              "                             warm_start=False),\n",
              "  0.9162146846919776,\n",
              "  0.5749486652977412]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otGkJ4WZnxlJ"
      },
      "source": [
        "model1 = GradientBoostingClassifier()\n",
        "model2 = RandomForestClassifier()\n",
        "model3 = KNeighborsClassifier()\n",
        "\n",
        "estimators = []\n",
        "estimators.append(('GBM', model1))\n",
        "estimators.append(('RF', model2))\n",
        "estimators.append(('KNN', model3))\n",
        "\n",
        "voting_model = VotingClassifier(estimators)"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d25hSqp5oyJm",
        "outputId": "2b2e93f8-cf55-4b22-8cfc-44b24d83616b"
      },
      "source": [
        "model_name, accuracy, f1score = run_model_validate(voting_model, train_X, train_y, test_X, test_y)\n",
        "result.append([model_name, accuracy, f1score])"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.95236443118343\n",
            "Test Accuracy :  0.9160527807010442\n",
            "Train F1 Score :  0.7681134943421719\n",
            "Test F1 Score :  0.5662902551233794\n",
            "Train Confusion Matrix :  [[25176   376]\n",
            " [  997  2274]]\n",
            "Test Confusion Matrix :  [[10639   346]\n",
            " [  691   677]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97     25552\n",
            "           1       0.86      0.70      0.77      3271\n",
            "\n",
            "    accuracy                           0.95     28823\n",
            "   macro avg       0.91      0.84      0.87     28823\n",
            "weighted avg       0.95      0.95      0.95     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.95     10985\n",
            "           1       0.66      0.49      0.57      1368\n",
            "\n",
            "    accuracy                           0.92     12353\n",
            "   macro avg       0.80      0.73      0.76     12353\n",
            "weighted avg       0.91      0.92      0.91     12353\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9wKppvYqR7G"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPMpRinYpZ47",
        "outputId": "6b6bdc8c-2ece-4453-cf23-a9417e15ca4e"
      },
      "source": [
        "voting_model = VotingClassifier(estimators, voting = 'soft')\n",
        "model_name, accuracy, f1score = run_model_validate(voting_model, train_X, train_y, test_X, test_y)\n",
        "result.append([model_name, accuracy, f1score])"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9646809839364396\n",
            "Test Accuracy :  0.9154051647373108\n",
            "Train F1 Score :  0.8274576271186441\n",
            "Test F1 Score :  0.5658496053178229\n",
            "Train Confusion Matrix :  [[25364   188]\n",
            " [  830  2441]]\n",
            "Test Confusion Matrix :  [[10627   358]\n",
            " [  687   681]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98     25552\n",
            "           1       0.93      0.75      0.83      3271\n",
            "\n",
            "    accuracy                           0.96     28823\n",
            "   macro avg       0.95      0.87      0.90     28823\n",
            "weighted avg       0.96      0.96      0.96     28823\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.95     10985\n",
            "           1       0.66      0.50      0.57      1368\n",
            "\n",
            "    accuracy                           0.92     12353\n",
            "   macro avg       0.80      0.73      0.76     12353\n",
            "weighted avg       0.91      0.92      0.91     12353\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "7vxVxQdrpqQT",
        "outputId": "c0b536d4-1996-4c89-bb36-6d219a26dd04"
      },
      "source": [
        "pd.DataFrame(result, columns = [\"Model\", \"Accuracy\", \"F1 Score\"])"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.909496</td>\n",
              "      <td>0.482886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.911277</td>\n",
              "      <td>0.501365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.910305</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.906096</td>\n",
              "      <td>0.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
              "      <td>0.907067</td>\n",
              "      <td>0.543720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DecisionTreeClassifier(ccp_alpha=0.0, class_we...</td>\n",
              "      <td>0.882782</td>\n",
              "      <td>0.482117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.913300</td>\n",
              "      <td>0.536564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.908929</td>\n",
              "      <td>0.484182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>([DecisionTreeRegressor(ccp_alpha=0.0, criteri...</td>\n",
              "      <td>0.916215</td>\n",
              "      <td>0.574949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>VotingClassifier(estimators=[('GBM',\\n        ...</td>\n",
              "      <td>0.916053</td>\n",
              "      <td>0.566290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>VotingClassifier(estimators=[('GBM',\\n        ...</td>\n",
              "      <td>0.915405</td>\n",
              "      <td>0.565850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Model  Accuracy  F1 Score\n",
              "0   LogisticRegression(C=1.0, class_weight=None, d...  0.909496  0.482886\n",
              "1   LogisticRegression(C=1.0, class_weight=None, d...  0.911277  0.501365\n",
              "2   LogisticRegression(C=1.0, class_weight=None, d...  0.910305  0.500000\n",
              "3   LogisticRegression(C=1.0, class_weight=None, d...  0.906096  0.428571\n",
              "4   KNeighborsClassifier(algorithm='auto', leaf_si...  0.907067  0.543720\n",
              "5   DecisionTreeClassifier(ccp_alpha=0.0, class_we...  0.882782  0.482117\n",
              "6   (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.913300  0.536564\n",
              "7   (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.908929  0.484182\n",
              "8   ([DecisionTreeRegressor(ccp_alpha=0.0, criteri...  0.916215  0.574949\n",
              "9   VotingClassifier(estimators=[('GBM',\\n        ...  0.916053  0.566290\n",
              "10  VotingClassifier(estimators=[('GBM',\\n        ...  0.915405  0.565850"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "byw7Kp-jrKzp",
        "outputId": "7d4441f9-585b-4bea-c51a-cdbc1765b0f4"
      },
      "source": [
        "data[\"y\"].value_counts().plot(kind = \"pie\", figsize = (10,10))"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2acbf9aa58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eazkk2dlrp-1",
        "outputId": "5503e603-2366-497b-d72e-1db264ce3084"
      },
      "source": [
        "data_preprocessed.y.value_counts()"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    36537\n",
              "1     4639\n",
              "Name: y, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwiPTOuarp6c"
      },
      "source": [
        "accept = data_preprocessed[data_preprocessed[\"y\"]==1]"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrMafpzosFqT",
        "outputId": "81e3e0c1-efad-400b-d770-64b8a91c500d"
      },
      "source": [
        "accept.shape"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4639, 62)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JLCQTb4rp2t"
      },
      "source": [
        "reject = data_preprocessed[data_preprocessed[\"y\"]==0]"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOT4eiqwrp03",
        "outputId": "5c88fc81-3016-4485-f360-43c9b78087dd"
      },
      "source": [
        "reject.shape"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(36537, 62)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DaOvzrWXrpxB"
      },
      "source": [
        "selected_reject = reject.sample(n = 4639, random_state= 20)"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-7sGMx7rpsy"
      },
      "source": [
        "data_undersample = pd.concat([accept,selected_reject ])"
      ],
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmhwcCNtrpkL",
        "outputId": "6d7acf81-1350-4b37-9400-1441ec25b12a"
      },
      "source": [
        "data_undersample.shape"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9278, 62)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "id": "On5Vss8Ms7kE",
        "outputId": "c45ad110-b332-4153-be58-3967e3141e19"
      },
      "source": [
        "data_undersample[\"y\"].value_counts().plot(kind = \"pie\", figsize = (10,10))"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ad4f4c748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWWNNYN_s7am"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgYS6a-TtNrG"
      },
      "source": [
        "X = data_undersample.drop(columns='y')"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXMhHobItNrH"
      },
      "source": [
        "y = data_undersample['y']"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4W_9qEaqtNrH"
      },
      "source": [
        "#Train Test split\n",
        "\n",
        "train_X, test_X, train_y, test_y = train_test_split(X,y, test_size=0.3, random_state = 24)"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vW1pweprtNrH",
        "outputId": "e96251e8-6d09-4a0b-ea40-fef5c87e6072"
      },
      "source": [
        "train_X.shape, test_X.shape, train_y.shape, test_y.shape"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6494, 61), (2784, 61), (6494,), (2784,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2DdkgftttNrH",
        "outputId": "01b7a0d7-482d-4fab-8be8-3d3f57c1a0e1"
      },
      "source": [
        "data_undersample['y'].value_counts(normalize = True)"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.5\n",
              "0    0.5\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXEBWMGftNrH",
        "outputId": "0e1b87e5-52a8-4723-b4cb-712bc659bd70"
      },
      "source": [
        "train_y.value_counts(normalize = True)"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.500154\n",
              "0    0.499846\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SkvzrmItNrI",
        "outputId": "3c48db3d-0a0e-4822-9034-38609e274797"
      },
      "source": [
        "test_y.value_counts(normalize = True)"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.500359\n",
              "1    0.499641\n",
              "Name: y, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxliDrsbtfVF",
        "outputId": "1e09a9d3-d7b4-4762-d7b6-d51e28c8dfad"
      },
      "source": [
        "model = [LogisticRegression(penalty = 'none'), LogisticRegression(solver = 'liblinear',penalty='l1'), LogisticRegression(penalty='l2'), \n",
        "        LogisticRegression(l1_ratio=0.5, solver = 'saga', penalty = 'elasticnet'),\n",
        "         KNeighborsClassifier(), DecisionTreeClassifier(), RandomForestClassifier(), AdaBoostClassifier(), GradientBoostingClassifier()\n",
        "]\n",
        "\n",
        "result = []\n",
        "for i in model:\n",
        "  model_name, accuracy, f1score = run_model_validate(i, train_X, train_y, test_X, test_y)\n",
        "  result.append([model_name, accuracy, f1score])"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.8647982753310748\n",
            "Test Accuracy :  0.8613505747126436\n",
            "Train F1 Score :  0.8656259565350475\n",
            "Test F1 Score :  0.8627311522048365\n",
            "Train Confusion Matrix :  [[2788  458]\n",
            " [ 420 2828]]\n",
            "Test Confusion Matrix :  [[1185  208]\n",
            " [ 178 1213]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.86      0.86      3246\n",
            "           1       0.86      0.87      0.87      3248\n",
            "\n",
            "    accuracy                           0.86      6494\n",
            "   macro avg       0.86      0.86      0.86      6494\n",
            "weighted avg       0.86      0.86      0.86      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.85      0.86      1393\n",
            "           1       0.85      0.87      0.86      1391\n",
            "\n",
            "    accuracy                           0.86      2784\n",
            "   macro avg       0.86      0.86      0.86      2784\n",
            "weighted avg       0.86      0.86      0.86      2784\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.8744995380351094\n",
            "Test Accuracy :  0.8724856321839081\n",
            "Train F1 Score :  0.8761586385047865\n",
            "Test F1 Score :  0.8750439985920452\n",
            "Train Confusion Matrix :  [[2796  450]\n",
            " [ 365 2883]]\n",
            "Test Confusion Matrix :  [[1186  207]\n",
            " [ 148 1243]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.86      0.87      3246\n",
            "           1       0.86      0.89      0.88      3248\n",
            "\n",
            "    accuracy                           0.87      6494\n",
            "   macro avg       0.87      0.87      0.87      6494\n",
            "weighted avg       0.87      0.87      0.87      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.85      0.87      1393\n",
            "           1       0.86      0.89      0.88      1391\n",
            "\n",
            "    accuracy                           0.87      2784\n",
            "   macro avg       0.87      0.87      0.87      2784\n",
            "weighted avg       0.87      0.87      0.87      2784\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.862026485987065\n",
            "Test Accuracy :  0.8577586206896551\n",
            "Train F1 Score :  0.8624501074608535\n",
            "Test F1 Score :  0.8587731811697575\n",
            "Train Confusion Matrix :  [[2789  457]\n",
            " [ 439 2809]]\n",
            "Test Confusion Matrix :  [[1184  209]\n",
            " [ 187 1204]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.86      0.86      3246\n",
            "           1       0.86      0.86      0.86      3248\n",
            "\n",
            "    accuracy                           0.86      6494\n",
            "   macro avg       0.86      0.86      0.86      6494\n",
            "weighted avg       0.86      0.86      0.86      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.85      0.86      1393\n",
            "           1       0.85      0.87      0.86      1391\n",
            "\n",
            "    accuracy                           0.86      2784\n",
            "   macro avg       0.86      0.86      0.86      2784\n",
            "weighted avg       0.86      0.86      0.86      2784\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.7802587003387742\n",
            "Test Accuracy :  0.7823275862068966\n",
            "Train F1 Score :  0.7619683069224353\n",
            "Test F1 Score :  0.7610410094637224\n",
            "Train Confusion Matrix :  [[2783  463]\n",
            " [ 964 2284]]\n",
            "Test Confusion Matrix :  [[1213  180]\n",
            " [ 426  965]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.86      0.80      3246\n",
            "           1       0.83      0.70      0.76      3248\n",
            "\n",
            "    accuracy                           0.78      6494\n",
            "   macro avg       0.79      0.78      0.78      6494\n",
            "weighted avg       0.79      0.78      0.78      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.74      0.87      0.80      1393\n",
            "           1       0.84      0.69      0.76      1391\n",
            "\n",
            "    accuracy                           0.78      2784\n",
            "   macro avg       0.79      0.78      0.78      2784\n",
            "weighted avg       0.79      0.78      0.78      2784\n",
            "\n",
            "Train Accuracy :  0.8925161687711733\n",
            "Test Accuracy :  0.8498563218390804\n",
            "Train F1 Score :  0.8945937783147087\n",
            "Test F1 Score :  0.8519830028328612\n",
            "Train Confusion Matrix :  [[2834  412]\n",
            " [ 286 2962]]\n",
            "Test Confusion Matrix :  [[1163  230]\n",
            " [ 188 1203]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.87      0.89      3246\n",
            "           1       0.88      0.91      0.89      3248\n",
            "\n",
            "    accuracy                           0.89      6494\n",
            "   macro avg       0.89      0.89      0.89      6494\n",
            "weighted avg       0.89      0.89      0.89      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.83      0.85      1393\n",
            "           1       0.84      0.86      0.85      1391\n",
            "\n",
            "    accuracy                           0.85      2784\n",
            "   macro avg       0.85      0.85      0.85      2784\n",
            "weighted avg       0.85      0.85      0.85      2784\n",
            "\n",
            "Train Accuracy :  1.0\n",
            "Test Accuracy :  0.8344109195402298\n",
            "Train F1 Score :  1.0\n",
            "Test F1 Score :  0.8349445041174365\n",
            "Train Confusion Matrix :  [[3246    0]\n",
            " [   0 3248]]\n",
            "Test Confusion Matrix :  [[1157  236]\n",
            " [ 225 1166]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      3246\n",
            "           1       1.00      1.00      1.00      3248\n",
            "\n",
            "    accuracy                           1.00      6494\n",
            "   macro avg       1.00      1.00      1.00      6494\n",
            "weighted avg       1.00      1.00      1.00      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.83      0.83      1393\n",
            "           1       0.83      0.84      0.83      1391\n",
            "\n",
            "    accuracy                           0.83      2784\n",
            "   macro avg       0.83      0.83      0.83      2784\n",
            "weighted avg       0.83      0.83      0.83      2784\n",
            "\n",
            "Train Accuracy :  1.0\n",
            "Test Accuracy :  0.8904454022988506\n",
            "Train F1 Score :  1.0\n",
            "Test F1 Score :  0.8947186744908526\n",
            "Train Confusion Matrix :  [[3246    0]\n",
            " [   0 3248]]\n",
            "Test Confusion Matrix :  [[1183  210]\n",
            " [  95 1296]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      3246\n",
            "           1       1.00      1.00      1.00      3248\n",
            "\n",
            "    accuracy                           1.00      6494\n",
            "   macro avg       1.00      1.00      1.00      6494\n",
            "weighted avg       1.00      1.00      1.00      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.85      0.89      1393\n",
            "           1       0.86      0.93      0.89      1391\n",
            "\n",
            "    accuracy                           0.89      2784\n",
            "   macro avg       0.89      0.89      0.89      2784\n",
            "weighted avg       0.89      0.89      0.89      2784\n",
            "\n",
            "Train Accuracy :  0.8746535263319988\n",
            "Test Accuracy :  0.875\n",
            "Train F1 Score :  0.8754589963280294\n",
            "Test F1 Score :  0.8761565836298932\n",
            "Train Confusion Matrix :  [[2819  427]\n",
            " [ 387 2861]]\n",
            "Test Confusion Matrix :  [[1205  188]\n",
            " [ 160 1231]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.87      0.87      3246\n",
            "           1       0.87      0.88      0.88      3248\n",
            "\n",
            "    accuracy                           0.87      6494\n",
            "   macro avg       0.87      0.87      0.87      6494\n",
            "weighted avg       0.87      0.87      0.87      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.87      0.87      1393\n",
            "           1       0.87      0.88      0.88      1391\n",
            "\n",
            "    accuracy                           0.88      2784\n",
            "   macro avg       0.88      0.88      0.87      2784\n",
            "weighted avg       0.88      0.88      0.87      2784\n",
            "\n",
            "Train Accuracy :  0.9006775485063135\n",
            "Test Accuracy :  0.8904454022988506\n",
            "Train F1 Score :  0.9041747140098054\n",
            "Test F1 Score :  0.8949362728212195\n",
            "Train Confusion Matrix :  [[2806  440]\n",
            " [ 205 3043]]\n",
            "Test Confusion Matrix :  [[1180  213]\n",
            " [  92 1299]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.86      0.90      3246\n",
            "           1       0.87      0.94      0.90      3248\n",
            "\n",
            "    accuracy                           0.90      6494\n",
            "   macro avg       0.90      0.90      0.90      6494\n",
            "weighted avg       0.90      0.90      0.90      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.85      0.89      1393\n",
            "           1       0.86      0.93      0.89      1391\n",
            "\n",
            "    accuracy                           0.89      2784\n",
            "   macro avg       0.89      0.89      0.89      2784\n",
            "weighted avg       0.89      0.89      0.89      2784\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "Lu9GOpW2tfSN",
        "outputId": "6469e99d-0f76-48ec-a80b-7215477a5b24"
      },
      "source": [
        "pd.DataFrame(result, columns = [\"Model\", \"Accuracy\", \"F1 Score\"])"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.861351</td>\n",
              "      <td>0.862731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.872486</td>\n",
              "      <td>0.875044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.857759</td>\n",
              "      <td>0.858773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.782328</td>\n",
              "      <td>0.761041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
              "      <td>0.849856</td>\n",
              "      <td>0.851983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DecisionTreeClassifier(ccp_alpha=0.0, class_we...</td>\n",
              "      <td>0.834411</td>\n",
              "      <td>0.834945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.890445</td>\n",
              "      <td>0.894719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.876157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>([DecisionTreeRegressor(ccp_alpha=0.0, criteri...</td>\n",
              "      <td>0.890445</td>\n",
              "      <td>0.894936</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               Model  Accuracy  F1 Score\n",
              "0  LogisticRegression(C=1.0, class_weight=None, d...  0.861351  0.862731\n",
              "1  LogisticRegression(C=1.0, class_weight=None, d...  0.872486  0.875044\n",
              "2  LogisticRegression(C=1.0, class_weight=None, d...  0.857759  0.858773\n",
              "3  LogisticRegression(C=1.0, class_weight=None, d...  0.782328  0.761041\n",
              "4  KNeighborsClassifier(algorithm='auto', leaf_si...  0.849856  0.851983\n",
              "5  DecisionTreeClassifier(ccp_alpha=0.0, class_we...  0.834411  0.834945\n",
              "6  (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.890445  0.894719\n",
              "7  (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.875000  0.876157\n",
              "8  ([DecisionTreeRegressor(ccp_alpha=0.0, criteri...  0.890445  0.894936"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7VruzhJtfPm"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bgr54B24t4QJ"
      },
      "source": [
        "model1 = GradientBoostingClassifier()\n",
        "model2 = RandomForestClassifier()\n",
        "model3 = LogisticRegression(solver = 'liblinear',penalty='l1')\n",
        "\n",
        "\n",
        "estimators = []\n",
        "estimators.append(('GBM', model1))\n",
        "estimators.append(('RF', model2))\n",
        "estimators.append(('LR', model3))\n",
        "\n",
        "voting_model = VotingClassifier(estimators)"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmFwOPBSt4QJ",
        "outputId": "dae2c9f7-fdd8-4aca-99da-3d475976fe14"
      },
      "source": [
        "model_name, accuracy, f1score = run_model_validate(voting_model, train_X, train_y, test_X, test_y)\n",
        "result.append([model_name, accuracy, f1score])"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9145364952263628\n",
            "Test Accuracy :  0.8904454022988506\n",
            "Train F1 Score :  0.9169037281030095\n",
            "Test F1 Score :  0.8950808393532851\n",
            "Train Confusion Matrix :  [[2877  369]\n",
            " [ 186 3062]]\n",
            "Test Confusion Matrix :  [[1178  215]\n",
            " [  90 1301]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.89      0.91      3246\n",
            "           1       0.89      0.94      0.92      3248\n",
            "\n",
            "    accuracy                           0.91      6494\n",
            "   macro avg       0.92      0.91      0.91      6494\n",
            "weighted avg       0.92      0.91      0.91      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.85      0.89      1393\n",
            "           1       0.86      0.94      0.90      1391\n",
            "\n",
            "    accuracy                           0.89      2784\n",
            "   macro avg       0.89      0.89      0.89      2784\n",
            "weighted avg       0.89      0.89      0.89      2784\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yA7hw7cWt4QK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27A1RRedt4QK",
        "outputId": "e9e9d379-1014-441c-aa3d-5d782672239f"
      },
      "source": [
        "voting_model = VotingClassifier(estimators, voting = 'soft')\n",
        "model_name, accuracy, f1score = run_model_validate(voting_model, train_X, train_y, test_X, test_y)\n",
        "result.append([model_name, accuracy, f1score])"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9320911610717586\n",
            "Test Accuracy :  0.8904454022988506\n",
            "Train F1 Score :  0.9341692789968652\n",
            "Test F1 Score :  0.895225008588114\n",
            "Train Confusion Matrix :  [[2924  322]\n",
            " [ 119 3129]]\n",
            "Test Confusion Matrix :  [[1176  217]\n",
            " [  88 1303]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.90      0.93      3246\n",
            "           1       0.91      0.96      0.93      3248\n",
            "\n",
            "    accuracy                           0.93      6494\n",
            "   macro avg       0.93      0.93      0.93      6494\n",
            "weighted avg       0.93      0.93      0.93      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.84      0.89      1393\n",
            "           1       0.86      0.94      0.90      1391\n",
            "\n",
            "    accuracy                           0.89      2784\n",
            "   macro avg       0.89      0.89      0.89      2784\n",
            "weighted avg       0.89      0.89      0.89      2784\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "Kk5fK63Ct4QK",
        "outputId": "85dda60b-4135-4560-f1c9-9d6d9078ae35"
      },
      "source": [
        "pd.DataFrame(result, columns = [\"Model\", \"Accuracy\", \"F1 Score\"])"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.861351</td>\n",
              "      <td>0.862731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.872486</td>\n",
              "      <td>0.875044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.857759</td>\n",
              "      <td>0.858773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LogisticRegression(C=1.0, class_weight=None, d...</td>\n",
              "      <td>0.782328</td>\n",
              "      <td>0.761041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNeighborsClassifier(algorithm='auto', leaf_si...</td>\n",
              "      <td>0.849856</td>\n",
              "      <td>0.851983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DecisionTreeClassifier(ccp_alpha=0.0, class_we...</td>\n",
              "      <td>0.834411</td>\n",
              "      <td>0.834945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.890445</td>\n",
              "      <td>0.894719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(DecisionTreeClassifier(ccp_alpha=0.0, class_w...</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.876157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>([DecisionTreeRegressor(ccp_alpha=0.0, criteri...</td>\n",
              "      <td>0.890445</td>\n",
              "      <td>0.894936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>VotingClassifier(estimators=[('GBM',\\n        ...</td>\n",
              "      <td>0.890445</td>\n",
              "      <td>0.895081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>VotingClassifier(estimators=[('GBM',\\n        ...</td>\n",
              "      <td>0.890445</td>\n",
              "      <td>0.895225</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Model  Accuracy  F1 Score\n",
              "0   LogisticRegression(C=1.0, class_weight=None, d...  0.861351  0.862731\n",
              "1   LogisticRegression(C=1.0, class_weight=None, d...  0.872486  0.875044\n",
              "2   LogisticRegression(C=1.0, class_weight=None, d...  0.857759  0.858773\n",
              "3   LogisticRegression(C=1.0, class_weight=None, d...  0.782328  0.761041\n",
              "4   KNeighborsClassifier(algorithm='auto', leaf_si...  0.849856  0.851983\n",
              "5   DecisionTreeClassifier(ccp_alpha=0.0, class_we...  0.834411  0.834945\n",
              "6   (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.890445  0.894719\n",
              "7   (DecisionTreeClassifier(ccp_alpha=0.0, class_w...  0.875000  0.876157\n",
              "8   ([DecisionTreeRegressor(ccp_alpha=0.0, criteri...  0.890445  0.894936\n",
              "9   VotingClassifier(estimators=[('GBM',\\n        ...  0.890445  0.895081\n",
              "10  VotingClassifier(estimators=[('GBM',\\n        ...  0.890445  0.895225"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQ55sLjNtfNa"
      },
      "source": [
        "model1 = GradientBoostingClassifier()\n",
        "model2 = RandomForestClassifier()\n",
        "model3 = LogisticRegression(solver = 'liblinear',penalty='l1')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTCabMxvtfLa",
        "outputId": "e83873df-471b-4f37-85fb-4e0fc88af7a9"
      },
      "source": [
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "\n",
        "clf1 = GradientBoostingClassifier()\n",
        "clf2 = RandomForestClassifier()\n",
        "clf3 = LogisticRegression(solver = 'liblinear',penalty='l1')\n",
        "\n",
        "lr = LogisticRegression()\n",
        "sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], \n",
        "                          meta_classifier=lr)\n",
        "\n",
        "print('3-fold cross validation:\\n')\n",
        "\n",
        "for clf, label in zip([clf1, clf2, clf3, sclf], \n",
        "                      ['GBM', \n",
        "                       'Random Forest', \n",
        "                       'Logistic Regression',\n",
        "                       'StackingClassifier']):\n",
        "\n",
        "    scores = cross_val_score(clf, X, y, cv=3)\n",
        "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" \n",
        "          % (scores.mean(), scores.std(), label))"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3-fold cross validation:\n",
            "\n",
            "Accuracy: 0.45 (+/- 0.01) [GBM]\n",
            "Accuracy: 0.45 (+/- 0.01) [Random Forest]\n",
            "Accuracy: 0.56 (+/- 0.08) [Logistic Regression]\n",
            "Accuracy: 0.45 (+/- 0.01) [StackingClassifier]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDFdpWH2tfJU"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "estimators = [('GBM', GradientBoostingClassifier()),\n",
        "              ('RF', RandomForestClassifier()),\n",
        "              ('LR', LogisticRegression(solver = 'liblinear',penalty='l1'))]\n",
        "\n",
        "clf = StackingClassifier(\n",
        "    estimators=estimators, final_estimator=LogisticRegression()\n",
        ")"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vz1Mqxt7tfDH",
        "outputId": "3e72b15f-f449-4723-80a7-193e53a7cc7f"
      },
      "source": [
        "model_name, accuracy, f1score = run_model_validate(clf, train_X, train_y, test_X, test_y)\n",
        "result.append([model_name, accuracy, f1score])"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train Accuracy :  0.9391746227286726\n",
            "Test Accuracy :  0.8933189655172413\n",
            "Train F1 Score :  0.9404492688074777\n",
            "Test F1 Score :  0.8973384030418251\n",
            "Train Confusion Matrix :  [[2980  266]\n",
            " [ 129 3119]]\n",
            "Test Confusion Matrix :  [[1189  204]\n",
            " [  93 1298]]\n",
            "Train Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.92      0.94      3246\n",
            "           1       0.92      0.96      0.94      3248\n",
            "\n",
            "    accuracy                           0.94      6494\n",
            "   macro avg       0.94      0.94      0.94      6494\n",
            "weighted avg       0.94      0.94      0.94      6494\n",
            "\n",
            "Test Classification Report :                precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.85      0.89      1393\n",
            "           1       0.86      0.93      0.90      1391\n",
            "\n",
            "    accuracy                           0.89      2784\n",
            "   macro avg       0.90      0.89      0.89      2784\n",
            "weighted avg       0.90      0.89      0.89      2784\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RnQ3ckpEte8u"
      },
      "source": [
        "final_result = pd.DataFrame(result, columns = [\"Model\", \"Accuracy\", \"F1 Score\"])"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "-MynqLPHygAD",
        "outputId": "70528a26-b282-4e89-9b7e-4dc15cc018d3"
      },
      "source": [
        "final_result[\"Accuracy\"].tail().plot(kind = \"bar\", figsize = (10,10))"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2ad62f0e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}