Employee_Turnover_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f6c70bd1",
   "metadata": {},
   "source": [
    "# Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c1691f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf087999",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('hr.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e803e7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93388f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years    sales salary  \n",
       "14994                      0  support    low  \n",
       "14995                      0  support    low  \n",
       "14996                      0  support    low  \n",
       "14997                      0  support    low  \n",
       "14998                      0  support    low  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26964a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     14999 non-null  float64\n",
      " 1   last_evaluation        14999 non-null  float64\n",
      " 2   number_project         14999 non-null  int64  \n",
      " 3   average_montly_hours   14999 non-null  int64  \n",
      " 4   time_spend_company     14999 non-null  int64  \n",
      " 5   Work_accident          14999 non-null  int64  \n",
      " 6   left                   14999 non-null  int64  \n",
      " 7   promotion_last_5years  14999 non-null  int64  \n",
      " 8   sales                  14999 non-null  object \n",
      " 9   salary                 14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c29c1166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad0d5e6c",
   "metadata": {},
   "source": [
    "Just under 15K observations over 10 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "48878adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       False\n",
       "last_evaluation          False\n",
       "number_project           False\n",
       "average_montly_hours     False\n",
       "time_spend_company       False\n",
       "Work_accident            False\n",
       "left                     False\n",
       "promotion_last_5years    False\n",
       "sales                    False\n",
       "salary                   False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data-values\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3548bd46",
   "metadata": {},
   "source": [
    "Arguably, this is a rarity but it is a clean dataset containing no missing values!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c4a3cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename column names \n",
    "df = df.rename(columns = {\n",
    "    \n",
    "    'satisfaction_level':'Satisfaction',\n",
    "    'last_evaluation':'Evaluation',\n",
    "    'number_project' : 'ProjectCount',\n",
    "    'average_montly_hours':'AvgMonthlyHours',\n",
    "    'time_spend_company':'Tenure',\n",
    "    'promotion_last_5years':'Promotion',\n",
    "    'sales':'Department',\n",
    "    'salary':'Salary',\n",
    "    'left':'Turnover'\n",
    "    \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "edecae7c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Evaluation</th>\n",
       "      <th>ProjectCount</th>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>Turnover</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Satisfaction  Evaluation  ProjectCount  AvgMonthlyHours  Tenure  \\\n",
       "0          0.38        0.53             2              157       3   \n",
       "1          0.80        0.86             5              262       6   \n",
       "2          0.11        0.88             7              272       4   \n",
       "3          0.72        0.87             5              223       5   \n",
       "4          0.37        0.52             2              159       3   \n",
       "\n",
       "   Work_accident  Turnover  Promotion Department  Salary  \n",
       "0              0         1          0      sales     low  \n",
       "1              0         1          0      sales  medium  \n",
       "2              0         1          0      sales  medium  \n",
       "3              0         1          0      sales     low  \n",
       "4              0         1          0      sales     low  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a07a569c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Evaluation</th>\n",
       "      <th>ProjectCount</th>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Satisfaction    Evaluation  ProjectCount  AvgMonthlyHours  \\\n",
       "count  14999.000000  14999.000000  14999.000000     14999.000000   \n",
       "mean       0.612834      0.716102      3.803054       201.050337   \n",
       "std        0.248631      0.171169      1.232592        49.943099   \n",
       "min        0.090000      0.360000      2.000000        96.000000   \n",
       "25%        0.440000      0.560000      3.000000       156.000000   \n",
       "50%        0.640000      0.720000      4.000000       200.000000   \n",
       "75%        0.820000      0.870000      5.000000       245.000000   \n",
       "max        1.000000      1.000000      7.000000       310.000000   \n",
       "\n",
       "             Tenure  Work_accident      Turnover     Promotion  \n",
       "count  14999.000000   14999.000000  14999.000000  14999.000000  \n",
       "mean       3.498233       0.144610      0.238083      0.021268  \n",
       "std        1.460136       0.351719      0.425924      0.144281  \n",
       "min        2.000000       0.000000      0.000000      0.000000  \n",
       "25%        3.000000       0.000000      0.000000      0.000000  \n",
       "50%        3.000000       0.000000      0.000000      0.000000  \n",
       "75%        4.000000       0.000000      0.000000      0.000000  \n",
       "max       10.000000       1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Breakdown of Summary Statistics\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df76717a",
   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf4188b",
   "metadata": {},
   "source": [
    "# Context\n",
    "\n",
    "This dataset contains - \n",
    "\n",
    "1. Roughly 15K employee observations of 10 parameters; \n",
    "2. Turnover rate near 24% (23.8%, in America, around 22% is considered average); \n",
    "3. Mean employee satisfaction if 0.612"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f9757c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Satisfaction       float64\n",
       "Evaluation         float64\n",
       "ProjectCount         int64\n",
       "AvgMonthlyHours      int64\n",
       "Tenure               int64\n",
       "Work_accident        int64\n",
       "Turnover             int64\n",
       "Promotion            int64\n",
       "Department          object\n",
       "Salary              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check columns for data-type(s)\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fda72c62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.761917\n",
       "1    0.238083\n",
       "Name: Turnover, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm turn-over rate\n",
    "\n",
    "turnover_rate = df.Turnover.value_counts(normalize=True)\n",
    "turnover_rate\n",
    "\n",
    "# 76% remained, 24% attrition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa39a7a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        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>Satisfaction</th>\n",
       "      <th>Evaluation</th>\n",
       "      <th>ProjectCount</th>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>Promotion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover</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>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Satisfaction  Evaluation  ProjectCount  AvgMonthlyHours    Tenure  \\\n",
       "Turnover                                                                      \n",
       "0             0.666810    0.715473      3.786664       199.060203  3.380032   \n",
       "1             0.440098    0.718113      3.855503       207.419210  3.876505   \n",
       "\n",
       "          Work_accident  Promotion  \n",
       "Turnover                            \n",
       "0              0.175009   0.026251  \n",
       "1              0.047326   0.005321  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "turnover_summary = df.groupby('Turnover')\n",
    "turnover_summary.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efa13c81",
   "metadata": {},
   "source": [
    "# Correlation(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "13ae91ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Evaluation</th>\n",
       "      <th>ProjectCount</th>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Promotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Satisfaction</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105021</td>\n",
       "      <td>-0.142970</td>\n",
       "      <td>-0.020048</td>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.025605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaluation</th>\n",
       "      <td>0.105021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>-0.008684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProjectCount</th>\n",
       "      <td>-0.142970</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>-0.006064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <td>-0.020048</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>-0.003544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>0.067433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>0.039245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover</th>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion</th>\n",
       "      <td>0.025605</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>-0.061788</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Satisfaction  Evaluation  ProjectCount  AvgMonthlyHours  \\\n",
       "Satisfaction         1.000000    0.105021     -0.142970        -0.020048   \n",
       "Evaluation           0.105021    1.000000      0.349333         0.339742   \n",
       "ProjectCount        -0.142970    0.349333      1.000000         0.417211   \n",
       "AvgMonthlyHours     -0.020048    0.339742      0.417211         1.000000   \n",
       "Tenure              -0.100866    0.131591      0.196786         0.127755   \n",
       "Work_accident        0.058697   -0.007104     -0.004741        -0.010143   \n",
       "Turnover            -0.388375    0.006567      0.023787         0.071287   \n",
       "Promotion            0.025605   -0.008684     -0.006064        -0.003544   \n",
       "\n",
       "                   Tenure  Work_accident  Turnover  Promotion  \n",
       "Satisfaction    -0.100866       0.058697 -0.388375   0.025605  \n",
       "Evaluation       0.131591      -0.007104  0.006567  -0.008684  \n",
       "ProjectCount     0.196786      -0.004741  0.023787  -0.006064  \n",
       "AvgMonthlyHours  0.127755      -0.010143  0.071287  -0.003544  \n",
       "Tenure           1.000000       0.002120  0.144822   0.067433  \n",
       "Work_accident    0.002120       1.000000 -0.154622   0.039245  \n",
       "Turnover         0.144822      -0.154622  1.000000  -0.061788  \n",
       "Promotion        0.067433       0.039245 -0.061788   1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84897dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Heatmap of Correlation Matrix')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap\n",
    "\n",
    "sns.heatmap(corr)\n",
    "plt.title('Heatmap of Correlation Matrix')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e06fdcb",
   "metadata": {},
   "source": [
    "# Positive correlations\n",
    "\n",
    "ProjectCount and Evaluation : 0.349333\n",
    "AvgMonthlyHours and Evaluation : 0.339742  \n",
    "ProjectCount and AvgMonthlyHours: 0.417211\t\n",
    "\n",
    "# Negative correlations\n",
    "\n",
    "Satisfaction and Turnover : -0.388375\n",
    "\n",
    "# Observations\n",
    "\n",
    "There is a positive correlation within projectCount, averageMonthlyHours, and evaluation. \n",
    "This could indicate staff who put in more time and worked on more projects received \n",
    "higher evaluations, i.e., the halo effect of being percieved as a 'good' and/or \n",
    "dedicated employee. \n",
    "\n",
    "Regarding negative(-) relationships, employee turnover and satisfaction are highly \n",
    "correlated. \n",
    "This could indicate an increased probability staff resign when less satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb85ba5f",
   "metadata": {},
   "source": [
    "# Statistical Test\n",
    "\n",
    "One Sample T-Test\n",
    "\n",
    "Hypothesis Testing: Check for a significant difference in the mean of satisfaction level between staff who resigned and the overall employee numbers.\n",
    "\n",
    "Null Hypothesis: No difference in satisfaction level between employees who left and the overall employee numbers.\n",
    "\n",
    "Alternate Hypothesis: There is a difference in satisfaction level between staff who departed and the overall employee numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "349b4dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean for the employee (total) numbers is: 0.6128335222348166\n",
      "The mean for the employees who is: 0.44009801176140917\n"
     ]
    }
   ],
   "source": [
    "# Compare mean of employee turnover statisfaction and mean of overall employee satisfaction\n",
    "emp_population_satisfaction = df['Satisfaction'].mean()\n",
    "emp_turnover_satisfaction = df[df['Turnover']==1]['Satisfaction'].mean()\n",
    "\n",
    "print( 'The mean for the employee (total) numbers is: ' + str(emp_population_satisfaction) )\n",
    "print( 'The mean for the employees who is: ' + str(emp_turnover_satisfaction) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f1c45db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-39.10948894348446, pvalue=9.012781195378076e-279)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# T-Test\n",
    "\n",
    "import scipy.stats as stats\n",
    "\n",
    "stats.ttest_1samp(a = df[df['Turnover']==1]['Satisfaction'], # Sample of employee satisfaction who resigned\n",
    "                  popmean = emp_population_satisfaction)  # The Mean for employee (total) satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2e6b2843",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The t-distribution left quartile range is: -1.9606285215955626\n",
      "The t-distribution right quartile range is: 1.9606285215955621\n"
     ]
    }
   ],
   "source": [
    "degree_freedom = len(df[df['Turnover']==1])\n",
    "\n",
    "LQ = stats.t.ppf(0.025,degree_freedom)  # For the Left Quartile\n",
    "\n",
    "RQ = stats.t.ppf(0.975,degree_freedom)  # For the Right Quartile\n",
    "\n",
    "print ('The t-distribution left quartile range is: ' + str(LQ))\n",
    "print ('The t-distribution right quartile range is: ' + str(RQ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcc634f",
   "metadata": {},
   "source": [
    "# Result(s)\n",
    "\n",
    "T-Test, the test statistic within the rejection region; conclude the sample data provides enough evidence \n",
    "to support an Alternative Hypothesis.\n",
    "\n",
    "Additionally, given the low p-value at less than 0.05; the Null Hypothesis can also be rejected."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f236a2",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dac0e209",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Satisfaction', 'Evaluation', 'ProjectCount', 'AvgMonthlyHours',\n",
       "       'Tenure', 'Work_accident', 'Turnover', 'Promotion', 'Department',\n",
       "       'Salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data-frame columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66c82f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of AvgMonthlyHour for Employees')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.AvgMonthlyHours,color='g').set_title('Distribution of AvgMonthlyHour for Employees')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fdf2d8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Overall Employee Satisfaction Distribution')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.Satisfaction,color ='r').set_title('Overall Employee Satisfaction Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "78cadf63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Employee Projects')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df.ProjectCount).set_title('Distribution of Employee Projects')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8a94fe0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ProjectCount', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.ProjectCount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "de0a9a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Satisfaction     14999 non-null  float64\n",
      " 1   Evaluation       14999 non-null  float64\n",
      " 2   ProjectCount     14999 non-null  int64  \n",
      " 3   AvgMonthlyHours  14999 non-null  int64  \n",
      " 4   Tenure           14999 non-null  int64  \n",
      " 5   Work_accident    14999 non-null  int64  \n",
      " 6   Turnover         14999 non-null  int64  \n",
      " 7   Promotion        14999 non-null  int64  \n",
      " 8   Department       14999 non-null  object \n",
      " 9   Salary           14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8dbf6a00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Company Employee Tenure')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Tenure).set_title('Company Employee Tenure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cb2df3ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Work_accident', ylabel='count'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Work_accident)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c1ac5344",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " [Text(0, 0, 'sales'),\n",
       "  Text(1, 0, 'accounting'),\n",
       "  Text(2, 0, 'hr'),\n",
       "  Text(3, 0, 'technical'),\n",
       "  Text(4, 0, 'support'),\n",
       "  Text(5, 0, 'management'),\n",
       "  Text(6, 0, 'IT'),\n",
       "  Text(7, 0, 'product_mng'),\n",
       "  Text(8, 0, 'marketing'),\n",
       "  Text(9, 0, 'RandD')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Department)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d213406a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Turnover', ylabel='count'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df.Turnover)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbd9ea1",
   "metadata": {},
   "source": [
    "Summary:\n",
    "\n",
    "1. Average Monthly Hours:  Bimodal distribution of employees with lower and higher average monthly hours (less than 150 hours & more than 250 hours)\n",
    "2. Employee Satisfaction: Contrast between low and high employee satisfaction. \n",
    "3. Evaluation: Bimodal distribution of employees for employees with low evaluations (below 0.6) and high evaluations (above 0.8)\n",
    "4. Highest department for number of employees is Sales.\n",
    "5. Number of employees with tenure of 3 years is fairly high.\n",
    "6. Number of on-the-job employee accidents is fairly low."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128d3675",
   "metadata": {},
   "source": [
    "# Employee Salary to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e8d5cb3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y=\"Salary\", hue='Turnover', data=df).set_title('Employee Salary to Turnover');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "721212b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y=\"Salary\", hue='Tenure', data=df).set_title('Employee Salary to Attrition');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ab6edf",
   "metadata": {},
   "source": [
    "Summary: \n",
    "    \n",
    "1. Low / medium salaries seem to be more likely to resign. \n",
    "2. Employees paid higher salaries seem to be the least likely resign."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6275fc55",
   "metadata": {},
   "source": [
    "# Departmental Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "508badae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y=\"Department\", hue='Turnover', data=df).set_title('Employee Turnover by Department');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee69465",
   "metadata": {},
   "source": [
    "Summary: \n",
    "\n",
    "1. Highest employee turnover Sales, Technical, Support (ranked order) Departments.\n",
    "2. Lowest employee turnover Management."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9c43b4",
   "metadata": {},
   "source": [
    "# Employee Project Count to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fba56a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y=\"ProjectCount\", hue='Turnover', data=df).set_title('Employee Project Count to Turnover');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003af26e",
   "metadata": {},
   "source": [
    "Summary: \n",
    "    \n",
    "1. Highest employee turnover (ranked order) 2, 6, 7 projects. \n",
    "2. Lowest employee turnover (ranked order) 3, 4, 5 projects. \n",
    "3. Every employee departed the company 7 projects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df266846",
   "metadata": {},
   "source": [
    "# Employee Evaluation to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9bbac807",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employee Evaluation to Turnover / No Turnover')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,4),)\n",
    "ax=sns.kdeplot(df.loc[(df['Turnover'] == 0),'Evaluation'] , color='b',shade=True,label='no turnover') # blue\n",
    "ax=sns.kdeplot(df.loc[(df['Turnover'] == 1),'Evaluation'] , color='r',shade=True, label='turnover') # red\n",
    "plt.title('Employee Evaluation to Turnover / No Turnover')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9977199",
   "metadata": {},
   "source": [
    "Summary: \n",
    "    \n",
    "1. Low / High performance employees (ranked in order) tend to resign the most. \n",
    "2. Employees within an evaluation range of 0.6 to 0.8 tend to stay to the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8d8a95",
   "metadata": {},
   "source": [
    "# Average Employee Monthly Hours to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "57aaa2d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employee Average Monthly Hours to Turnover / No Turnover')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,4))\n",
    "ax=sns.kdeplot(df.loc[(df['Turnover'] == 0),'AvgMonthlyHours'] , color='b',shade=True, label='no turnover') # blue\n",
    "ax=sns.kdeplot(df.loc[(df['Turnover'] == 1),'AvgMonthlyHours'] , color='r',shade=True, label='turnover') # red\n",
    "plt.title('Employee Average Monthly Hours to Turnover / No Turnover')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c55533b",
   "metadata": {},
   "source": [
    "Summary: \n",
    "1. Employees working less (150 hours or less) left the at the highest rate.\n",
    "2. Employees working more (250 hours or more) left the at the second highest rate.\n",
    "3. Under-utilization or over-utilization seem to be problematic issues needing to be addressed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d5db3c",
   "metadata": {},
   "source": [
    "# Employee Satisfaction to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4c5beebe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Employee Satisfaction to Turnover / No Turnover')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w+utBJxEREUkYKvBEROLBXXfBAQcEO/auNMOG+XGBhYVBJxEREUkIKvBERGLdsmUwapSfXCXadO/ul054992gk4iIiCQEFXgiIrHuvvtgwACoUyfoJP9lBscfDzfdBM4FnUZERCTuqcATEYlla9fCM8/A0KFBJ9my3r39cglffBF0EhERkbgXtgLPzJqZ2TdmNs3MppjZRaXs09/M1pjZxNB2Q7jyiIjEpRdfhK5doWHDoJNsmZlfuuHhh4NOIiIiEvfC2YJXAFzmnNsV6A2cZ2btS9nvB+dcl9B2cxjziIjEF+fgkUeiY927suy3H/z8M8yeHXQSERGRuBa2As85t8Q5NyH0+TpgGtAkXM8nIpJwxoyBoiLo3DnoJGVLT4dBg+Cxx4JOIiIiEtciMgbPzFoCXYHfSnl4DzObZGajzaxDJPKIiMSFhx/2rXdmQScpn4MPhhdegA0bgk4iIiISt8Je4JlZVeAd4GLn3NrNHp4AtHDOdQYeAd7fwjGGm9k4MxuXmZkZ1rwiIjFh/nz4/nsYODDoJOXXpAm0bw+vvRZ0EhERkbgV1gLPzFLwxd0rzrn/LILknFvrnMsOff4pkGJmdUvZ72nnXA/nXI969eqFM7KISGx44gnYd1/IyAg6ybY55BB48EEtmSAiIhIm4ZxF04DngGnOufu3sE/D0H6YWc9QnpXhyiQiEhc2boRnn42NyVU216MHrFsHP/0UdBIREZG4lBzGY+8JnAhMNrOJofuuAZoDOOeeBI4CzjGzAiAHONY5va0rIrJVb70FrVtD8+ZBJ9l2lSr5sXgPPgh77RV0GhERkbgTtgLPOfcjsNWR/865R4FHw5VBRCQuPfYYHHRQ0Cm236BBcNxxkJkJ6nYvIiJSoSIyi6aIiFSQf/6BmTNhjz2CTrL9qlaFPn3g5ZeDTiIiIhJ3VOCJiMSS55/3k6skh7OHfQQMGuTHEapXvoiISIVSgSciEisKC+Gll3xxFOs6d4a1a2H8+KCTiIiIxBUVeCIiseKLL6BOHWjVKugkO65SJb+G33PPBZ1EREQkrqjAExGJFc88E1sLm5dl//3h9dchJyfoJCIiInFDBZ6ISCxYuRK+/BIGDAg6ScVp2BB22QXefz/oJCIiInFDBZ6ISCx45RU/c2a1akEnqVj77+9bJkVERKRCqMATEYkFzz3ni6F4s9de8McfMG9e0ElERETiggo8EZFoN2kSLF8OXbsGnaTipabCPvvACy8EnURERCQuqMATEYl2I0f6yVWSkoJOEh4DB/rlH7QmnoiIyA5TgSciEs0KC+G11/zi5vGqXTv//xw7NugkIiIiMU8FnohINPvuO6hVC1q0CDpJ+Jj52UFHjQo6iYiISMxTgSciEs1efhn69w86Rfjtuy+88QYUFASdREREJKapwBMRiVa5ufDuu/G19t2WNG0K9evD118HnURERCSmqcATEYlWo0dDmzZQr17QSSJjn338ZCsiIiKy3VTgiYhEq1GjEqN7ZrF99oGPP4YNG4JOIiIiErNU4ImIRKM1a+Crr6Bfv6CTRE7t2tC+PXz4YdBJREREYpYKPBGRaPTee35h82rVgk4SWf37q5umiIjIDlCBJyISjV56KbG6Zxbr2xd++AFWrAg6iYiISExSgSciEm2WLoXx46FPn6CTRF7lytC7N7z9dtBJREREYpIKPBGRaPP227DHHpCWFnSSYPTrB6++GnQKERGRmKQCT0Qk2rz6amJNrrK5nj1h0iRYsiToJCIiIjFHBZ6ISDRZtAimToXu3YNOEpzUVN+CqW6aIiIi20wFnohINHnrLdhzT1/kJLJ+/eC114JOISIiEnNU4ImIRJPXXoO99w46RfB69IBp02DhwqCTiIiIxBQVeCIi0WLBApg+Hbp1CzpJ8FJSfEvmm28GnURERCSmqMATEYkWb74Je+3lixvxLZnqpikiIrJNwlbgmVkzM/vGzKaZ2RQzu6iUfczMHjazmWb2p5npbWsRSVyvv+4X+havWzeYNQvmzg06iYiISMwIZwteAXCZc25XoDdwnpm132yfA4C2oW048EQY84iIRK+5c30xo+6ZmyQn+4L3jTeCTiIiIhIzwlbgOeeWOOcmhD5fB0wDmmy226HAS877FahpZo3ClUlEJGq9+aYvZpKTg04SXfr18y2bIiIiUi4RGYNnZi2BrsBvmz3UBFhQ4vZC/lsEYmbDzWycmY3LzMwMW04RkcC8/rpmzyxN585+Js2ZM4NOIiIiEhPCXuCZWVXgHeBi59zazR8u5Uvcf+5w7mnnXA/nXI969eqFI6aISHDmzPFdNLt0CTpJ9ElK8hPPaNFzERGRcglrgWdmKfji7hXn3Lul7LIQaFbidlNgcTgziYhEnbff9t0zk5KCThKdNA5PRESk3MI5i6YBzwHTnHP3b2G3D4GTQrNp9gbWOOeWhCuTCODXGnvySbjmGnjhBfjxR1i2LOhUksjeesu3UknpOnf2P7ezZwedREREJOqFczT/nsCJwGQzmxi67xqgOYBz7kngU2AIMBPYAJwaxjySyLKy4N574f33YdEi6NULGjaEP/7wt+fNg65d4ZFHoGPHoNNKIlmwAGbM8OeflK5kN80rrww6jYiISFQLW4HnnPuR0sfYldzHAeeFK4MIAD//DMOGQadOcNZZ0L79f7vCFRbCRx9B//5w9NFw661Qt24gcSXBvPsu7LmnZs8sS9++8OqrKvBERETKEJFZNEUCUVjoC7VDD4Uzz4TLLvOtc6WNc0pKgsMO8102V6yAdu3ghx8iHlkS0BtvqHtmeXTp4iejmTcv6CQiIiJRTQWexKfcXBg0CN57Dx5/vPwX0NWrw/nnw9VX+4Lv++/DGlMS3OLFMHWqFjcvj+Jumm+9FXQSERGRqKYCT+JPUREcf7z//O67YXuW1ujRw0/Ccvjh8O23FRpP5H/eew/22ANSU4NOEhs0m6aIiEiZVOBJ/Ln0Ut+V68ord2za+e7d4brr4Mgj4euvKy6fSDF1z9w2Xbv6Bc/nzw86iYiISNRSgSfx5f774cMP4aabKqZVpGtXuP56OOYYP9OhSEVZtgwmToTddw86SexITtai5yIiImVQgSfx4733fJfM22+HatUq7rhdusAJJ/jZNTdurLjjSmJ7/33o3VvdM7fVXnvB668HnUJERCRqqcCT+LBkCQwfDiNG+PXtKtphh0GNGnDVVRV/bElMb7zhl0eQbdOtm29NX7Qo6CQiIiJRSQWexD7n/DIIQ4b45Q3CwcyP7XvjDfjkk/A8hySOlSth7Fjo1SvoJLEnJQX69PHrB4qIiMh/qMCT2Pfqq/DPP5tmzgyXGjXg//4PTj1VrQeyYz74wI+9S08POklsUjdNERGRLVKBJ7Ft6VK4+GK4/PLIjGXq1AkOOgjOOCP8zyXxS7Nn7pju3eGvv3zXbBEREfkXFXgSu4q7Zh5wAOyyS+Sed9gwmDYNPv44cs8p8WPNGvj5Zz/Bimyf1FT/+r33XtBJREREoo4KPIld77zjC60TTojs86akwLnnwoUXQm5uZJ9bYt9HH/nlNypXDjpJbOvbV900RURESlGuAs/M3jGzA81MBaFEh7w8uOIKX2gFMc18z57QrBncd1/kn1ti25tvavbMirD77n4dweXLg04iIiISVcpbsD0BHAfMMLM7zSxMUxWKlNPjj0OjRn7K9KCcfbYv8BYsCC6DxJbsbPjmGz8LpOyYtDQ/C+n77wedREREJKqUq8Bzzn3lnDse6AbMBb40s5/N7FQzSwlnQJH/yMqC227z4++C1KgRHHooXHJJsDkkdnz6KXTsCNWqBZ0kPmg2TRERkf9ILu+OZlYHOAE4EfgDeAXYCzgZ6B+OcCKluv12P8FCq1ZBJ4GhQ/2Mmt99B/36BZ1Gop0WN69YvXrBvff6dQXr1Ak6TVxxDtav93MCrV3rt+xsP+w4J8d/zM+HoiIoLPT7V6oESUl+S031jazp6X6rWhWqVPEfq1WD6tUhudxXICIisi3K9evVzN4F2gGjgIOdc8VzU79hZuPCFU7kP+bPh2eegaefDjqJl54OJ5/s18f76Se/ILpIaTZsgC+/hJdeCjpJ/EhP9+Nh338fTj896DQxwTlYscL/Kl2wwH9ctMhvixf7IY0rV8KqVb5gq1bNF2WVK0NGhi/aUlP9XFPJyX4fM785t6ngKyz0BWBenv+Yk+O3DRt84Zid7Y9XvTrUquW32rV9nV6/PtSt67c6dTZ9rFPH75eUFPSr6BUXwWvXwrp1m/5f69f7AnjjRr/l5fnXpXirVGnT65ea6l/b4gK4Ro1N/88ghpeLSHwo7/tnzzrnPi15h5mlOec2Oud6hCGXSOmuvRYOOQTq1Qs6ySYDBsBrr/mL9/33DzqNRKvPP4d27aBmzaCTxJc99/Qtoyrw/qeoyBds06fDjBnw998wcybMng1z5/rCoVGjTYVUrVrQpIlf5rN2bV9kVK/ui7lwZszJ8QXRunV+K24pXLUK5s3bdP+aNX7LyvL7V63qM9es6bcaNfzH6tU3bZUrb2pBTEvzRWFy8qbisLjYKiz8dyGWk/PvQm3tWv/cm2fMzvZbWprPk5GxqQhOT/93EZySsqkQhk2FcEGB3zZu3NQyWtxqumaNP069etCwITRu7Of1atUKWrSAli2hdWv19haR0plzruydzCY457qVdV8k9OjRw40bp0bDhDR1Kuy9N7z4on+rM5qMGePHV40dq1Y8Kd2xx/ortcMOCzpJfMnJgWOO8RVB7dpBp4movDxfwE2dummbNs0Xc1Wr+oKgSRNfzDVu7D9v2NA/FqsKCzcVXsVFVvFW3EqYm+tfm+LWw+IWtMJC/7G4xbG46EpJ+XeLWlrapuKwcuV/b1Wq/PtjuFoTi1sHV63yLaorV0Jm5qZt6VJYuNAXs61b+6VgO3SAXXf17yO1ahU9LZ0iEh5mNn5LDW1bbcEzs4ZAEyDDzLoCxVeu1QEt4iSRdeutcMQR0VfcAfTv71vxPv0UDjww6DQSbXJzYfRoeOGFoJPEn4wM6NEDPvgATj016DRhkZcH//wDU6bAX3/B5Mm+mJs/3xdtzZtD06aw006wzz6+sIvGX5MVISlpUytdPDPzhXjVqv77W5qiIl/4LVrkz4WxY+G993wrbVYWtG3r53Tq3Nm3znbs6It9vQcpEv/K6qI5CDgFaArcX+L+dcA1Ycok8l+zZ8Nnn0Xv+KVKleDEE30X0iFD9BdU/u3LL6FNm4RrYYqY4tk0Y7zAc85frP/5p1/ib+JEX8zNmeNb4Fq29MVb586+IbhpU9/yJImpUiXfhbNePejS5d+PbdjgC705c3zh99ZbvmUXYLfdoGtXv3XpAu3ba7yfSLwpbxfNI51z70QgT5nURTNBnXmmfxs7mi/gnPMLr99+Oxx+eNBpJJqccIKfOUHnRXhs2OBntJ0/3w/OihErV/q5mX7/3W8TJvhfI23bbhpj1bq1b8HRBbjsKOd8l89Zs/w2d67/uHChf/+pSxfo3t1/7NxZ70eJRLutddHcaoFnZic45142s8uA/+zonLu/lC8LKxV4CWjRIv+W48iRfjR9NPvlF3j5Zf+2e6VyLTMp8W7jRmjQAJ591s9oIeExYgSccorfolRWlm/M/eIL+OEHf2G9226w885+22UXf4qoA4BE0saNvtgrnohnzhw/QU+NGr7QKy76unTxY/v0p00kOmz3GDyguBd/DA/Jlph3990waFD0F3fg1+cbNQo+/tjP9iny9df+qkjFXXgVd9OMsgJv/nw/PPfDD33Xy86d/YXyJZf4VhNNhCFBS0vzby7sssum+4qKYMmSTUXft9/61r516/ybEt26beri2aGDHworItGjXF00o4la8BJMZqbvrxRLrR/ffOPfov/ll6CTSDQ45RQ/l/mRRwadJL4Vd9NcsCDwpSjWr4e33/Zz6kyaBH37Qp8+/mI4PT3QaCI7ZM0aX+jNnOlb/YqX3mjRYlMXz86d/dawYcBhReLcjrTgFR/gbuBWIAf4DOgMXOyce7nCUoqU5v77/bRwsVLcgV/K4fnn/eCaPfcMOo0EKT/fz+741FNBJ4l/lSv7ZoUPPoCTTw4kwooV8PDD8Nhjfrr6gQPhmms0fk7iR40a/sesW4lFsvLyfEv1zJkwfvymCV2Sk/3MncUtfZ06+SUcwrm+ooh45V3ofH/n3JVmdjiwEDga+AZQgSfhk53tL4wffTToJNsmKQmOOgruvBM++ijoNBKkMWP8tIf16wedJDEUd9OMcIG3ciXcfLNfonPvveHBB/23XSQRpKb67sZt2my6zzn/hkdxF89Ro/zHhQv9BEIdO25auqF9ez+ZUHJ5r0hFpEzl/XEqnoh5CPCac26VlTEK3MyeBw4Cljvndivl8f7AB8Cc0F3vOuduLmceSQQjR/p+Ho0bB51k2w0eDMcfD3//7d+ylMT05pu+6JDI6NPHN6FlZUWkm2ZhITzzDFx/vS/snnnGT1kvkujMNi3hsMcem+4vbu2bMwemTYPPP4d58/xojNat/Z/L4q1tW7+2Y716mnhIZFuVt8D7yMz+xnfRPNfM6gG5ZXzNSOBRYGsLl/3gnDuonBkkkRQVwQMPwMUXB51k+6SlwaGHwl13aXHrRJWfD++/7/vrSWRUqRKxbprjx8MZZ/jP77jj360XIlK60lr7AHJzfeG3cKHfxo/3E2gvWuSLwubN/dasmf/YpImfnLhePd9BonZtP9R5R2f4LCz0Y2jXrfOdiNau3bStW/fvbcMGyMnxH/PzfatlYaE/TkaG7zVeubLP1rixz9ykiS9c1U1Vwq1cBZ5z7mozuwtY65wrNLP1wKFlfM33ZtayAjJKIvr0U/8bcLf/NP7GjkMO8ReZt90Wm62QsmO+/RYaNdJMA5HWty+8+mrYCjznfCPhzTf75TkHDVLrgsiOSk/ftFzI5tav9zN6Zmb6bcYM+O03P+FLVhasXu0/z8nxBVXVqv546em+oExJ8T+jxVtBgS/ICgp88bhhgy8wc3L87eLirEoVvxUXahkZ/pjFH9PS/Fa9uu9earapwMzL88fcsAGWL/dzrq1c6T9fssS3Vnbp4jsdDBniJ1oWqUjlnkXTzPoALSlRFDrnttY6R6jA+3grXTTfwY/pWwxc7pybsoXjDAeGAzRv3rz7vHnzypVZYlj//n6CkoEDg06yYx57zE8vdu+9QSeRSDv9dP/X/5hjgk6SWHJy/Gs+d65fXL4CZWX5unHGDLjuOr1vIxJNior8j//69b7Ays/3HwsK/BszxZe7SUl+S072xV9a2qZiMC0t/Ov85eVtWndwyhRfrNarBwcfDKee6idoEimP7V7ovMQBRgE7AROBUAM0zjl3YRlf15ItF3jVgSLnXLaZDQEecs61LSuLlklIAJMmwf77+wXDU1LK3j+aLV0K55zjp26vquUkE0Z+vu8/9MQT/qNE1i23wLBhm/pQVoCpU/077T16wPDhmhlTRCpGUZEfrv/zz35MYufOcOmlfii/FpWXrdlagVfeU6cHsKdz7lzn3AWhbavFXVmcc2udc9mhzz8FUswshubCl7B54AHfvTHWizvw3fO6dPETxkji+PprP1hExV0w9t4bXnmlwg7366++U8Fxx8H556u4E5GKU6mSn0n0jDP8r60ePeCyy/wC8l9/HXQ6iVXlLfD+Aip0IImZNbTQVJxm1jOUZWVFPofEoGXL4L334KA4mnvnsMP8vOlFRUEnkUh57TU/FkyC0auXn6Vh2bIdPtTnn8OBB8Ill/iOBSIi4ZKa6lvuHn/cT8R90klw5JG+E5DItihvgVcXmGpmn5vZh8Xb1r7AzF4DfgF2MbOFZna6mZ1tZmeHdjkK+MvMJgEPA8e68g4IlPj1xBPQr59fTTVedOrkO/t//nnQSSQS8vL8LI79+gWdJHGlp/u52d95Z4cO8+ab/iJrxIh/T/UuIhJOZn6Fneee85O4dOoETz65aRyhSFnKOwav1CsV59x3FZ6oDBqDF8fy8/38x7fd5qeYiieffeZbFL78MugkEm4ff+wXRnvggaCTJLaffoLRo+HHH7fryz/91E+ocuedfi0uEZGgzJ/vZ+7dc09f6GVkBJ1IosEOj8ELFXJzgZTQ52OBCRWWUAR8q0ejRvFX3AEMGAATJ/qR1BLfXnvNjwGTYO2+O/z1l19Iaxv9/DOceCLcdJOKOxEJXvPm8MgjsHix74E+e3bQiSTalavAM7MzgbeBp0J3NQHeD1MmSVSPPBJfY+9KSk31A3kefDDoJBJOubnwyScq8KJBaqp/u/vNN7fpy/76Cw49FK66yk98ICISDTIy4Jpr/IRPvXvDBDWzyFaUdwzeecCewFoA59wMoH64QkkCmjrVb/E8McUhh8Drr/tVWSU+ffYZtGlT4euvyXbq18+3qJbTggV+4fJzzoGePcOYS0RkO5jBEUfAeef531UasSRbUt4Cb6NzLq/4hpklAxrqKRXnscf8IlPxsDTCltSu7d92e+aZoJNIuGj2zOjSrZtfTXjOnDJ3zc31LXcHH+x7VIuIRKt+/eCii/yMm7/9FnQaiUblLfC+M7NrgAwzGwi8BXwUvliSUNat84u/xGv3zJIOPdQXs4WFQSeRirZ+vW/BU/fM6JGc7K+EylgTzznfalerFgwdGqFsIiI7YK+9/Hp5Bx6oIk/+q7wF3tVAJjAZOAv4FLguXKEkwbzyil8MvF69oJOE3667QrVqfnY/iS8ffeQHbdWqFXQSKWnAABg1aqvziz/1FPzwA1x+ue8CJSISC/bYwxd5hx4K8+YFnUaiSXln0SzCT6pyrnPuKOfcM1qzTiqEc35ylYMPDjpJ5Bx8MDz8cNAppKKNGuVHv0t02W0337o6cWKpD//yC1x7rV/rTlOPi0is2WMPOPpoP8pl3bqg00i02GqBZ94IM1sB/A38Y2aZZnZDZOJJ3PvpJ3/x1a1b0EkiZ599/Jp4M2YEnUQqysqV8P33Gn8Xjcz8z9yoUf95aPVqf2F02WXQtGkA2UREKsARR/glXYYO1QgQ8cpqwbsYP3vm7s65Os652kAvYE8zuyTc4SQBPPaYH3uXSP2iUlPhgAPg0UeDTiIV5Z13/OJElSsHnURKs+++fgKcElc+zsHw4f7d7z59AswmIrKDzODCC2H5crjiiqDTSDQoq8A7CRjmnPvfFGTOudnACaHHRLbfihXw6aew//5BJ4m8gw6Cl17yrZcS+156Sd0zo1nLllCjBnz33f/uGjUK/vgDzjgjuFgiIhUlORluuMEv/fnBB0GnkaCVVeClOOdWbH6ncy4TiOP57CUiXnjBTwNVvXrQSSKvYUPo1KnM2f0kBixYAFOmaOG0aFeim+acOXDJJXD11ZCWFnAuEZEKUr26/7125pmweHHQaSRIZRV4edv5mMjWFRXBE0/4UcGJ6pBD4KGHtjq7n8SA4rXvUlODTiJbM2AAvPceBdm5HHecH6vSpk3QoUREKtZuu/lOQiee6C+1JDEll/F4ZzNbW8r9BqSHIY8kijFj/KLm7dsHnSQ43br5cXg//KC102LZyy/DqacGnULKUq8etG3LR2d/Qn7+kRx1VNCBRGKHFRaQvnQuGQtnkLFoJilZy0nJWkHKmhUkb1j7rzcqi9LSya9el/wadcmvWY/chi3Z0HRncpq2pShd45Qj4fjj4dJL4YEH/CRSkni2WuA555IiFUQSzOOP+9a7RJpcZXNmm5ZMUIEXm6ZO9aPaO3UKOomUw6rO/Ul/6UUue/FIKpV3FViRRFNURJV5U6k+9Veq//Uz1af+QsaS2eRXr0tu/Wbk1W5EYdUaFFSuzobmu1CUXgVX4m95pfyNJK9fS3L2atKXzaP2b5+StnwB6ZkLyatRl3W79mJNp76s6dCH7DZdcCnq/VDRkpJ8V83zzvNzTHXpEnQiibSyWvBEKt6SJfD11/Dqq0EnCd7++/u32pYsgUaNgk4j2+qVV/zkKkl6LyzaFRXBHT/34xaeYnzlFeRTN+hIIlEjed1qao39gjo/f0idsZ9TmF6F9S3bs6HZLiw86mJyG7ba8UKsqJDUlUuoMncqNSZ+R+MPniB11VJWd9+PFX0PZ2WvIRRUr10x/yGhUSM/U/DJJ/uVmZJ1xZ9Q9O2WyHvuOX9RXKVK0EmCV7WqHxv01FN+pWWJHUVFfvbMG28MOomUw0cfwcrcqqzvtAf1v3qFRUdeFHQkkUBVyt1A3Z8+oOFnI6n+189kt+nMuna7M/2iR8mv3TAMT5hEXr2m5NVryurd/ezZyetWUX3qrzT6+Bna3n8Oazv0ZslBw1nR5xBcqmZA2lGDBvn30x9+2HfZlMRhLsYmeOjRo4cbN25c0DFkexUW+inLr78edt456DTRYc4cuOYamD/fj0uU2PDNN3D22b44l6i2fLlfDuHcc6HNmnE0/GIU456bFHQskUBUnfEHTd55mHo/vsv6Fu3J6jaANbvtRVFaRqC5LC+XmpN/oNbvn5OxdC7LBp7AwiMuJLdx60BzxboFC+Cii2DiRGjePOg0UpHMbLxzrkdpj2kUgkTWZ5/5eXxV3G3SqhU0bgzvvx90EtkWzz0HAwcGnULK4Bzce69fkaVhQ8hu25WUrOVUmakCTxJIYSF1v3+XrhfsRcerh+CSU/j7iueZc+YdrO4+MPDiDsClprO6+0Bmn3MvM85/iJSsTLqf1YNdbxqqn9cd0KwZHH44nHOOJu1OJCrwJLIefxwOPDDoFNHnoIP8kgkSG7KzfZ+/AQOCTiJl+P57WLiwxLeqUhKruw+k0ejnA80lEhGFhTT4YhS9TmxLy5EjWN1tX6ZdM4rl+x1HQY06Qafborx6TVhy0JlMu3YUBdVr0/mK/el45WAVettp6FCYNk3vIycSFXgSOfPnw88/+wWH5d/69oXp02Hy5KCTSHm8/TZ07gy1NSFANNuwAR55BI444t8TDKzafRANvnoFy9dyrhKnioqo//Vr9Dy5HU3fup9Fh1/AzAsfJqvrPpAUO9MvFKVXIXOfoUy7dhQbmu1C58v3o93tJ5GWuTDoaDElNdV30zzvPFi3Lug0Egkq8CRynnnGv42eEXxXkKiTnOxbNh99NOgkUh7PP+/nnpao9vzzsNNO/13QPK9eE3LrN6P2b6ODCSYSRjUm/0j34d1o/srtLD74bGadez/ZbbsGHWuHuORUVvY9nL+vHglAj9M60vK566iUlxtssBjSubNf0efOO4NOIpGgAk8iIz/fF3gHHRR0kuh14IHw+uuwZk3QSWRr5s6Fv/6CPfYIOolsxaxZ8MUXfqnJ0qzuvh+NPn02sqFEwiht+QLa33QM7Ucczco+hzDzgofJ3qV7XK03W5RehaVDTmP6ZU9TY8rP7H5qB2r+8U3QsWLGaaf5kTLz5wedRMJNBZ5Exkcf+UVZWrUKOkn0qlsXevaEF14IOolszYsv+m7GqVqcN1oVFfmJVQ44wK9EUpqsLvtQc9J3pKxeHtlwIhWtsJCmr99Lj9M7U5hWhX+ufJ6sbgPiqrDbXH7Nesw76QaWHHAau956HO3uOJnktauCjhX16teHQw+Fq64KOomEmwo8iYzHH4chQ4JOEf0OOcQvWFNUFHQSKY1zvgDX7JlRbfRoyM2FXr22vE9RemXW7LYnDb4cFblgIhWsypy/6H5OT+p/8zozLnqEZYNPjooZMSNl7W578s8Vz5GUm83up3Wk5oQxQUeKekOHwpgx8PvvQSeRcFKBJ+E3axZMmAD9+gWdJPrtthukpfnlJCT6/PijHy+5yy5BJ5EtWLsWnn7aT6xSqYy/cKt6HkDjD5/U3OESc6ywgBYv3EiXi/ZmdbcBzD77HvLqNgk6ViCK0iuz6PALWHjURbS/ZRitn7hcEyhtRUYGnHwyXHihfvXFMxV4En5PPgn7768ubeVh5vtPPPhg0EmkNE895Vvv4rjrU6x79lk/kUCzZmXvu751R6yoiBqTvg9/MJEKkr54Nl3P70PtcV8w/dKnWNX7QP1OAta168k/lz1F9Wm/0e2cnqQvmhV0pKg1aBBkZcFbbwWdRMIlbAWemT1vZsvN7K8tPG5m9rCZzTSzP82sW7iySIByc32XNk2uUn4DBsD48X7ZBIkeq1fDhx/C4MFBJ5EtmD4dvvvOj70rFzNW9j6QJh88FtZcIhWlwZcv0/3s3VnbridzTr+N/Jr1go4UVQqr1mTuqTeT1akf3c7rTe1fPw06UlRKSoIzz/Rj8fLzg04j4RDOFryRwNauhA4A2oa24cATYcwiQXn7bWjbFpo2DTpJ7EhN9eMVH3kk6CRS0ksvQe/eUKNG0EmkFEVFcP/9vrirXLn8X7e6x0Bq//4ZKVmZ4QsnsoMq5W6g3W0n0PKFG5l11l2s6HdU2X2QE5UZK/c+nHkn3UC7u06lxYs3aVx7Kbp3h3r1/HIyEn/C9tvBOfc9sLUpjQ4FXnLer0BNM2sUrjwSkEce8dP/y7Y5+GAYNUorkkYL53xXY00UFLU+/xw2bvQT0W6LwsrVWNOxLw1Ha/ZaiU4Zi2bS/ZzdSVmzkukXP0ZukzZlf5GwvnVHpl/8OPV+eI/drj2EpJzsoCNFnVNOgZtugpycoJNIRQvy7Z8mwIIStxeG7pN48ccffrEVrRe27erXh27dYOTIoJMIwC+/+O7GnTsHnURKsW6dHx55+OHb16ixsveBNP7wCb3LL1Gnzk8f0u3c3qzqMYgFw67EpaYHHSmmFNSow6yz7wGrRNfz+5CauSjoSFGlfXvYeWc/0bnElyALvNJGBJc6n4+ZDTezcWY2LjNT3WhixqOP+rF3SUlBJ4lNhx4KDz2ki85o8MQTvu+fJjKISs8+6yegbd58+75+Q4tdcSlp1PpDU6xLlHCOls/fwM73n8Wc025m5Z6H6PfPdnLJKSw8+hLWtO9D93N7UWXmpKAjRZWTT4Y77/QzEEv8CLLAWwiUnOesKbC4tB2dc08753o453rUq6cBxTEhK8uPvyv3bAfyH506+fF4n2qQeKBWr4YPPvDTjknUmTEDvv12B+e+MWNlrwNo/L4mW5HgVdqYQ/sRx1D3x/eZcdFjbGjRPuhIsc+MzH2PZfGBZ9Dlsn2p/dvooBNFjVat/Hi8Bx4IOolUpCALvA+Bk0KzafYG1jjnlgSYRyrSyJF+leHatYNOErvMfJ+ze+4JOkliGzVKk6tEqeKJVQYPhqpVd+xYq7vvR60JY0hdqT9DEpzUlUvoeuFeJG9Yw6xz7qWguv6GVqQ1Xfoz55QRtLvjJOp/9UrQcaLGiSf6DkMrVwadRCpKOJdJeA34BdjFzBaa2elmdraZnR3a5VNgNjATeAY4N1xZJMKc29Q9U3ZM//7wzz8wcWLQSRKTJleJal984YdG9uq148cqSq/C6q4DaPyBJnSWYFSZ9Sfdz96ddW26Mv+4/8OlaO3YcNjQajdmn3U3Oz1+OY3fezToOFGhSRPo2xfuvTfoJFJRwjmL5jDnXCPnXIpzrqlz7jnn3JPOuSdDjzvn3HnOuZ2ccx2dc+PClUUibMwY3/rUsWPQSWJfSoofi6ffusH48Uc/vZgmV4k669b52nt7J1YpzYq+h9P4wyeolJdbMQcUKaeaE8bQ5dIBLDngNJYPPEHj7cIst1ErZp13P81fu5sWI0f4N/MS3LBh/neqWvHigxZRkYr34IN+mn/9gaoYBx0EH38Mi0sdoirhdN99vsDWuRx1nn0WOnTY/olVSrOxQXNymu5M/a9erbiDipSh/tev0eGmo5l34nVkdRsQdJyEkVenETPPf5AGX7/GTk9clvBFXsOGsPfeej85XqjAk4o1e7Zv9Rg4MOgk8aNaNdh3Xy18Hmlz58L332tylSj099/w3Xfh6Tmb2fdwmr15X8Jf7ElkNH39XnZ6/FJmnXUP2W26BB0n4RRUr82ss++h9m+f0ebRixL+516tePFDBZ5UrEce8TNnZmQEnSS+HH44PP00bNgQdJLE8cgjvrjTuRxVCgv9vEMHHghVqlT88bN37o7lb6TWhK8r/uAixZyj1VNX0eTDx5l5/kPkNm4ddKKEVVilOrPPvpuaE8bQ9sHzEnppIrXixQ8VeFJxsrP97JmHHBJ0kvjTtKlf6OuFF4JOkhiys/1rfeihQSeRzbz3HiQnQ48eYXoCM1b0PZymb94XpieQhFdYyM73nUXdXz5m5rkPkF+rftCJEl5hRlVmD7+TGn/9yM73n53QRZ5a8eKDCjypOCNHQpcu/i0gqXhHHQV33w0FBUEniX8vvugnVtG5HFUyM/235ogjwjsscnW3/aj+91gy5v8TvieRhGT5ebS/ZRjVZkxg1tl3U1hVy69Ei6KMqsw+806q//07bR88N2G7axa34mmFptimAk8qRlGRX0TlsMOCThK/OnaEOnXgzTeDThLfior8REE6l6POww/DnntCgwbhfR6XmsaK3gfS9G2t/CsVp1JeLrtddyipq5Yy+4zbKUoPQx9j2SFF6ZWZfcZt1Jj8I20evThhi7xhw+Cpp2DVqqCTyPZSgScV44svICkJOnUKOkl8GzoUbrstYf/oRITO5aj0008wfToMiNAkgyv3PJQGY14nJSszMk8oca1Szno6XnUAVljA3JNv0Bp3UawovQpzzryD2mM/p9XTVyfk39uGDWGvvfx7nRKbVOBJxbj/ft/ioenkw6tnTz/LxCefBJ0kft13n87lKLNunf+2HH00pEbourigem1Wd9mHpm/dH5knlLiVtH4tna8YSFFaZeYfdzUkJQcdScpQWLkas4bfRb3v36XlCzcGHScQxx4Ljz4Ka9YEnUS2hwo82XF//w0TJkTurfVEZgbHHAO33pqQ7yqG3R9/wOTJOpejzGOP+TmG2rSJ7PNmDhhK4w+fJHnd6sg+scSN5HWr6XzpAPJqNWDBMZdBpaSgI0k5FVatweyz76LBFy/R9I3Em3SpSRPo1ct3jZfYowJPdtw99/iZMyP11nqi69cPliyBH34IOkn8ueUWP5mNzuWo8fvvMG5ceNa8K0te7Yas2W1PmryjKxzZdslrVtLlkv7kNm7NoiMuhEq65Io1BdVqM/usu2j21n00/OTZoONE3LBhvpvmunVBJ5Ftpd82smOWLIF33tF08pGUlOT7qt16a9BJ4su0aX5h8wMPDDqJhKxf798/OvpoSE8PJkPmPkNp+u7DJK1fG0wAiUkpa1bQ5ZJ9WN+iPYsPOUddvmNYfq0GzB5+F62fvYZ6374VdJyIat4cunf3XTUltqjAkx3z4IOw335QQ1M9R9T++8OkSTB+fNBJ4sftt/sF5bWwedR44gnfLXOXXYLLsLF+M9bt3J3G7z8eXAiJKSlZmXS5uB/ZO3ViyUHDVdzFgY31mzHnjNvZ+YFzqP37Z0HHiahhw/w0C+vXB51EtoUKPNl+a9fCM8/4Lm0SWampfgT0ddcFnSQ+zJ3rJ65RS3TU+PFH+O033/s7aMv3HUazt++nUo6ucGTrUlYvp8vF/Vi3c3eWDjldxV0cyWnShjmnjGDX206g+pRfgo4TMa1a+VWaHtd7XDFFBZ5sv6eegh49tBh0UA48ECZOhF9/DTpJ7LvzTv96Vq0adBIBVqyAe+/17xxHQ4NqbqNWrG/ZgcYfPRV0FIlixcXd2nY9WTr4VBV3cWhDq92Yf+wV7HbtIVSeMyXoOBFz/PG+u/yGDUEnkfJSgSfbJy8PHnjAD46RYKSmwnHHwbXXBp0kti1ZAq+/DkccEXQSwa8zf/vt0Ls3tG4ddJpNlu93PM1fu4ukDZptQP4rZdUyulzUj7Xte7Ns8Ckq7uLYul17sfjgs+h8xf6kLZ0XdJyI2GknaN8ennwy6CRSXirwZPu8+qoffdu2bdBJEtvgwfDPP5pRc0fcfTcMHAi1agWdRIC33vLrLg0cGHSSf8tp0obstl1o9sa9QUeRKJOyahldLu7P2g69WTbo5KDjSARkdd+PzL2PpPPl+5GSlRl0nIg44QT/5zInJ+gkUh4q8GTbFRX5Lm1qvQteSor/rXvNNVoXb3vMnw8jR/rxjBK4f/6Bl1/2DdNJUbhc2NLBp9Lk3YdJXbU06CgSJTYVd3uouEswK/Y+grUd9qDTlYNJyskOOk7YtWkDO+8MTz8ddBIpDxV4su3efReSk6Fbt6CTCPimjgULYMyYoJPEnhtugIMPhjp1gk6S8Fav9nMGHXVU9H478mo3ZHWP/WkxckTQUSQKpK5aSteL9w4VdycFHUcCsHTwqWys24QO1x2G5ecFHSfsjj/ev7+fmxt0EimLCjzZNkVF/qL4hBM0xiBaJCX578f//Z9a8bbF1Knw0UdwzDFBJ0l4BQX+10rXrtC5c9Bptm7ZfsdR/9s3yVgwPegoEqDUVUvpctHerNltLxV3icyMhUdeRKXCfNrdfqK/Ropju+zix+M9m3hrvsccFXiybd59FypVgl69gk4iJe2zjx+49M47QSeJHVddBUOHaubMKPD44/69iUGDgk5StsIqNcjsdzStn74q6CgSkOLiLqtjX5btf2LQcSRoSUnMO+4aKi+cTpvHLo77N1pPOAHuuEOteNFOBZ6Un1rvoldSEpx1Flx2mX7rlsfPP8O4cXDYYUEnSXiffw4//eS7/kTjuLvSZPY9gupTf02otbDES12xmC4X7k1Wp71ZruJOQlxqGnNPu4Xav42m+at3Bh0nrNq18zMcP6VVY6KaCjwpP7XeRbdu3aBFC3jooaCTRDfn4Ior4KST/FITEpjx4+Gxx+CUU6JjvbvycqlpLB18Km0fPBcKC4OOIxGSlrmQrhf2JavrPiwfeELQcSTKFGZUZfaZd9D4/cdo+OlzQccJq5NO8q14WhcveqnAk/JR611sOPNMuOsuWL486CTR66OPYOlS2H//oJMktH/+gZtvhpNPhkaNgk6z7Vb3GAhWiSYfPB50FImAtKXz6HJBX1b1HMzyfYcFHUeiVEGNusw58w5aP/1/1Pnpw6DjhE3btrDrrr57vUQnFXhSPmq9iw3NmvlZNa+7Lugk0WnDBrjgAjjnnNjpDxiHFiyAq6/2M2butFPQabaTGQuPvJCWI0eQumJx0GkkjNKXzKHrRXuzss/BZPbX8kCydRvrN2POaTfT7u7TqPFn/K5Re9JJfl287PhfISImqcCTshUUwLXXwoknqvUuFpxwgi/IJ08OOkn0ue02/9Zjjx5BJ0lYmZlw+eUweDB07Bh0mh2zsUELVvY+kDaPXhx0FAmTjPn/0PXCvmT2PYIVex8RdByJETnN2zHvuKvpcMMRVJn1Z9BxwqJVK+jSBR5+OOgkUhoVeFK2Z5+FatWgZ8+gk0h5VKvmZ6w4//y4n81rm0yfDk884SejkUAsXOhPyz594qczwLL9jqP6lJ+pNfaLoKNIBasy60+6XtyPpQNPYOVehwYdR2JM9i49WHzouXS6chDpi2YFHScsTjgB7r/fT+It0UUFnmzdunUwYgQMH67WuyjjHGRlwbx58Oef8OOPfq3zr7+Gr6scwppZK5h0yQv89BP89ZdvOUnYes853y3z2GOhXr2g0ySkmTPhwguhf3/o1y/oNBXHpaaz6PDz2fn+s6m0MSfoOFJBqv09ls6X7cfig4ezuufgoONIjMrqug/L9j2OzpftR+rKJUHHqXDNm/v3/u+5J+gksjlzYbziM7PBwENAEvCsc+7OzR7vD3wAzAnd9a5z7uatHbNHjx5u3LhxFR9WSnf99TB2rB8wI4HJy4Np03yvy1mzYP58WLzYDyOrVg2qVIHKlSElxe9vBg3Xz+SMmVdzeNspLMhrwMqVfgWFJk38L+UOHaBTJ/+xY0d/nLj11ltwzTW+BS85Oeg0CWfKFP/yH36479ITj1qMupX1Ldsz8wL1V4p1NSd+S4cbj2LBUReztuNeQceROFD/q1eoMeVn/nj4Rwqq1Qo6ToVautS/fzptGjRsGHSaxGJm451zpY45CVuBZ2ZJwHRgILAQGAsMc85NLbFPf+By59xB5T2uCrwIWrzYX/0/8YR+agMwbx58/71fsm32bGjc2K+C0Lgx1K/vt8qVt36Mhp88S6X8PKaOeBOAnBxYtsz/Qp43zxeK8+b547dsCb17++5z/fpBmzZx0mi7Zg20b+8XNu/UKeg0CWf0aP8rZNgwP+tavEpav5ad7zuLv//vRVbvrhlaY1WdHz+g3d2nMe/Ea8lu2y3oOBIvnKPxh0+SvnQOk+4fQ2FG1aATVainnoLq1bU2XqQFVeDtAYxwzg0K3f4/AOfcHSX26Y8KvOh12mm+yWf48KCTJIzly+Hjj+Gbb/zMVB07+hq7ZUtIS9v241neRna5bzgzLn6MlXts+ccsP98XeVOn+unr//jDN3QNGACDBvkJMerW3f7/V6BOPNF3Nb744qCTJJS8PHjkEd8B4JRTEuM9oqrTx9PsjXsZ9/xk8mvE6g9M4mrw2Uh2evJK5px2CznNdwk6jsQb52j65v0k5axj8t2fUZSaHnSiCrN2rf89/8svsIt+dCImqALvKGCwc+6M0O0TgV7OufNL7NMfeAffwrcYX+xNKeVYw4HhAM2bN+8+b968sGSWEiZPhn32gZEjoWp8vdMUbYqKYNw4eO89/7J36+a35s39yhQ7quqMCTR7835+f3EahZXL1w/TOT8hxh9/wIQJfkHqXXeFQw7x3ezat4+R1r2334bLLoMnn4ytlbRj3NKlcOON/iUfOjSxXvpGHz1FUu56/rr1gxj5IRGApm/cR7O37mP2mXeysUHzoONIvCoqpPkrd+BS0vjr1vdxySlBJ6owr78OS5bABx8EnSRxBFXgHQ0M2qzA6+mcu6DEPtWBIudctpkNAR5yzrXd2nHVghcBzkHfvn4q+cMOCzpN3Coqgh9+gOef95/36eMLu+1pqStL0zfvI79GPf656vnt+vq8PJg0CX77DX76ydf8Rx/tty5dovQ6dvFi6NwZbrrJV6QSdoWF/o2KF1/0rb/9+0fpuRFGVpBH24cuYMHQy1ly0JlBx5GyFBbS5vFLqPPrp8w+43bya9UPOpHEu8ICWo4cQV6dxky9/rW4WZN140bfivfOO/56RsIvartolvI1c4EezrkVW9pHBV4EvPAC3HuvX9wkTn7xRBPn/Ni655/3LXQDB/rWsXBeCFfK3cDOD57D7OF3sXzAsTt0LOd8N84ffvD/j7Q0OO64KBtj5Rzsvz80bQonnxx0moQwZw7cead/s+Loo/0Y0USVvmQOOz1xOZPu/5rsNl2CjiNbUGljDu1vOZa0zEXMPWVE3I2Lkuhl+Xm0eu5aNjTflb+vHlkx3XWiwOjRm+YOSLQ394IQVIGXjJ9kZV9gEX6SleNKdsE0s4bAMuecM7OewNtAC7eVUCrwwmzFCt/aceutsPPOQaeJO//8Aw895MfXDR4c/sKupIwF02n97DWMf+J3chu3rpBjFhd733wD334LDRrASSf5gq9x4wp5iu3z6KO+W+aDD2rWzDBbtsz35P7hBxgyxE/UEyfXKjuk5h9jaDh6JOOfGqdWoSiUsmYFHa8eQkHVWiwYehkuOTXoSJJgKm3ModVz15G9Uyf+ueK5uPjFWVjo1zq94Qb/pq+EVyAFXuiJhwAP4pdJeN45d5uZnQ3gnHvSzM4HzgEKgBzgUufcz1s7pgq8MDv1VFi/Hs49N+gkcWX1anj6aT8AefBg2H33YBpH637/LjWm/MSER3/BpVTsBU1hoe/GOWaMX5OvWzffeHbEERFeguG333yl8eCD0KxZBJ84saxYAa+8Al9+CXvs4btjVqkSdKro0nD0C2QsmsHEB76t8J832X5VZk+m4zUHk9Vpb5YOPiUuLqwlNlXamEOrZ69h3c7dmX7Z03FxLk6eDHfcAdOnawqHcAuswAsHFXhh9MMPcMwx8NxzZc+/L+XinL8Afuwx6N7dd8cMdMIJ52j5wg2sa9eTWefeF7an2bjRd9EYM8Yvwn7AAb7YGzgwzA1qixf7saPnn69BAGHgHEycCO++6yfg6dHDj7WrXj3oZFGqqIiWL95ETpM2/HPFs+qzFAWKl0FYdOg5ZHXfL+g4IlTK3UDrZ65mTYc+zLj48bgo8u66y88CfvfdQSeJbyrwpGx5eX6NsOOOg733DjpNXFi61A9lXL7cj0mKlsakpPVr2Pn+s5lx4aOs6Hdk2J8vK8sXet9+62fYOuYYv3JBr14VfL2bmwt77QVdu8Lxx1fggRObczB3rv/+ffWVv2+PPfwbFok0O+b2qpS7gTaPXsSiw85j0VEXBx0ncTlH85dvo+l7jzLnlBHkNG8XdCKR/6mUu57Wz/wfa9v1jIuWvJUr4cwztWxCuKnAk7JddRX8+iuMGKF3mXeQc36g8ZNP+slIBwyIvrlqMhbOoPUz/8fkOz5mbfveEXveRYt8sTdmjO/SefTRvp9+9+47eNo555sIFy2C667TObyDNmzwLa8TJvhZU3Nz/fs/nTr5NRn18m6b1FVL2emxS5hz+m0sHXJa0HESTnJ2Fu3uOIn0xbOZe8oICrRGoUShSrkbaPX89axvsSv/XD0SlxTb48ffest30/ziC/3NCBcVeLJ1333nm1Weegpq1gw6TUxbswbuuce3eBx/PDRqFHSiLas29Veavf0gEx79ucImXSkv5/zC6t9+62fccs6vyHH44b4o3uZunHfd5Wd/feABNStto8JCmD/f/yH++2+/2P28edCiBbRu7ScCatZMf6B3VNry+ez0xBXMPO9+lu+nFuZIqTp9Ah1uOJJ1u3RnycFnxdW6YxJ/LC+XliNHsLFuU6bd8FpMn68FBXDWWb4n0+GHB50mPqnAky1bvdq/LX/++b7PnGy38ePh9tv90msHHAApMfB7uc6PH1Dn10+Y8PivFFSvHUgG5/wU+z/95BuRFy+Gfff1r+HAgX7B96169FFf4N1/P9SrF5HMscQ5yMmBzEw/4+WyZb6r7Ny5fjH7pUuhdm2/okTjxv71bt4cUjUnSIVLXzKH1k9dyfRLnoxI9+iE5hyNPn6a1s9cw6LDzyOr64CgE4mUi+Xn0WLULRRWqcGUEW9RlBa7b1pOmuT/PE+ZArVqBZ0m/qjAk9I5B0OH+rfwL7ig7P2lVIWFfl6a0aPh2GNjr7954w+eIHXFIv68+3OKMoKfBjEz0xfLEybA2LH+j8Jee0G/frDnnn71jv8NT3jmGbjxRl/cNWwYaO5wKCryw2M3bvRbbu6/tw0bNm3r1/sW5LVr/bZmjX//JivLt77Vru1fy5o1/Vavnl+rrl49FXORVNw9+p9Ln1KRFyYpWZnscs/pVJ7/N/NOvI6N9ct6l0gkyhQW0Pz1e0jKWcfkOz6hoFrsVkePPOJnWH7ppaCTxB8VeFK6l1/2F8ePP+5Xq5Zttny5H7YIfixZRJcDqChFhTR74z6S12fx512fUVgleqZELCryrXt//QXTpvmPWVnQoQOcXflFho6/ilnn3kedTk1o0CDyYx3z8nzrWHGRVfx5To7figux4tubF2kbN24q4PLzN33My/Nbfr5vCU5N9VtKiv9RLb6dlgbp6Zvuq1zZbxkZ/g9qtWp+hkv9eEeXjAXTafX89cw/7moWHn1J0HHiSu1fPqHdPaezutsAlg4+RevbSewqKqLxx09TZdYk/rz3SzbWaxp0ou2Sk+MnXHn6ab+CkVQcFXjyX1On+sFOd94JbdsGnSYm/fqrf/n69oV99onxSa+Kimjy3iOkL5/PpHu+DKy7ZnmsWwcZo56m18fX8ewu9/JPTnMyM32rVY0avpWquLWqalW/ZWT4Aig52W9JSb54dM5/LCj4d2FVXJyVLNhKK+Cc88dOT99UaBVvxQVZyY8lC7XybMnJGvsWr1JWLaX1s9eysvcQZp73YPTNxBRjktetZqcnLqf22M+Zf+wVrN+pc9CRRCpEvW/eoM4vH/PnPV+wocWuQcfZLhMmwH33+UvPGjWCThM/VODJv61a5RewGjoUBg0KOk3MKe6S+dlnfiKVnXYKOlEFcY7GHz1F5Tl/Men+MeTXqh90ov8qLGSnp66g7vfvMuf0W8kr8Y5mfr4v/tau9R+zsze1khW3jBUVbdrMNhXlSUl+Ky7+SraSbd5SVvJzFWCyI5Jysmnx4k3k1WrItOteiarW85jhHPW/eYM2j17Mmg59WHLg6RSlB9/VXKQi1Rz7JY0/eZpp173K6h4Dg46zXR56yL/x+vzzQSeJHyrwZJOCAth/fz/45uyzg04Tc1at8l0y8/P9koEx2SVza5yjwecvUnPid0y55V2y23YNOtH/JOVks+vNw0hbtYS5J92gi2GJC1aQT+P3H6PqrD+ZesPrrNu1Z9CRYkb6olns/OA5pC+Zw8KjLmZDyw5BRxIJmyqzJtFi1K3MPWUEiw87L+g422zDBhg+HB58EI46Kug08WFrBV4sdyqT7XHppf6n7Mwzg04ScyZOhDPO8DMNnnFGHBZ3AGYsG3wKy/Y/kc6X7UfDT54NOhEAledOpet5ewCO2WfeoeJO4oZLTmHRURezdPDJdLp6CM1fvt13E5AtSl63mjaPXkz3s3cnp2Frpl/yhIo7iXvrd+rMzPMfpNkb99H2wfOwwoKgI22TypXh+uv90gkzZgSdJv6pBS+RPP003Habn1Y+LquT8CgqgldegXfe8bNktmsXdKLISFs2j5Yv3kxWp77MuPhxitIrRz5EYSHN3ryP5q/dxZLBp7Bqj4PUJ1LiVsrq5TR/7U4K06sw/dKn2NCyfdCRoorlbaTJB4/T4uXbyOq4F8sGnURBtegdLywSDkk52TR/+TZcUjJTR7xFXu3YmkH6gw/gq6/g99+1bO2OUhdNgTfe8Esh3HtvORYWk2Jr1sCtt/rp5k84IfHWga+0MYcm7z5MlTlTmHnBQ6zY67CIFViV5/9NuztOgqIiFh5zGXl1onjVeJGKUlRI3R8/oMFXL7N00MnMPeWmhG+xrpS7gUYfP03z1+4mp0kblh5wKrmNWgUdSyQ4RYU0+OJlao/9jKk3vsmaTn2DTlRuzvk1g5s39/MZyPZTgZfoPvjA9ym88844mhEk/CZNgltu8QuXDxmS2JPcVZ0xgSbvPUZuw5bMuPgxNjQPXzNm+tK5tHjxZur++D5LB53Eyj6HxPgUpSLbLnndahqOfp7qf49lzmm3sHTQybiUxJryP3ntKhp9/DTN3nqA9S12Zfm+x5HTbOegY4lEjWrTfqfZG/ew4NgrWXDMZTHzt3LDBjjvPN9l87TTgk4Tu1TgJbLiqR5vvz32VuAOSGEhjBoF778PxxwD7dVLyissoO6P79Pgq1dZ3W0Ai468iDUd96qwFr2MhTNo9vrd1PvubVb2OYTMfkdRWFldiSWxVZ43jQafv0j68gXMH3YlSw48M5ju0hFUdcYfNHn3Eer98A5r2u9BZv9jyG3cOuhYIlEpZdVSWrxyB/k16vD3/70UM+vlzZ0Ll18Or77q5/6TbacCL1F98YWf6nHECNhtt6DTxIRly/wwxY0b/Uun9Vr+q1JONrXHfkGdXz6iKK0yiw89h1U9B5PTeKdtLvZSVyym/pjXafDly6RlLmDV7oPI7H80hVX0wouUlDH/H+p/8zpV5vzFkgPPYOmQ08lp0iboWBUmZdUy6n/zBg2/eInUzEWs7HMQq3oNoaBaraCjiUS/wkLqj3mNej++z/SLHiNzwNCgE5XL5Mn+EvXLL6Fbt6DTxB4VeInouefgqqvgxhuhY8eg00Q95/yg30cfhb33hv79E7tLZrk4R9XpE6g14SuqTZ+AS05mddcBrNu1FxvrNmFj3Sbk1W2Mw0jKySYpJ5uUdauoMvtPqv89lqrTx5O6ejlZnfqS1aU/2W266kUXKUPasnnU+W00tcZ/xYZmu7DkwDNY0ecQCmrUCTraNktdsZg6v31KvW/epPq0X1nToQ9ZXQewbufu+l0gsh0y5v9N89fuYl273ZlxwSPk124QdKQyff89PP44/PSTRhFtKxV4icQ5uPZa38fwtts0oUo5rF0L998P06fDsGHQrFnQiWKQc6RlLqTqjAmkL5lDyrrVpKxdSUpWJuAoTK9CUVoGhWlV2NiwOTlN2rKhaVtyG7SApOSg04vEHCvIp/rUX6k54Wuq/TOe9Tt1ZMWeh7Gq9xDWt2gflWNxknKyqTb1N2pO/Ia6P39E+rJ5rG3Xk7Xte7O2wx4UpWlKPZEdZXm5NPziJWqP/YLZZ9zGkgPPjMrfByV9+KGfLuL776FJk6DTxA4VeIkiJwdOP923ed98M9RS15atcQ6++w4eftg3ch54IKQm1hwGIhIHLG8jVWf+QfWpv1Ft+jiScrJZu2sv1nTem7XterG+dUfya9WPeKYq86ZSddYkqs6YQI3JP1J5/t/kNN2Z7Ja7sW7Xnqxv2UEtdSJhkr54Fk3ffojC9MrMuPRJstt0CTrSVr3+Oowe7XtTtW0bdJrYoAIvEUye7GcEadYMLrsM0tKCThTVVqyA++6DefPg6KOhlWbcFpE4kbxmBVXmTKHKvClkLJ5FxqJZuEpJrG/Znpwmbclt1IrcBi3Y2KA5+TXqkl+9DvnV65R/ls6iIpI2rCM1azmpq5eRumopqZmLyFg8k4yFM8hYPJv05fPZWK8pOY13IrdhS9a3bM+GZu1wqfrbJBIxRUXU/vUTGn7xEqt6HsCcM2+P6klYPvnEd0AbPRq6dg06TfRTgRfPnPNNUDffDGedBQMHaiHorcjP9wuWv/IK7Lkn7LsvpKQEnUpEJIycI3ntKtKXziFt5WJSsjJJycokdU0mSdlrSMnOImn9GlxSMkWp6RSlZlCUmoZLSsaKisAVYUWFVNqYS1JuNpXyNlKUlkF+9drkV6tNQbVa5FerTX7thmys04i8uk3YWKexijmRKFEpJ5v6Y96gzq8fs/jgs1gw7KqoncDou+/gkUfg7bf9fAiyZSrw4tXMmXDOObB4MVxzjToul+GXX/wvjTp14KCDoEH0jz0WEYkM56iUl4vl51GpIA/L3+iLOzOcVQIzX/ylZVCUkhb1Y3pE5L9SVi+nwRejqPHXjyw+aDgLj7ks4t23y2P8eLjjDr+MwpVX6tfNlqjAizfr1vkVuJ991vcvPPpoSNZEFVvy11/+pVq6FA45ROvaiYiISOJKXbWUet+8Qa0/vmHpwBNYdMSF5DTbOehY/7J8uZ8rsH593+uqfvTVoYFTgRcv8vJ85+TrrvOdk087DerWDTpV1Jo61a8WMW+e74rZs6fG84uIiIgAJK9ZSd0f36PO75+R3boTi468kJV7HISLktmtCwpg5EgYMwYeewwOO0yjkEpSgRfr1q2Dp57yc/k3bw4nnaRmqC0oLIQff4Q334QlS3xh16uXGjhFRERESmMFedSc9B11fv6Y1FVLWT7gWJbtf6JfkzIKKqo//vAFXrNmfr3iDh2CThQdVODFIudg4kR44QV4+WXo3t13xdw5uprQo8XSpfDll34tlRo1YK+9/NIHarETERERKZ+0ZfOp+ccYav0xBpecyvJ+R7Oyz8Gs3bVXoBdVhYX+Gm/UKBg61I/NS/QZ0FXgxZIZM+C993yb9Jo1sN9+MGgQNG4cdLKos2qVnzjls89gzhzo0sV3w9Ta7iIiIiI7wDkqz/+b6lN+pvq030lZs4JVPQexusf+ZHXuR27DloG07mVl+V5ao0f7WTavuAL69Il4jKigAi+aFVcpo0f7BUDWr4fevWHAAOjUSVMHlVBQANOnw9ix8NNPsHAh7LqrL+x23VXdMEVERETCIWX1MqpP/Y0qcyZTdeZEXEoaWZ36snbX3qzbpQfZbbpQlFElYnlycvwb/O++C5Ur+1a9oUMTq/tmYAWemQ0GHgKSgGedc3du9riFHh8CbABOcc5N2NoxY7rAy8yEKVP89ttvvrBbssRXJ127+uannXaKiv7OQSsq8t0uZ83yRd2kSb5xs25daNPGv2StW6uoExEREYko50jLXECV2ZPJWDiTyotmkL54FhvrN2d9i11Z37ojG1p2YEOTtuQ2bh3WNfeKimDaNPj+e79VqeLbSPr3h733hqbRu677DgukwDOzJGA6MBBYCIwFhjnnppbYZwhwAb7A6wU85JzrtbXjRm2Bl5/vi7WlS2HZMr823dy5MHu27z84e7bfp3VraNHCf2zf3ncgTsCBYs75xsqVK2HFCl/7Llq0aZs/HzIy/NJ+jRpBy5b+pcrICDq5iIiIiJRkBXmkLV9A+tJ5pC2bR3rmAtJWLCYtcyGuUhK5DVuwsV5TNtZtwsb6zcmr04i8GvXIr1mP/Bp1ya9eh8KqNXZoBs+iIr9E9J9/+raUSZMgJcVfbnfqBLvt5q8lmzf3E7bE+jVlUAXeHsAI59yg0O3/A3DO3VFin6eAb51zr4Vu/wP0d84t2dJxo7bAO+AA31YMfiXt2rX9oh2NGvkVtRs39vfHoD8m+prVOf/DU3IrLNy0FRT4Grbklpvrt40bfXN67sb/Hj81BapVg+rVoWZNv9WpA2lpEf6PioiIiEjFcZC6cS2V1ywhPXsF6dkryMheQdqG1aTmZJG2IYtKRQWlfmlRpSTy06qSn1aVwpQMClIyKEjNoDAlg8KUdAqT0yhMTqMoOZWipJRNW6VkXKUkiiolM6NObz6rfARz5vjGg2XLIDv738+TluavO6tX91u1alC1qi8A09OhbVs/qUu09RrbWoEXzqhNgAUlbi/Et9KVtU8T4F8FnpkNB4aHbmaHCsGo0hyaVoPqgG+WWrnS9ymMMqsguTaU/pO0BY6MjIp6G8BCR/T/hI6a74OxqoKeRMplNYXUIvFaj+W/dC4I6DyQTXQuCETqPEgKbcW3iqhEkSVRhBUVQs4av22nXNLcFNrk/vcRA5KSISlp40artHix73y3JddeO2kiFBRud5DwaLGlB8JZ4JU2kGzzOqE8++Ccexp4uiJCJTozG7d4C9W+JBZ/LuTrXBCdCwLoPJBNdC4I6DyIZeGconEh0KzE7abA5rVxefYRERERERGRcghngTcWaGtmrcwsFTgW+HCzfT4ETjKvN7Bma+PvREREREREZMvC1kXTOVdgZucDn+M71z7vnJtiZmeHHn8S+BQ/g+ZM/DIJp4Yrj/yPurpKMZ0LUkzngoDOA9lE54KAzoOYFXMLnYuIiIiIiEjpwtlFU0RERERERCJIBZ6IiIiIiEicUIEXp8xssJn9Y2YzzezqUh43M3s49PifZtYtiJwSXuU4D44Pff//NLOfzaxzEDkl/Mo6F0rst7uZFZrZUZHMJ5FTnnPBzPqb2UQzm2Jm30U6o4RfOf4+1DCzj8xsUug80DwJccrMnjez5Wb21xYe1zVjjFGBF4fMLAl4DDgAaA8MM7P2m+12ANA2tA0HnohoSAm7cp4Hc4B+zrlOwC1oQHVcKue5ULzfXfjJsSQOledcMLOawOPAIc65DsDRkc4p4VXO3wnnAVOdc52B/sB9oVnRJf6MBAZv5XFdM8YYFXjxqScw0zk32zmXB7wOHLrZPocCLznvV6CmmTWKdFAJqzLPA+fcz8651aGbv+LXopT4U57fCQAXAO8AyyMZTiKqPOfCccC7zrn5AM45nQ/xpzzngQOqmZkBVYFVQEFkY0okOOe+x39/t0TXjDFGBV58agIsKHF7Yei+bd1HYtu2fo9PB0aHNZEEpcxzwcyaAIcDT0Ywl0ReeX4v7AzUMrNvzWy8mZ0UsXQSKeU5Dx4FdgUWA5OBi5xzRZGJJ1FG14wxJmzr4EmgrJT7Nl8Pozz7SGwr9/fYzPbBF3h7hTWRBKU858KDwFXOuUL/hr3EqfKcC8lAd2BfIAP4xcx+dc5ND3c4iZjynAeDgInAAGAn4Esz+8E5tzbM2ST66JoxxqjAi08LgWYlbjfFvwO3rftIbCvX99jMOgHPAgc451ZGKJtEVnnOhR7A66Hiri4wxMwKnHPvRyShREp5/z6scM6tB9ab2fdAZ0AFXvwoz3lwKnCn8wsmzzSzOUA74PfIRJQoomvGGKMumvFpLNDWzFqFBkQfC3y42T4fAieFZkbqDaxxzi2JdFAJqzLPAzNrDrwLnKh35+NameeCc66Vc66lc64l8DZwroq7uFSevw8fAH3NLNnMKgO9gGkRzinhVZ7zYD6+FRczawDsAsyOaEqJFrpmjDFqwYtDzrkCMzsfPxNeEvC8c26KmZ0devxJ4FNgCDAT2IB/p07iSDnPgxuAOsDjoZabAudcj6AyS3iU81yQBFCec8E5N83MPgP+BIqAZ51zpU6fLrGpnL8TbgFGmtlkfBe9q5xzKwILLWFjZq/hZ0qta2YLgRuBFNA1Y6wy3/IuIiIiIiIisU5dNEVEREREROKECjwREREREZE4oQJPREREREQkTqjAExERERERiRMq8EREREREROKECjwREYl5ZnatmU0xsz/NbKKZ9drKvqeYWeMSt581s/Zb2b9d6Jh/mNlO25irv5n1KXH7bDM7aVuOISIisi20Dp6IiMQ0M9sDOAjo5pzbaGZ1gdStfMkpwF/AYgDn3BllPMVhwAfOuRu3I15/IBv4OfRcWnNQRETCSi14IiIS6xoBK5xzGwGccyucc4vN7AYzG2tmf5nZ0+YdBfQAXgm1ymWY2bdm1sPMksxsZGj/yWZ2iZkNAS4GzjCzbwDM7H0zGx9qMRxeHMLMBpvZBDObZGZfm1lL4GzgktBz9TWzEWZ2eWj/Lmb2a6jV8T0zqxW6/1szu8vMfjez6WbWN4KvpYiIxDgVeCIiEuu+AJqFiqHHzaxf6P5HnXO7O+d2AzKAg5xzbwPjgOOdc12cczkljtMFaOKc28051xF4wTn3KfAk8IBzbp/Qfqc557rjC8ULzayOmdUDngGOdM51Bo52zs0t8bVdnHM/bJb7JeAq51wnYDJQsoUw2TnXE19cbk/LoYiIJCgVeCIiEtOcc9lAd2A4kAm8YWanAPuY2W9mNhkYAHQo41CzgdZm9oiZDQbWbmG/C81sEvAr0AxoC/QGvnfOzQllWrW1JzKzGkBN59x3obteBPYuscu7oY/jgZZl5BYREfkfjcETEZGY55wrBL4Fvg0VdGcBnYAezrkFZjYCSC/jGKvNrDMwCDgPOAY4reQ+ZtYf2A/Ywzm3wcy+DR3XAFdx/yM2hj4Wor/VIiKyDdSCJyIiMc3MdjGztiXu6gL8E/p8hZlVBY4q8fg6oFopx6kLVHLOvQNcD3Qr5elqAKtDxV07fMsdwC9APzNrFTpW7a09l3NuDbC6xPi6E4HvNt9PRERkW+ldQRERiXVVgUfMrCZQAMzEd9fMwo9tmwuMLbH/SOBJM8sB9ihxfxPgBTMrfvPz/0p5rs+As83sT3wR+SuAcy4zNOHKu6GvXw4MBD4C3jazQ4ELNjvWyaEclfHdQ0/d1v+4iIjI5sy5iuxRIiIiIiIiIkFRF00REREREZE4oQJPREREREQkTqjAExERERERiRMq8EREREREROKECjwREREREZE4oQJPREREREQkTqjAExERERERiRP/D8onKpuBBwDHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,4))\n",
    "ax=sns.kdeplot(df.loc[(df['Turnover'] == 0),'Satisfaction'] , color='b',shade=True, label='no turnover') # blue\n",
    "ax=sns.kdeplot(df.loc[(df['Turnover'] == 1),'Satisfaction'] , color='r',shade=True, label='turnover') # red\n",
    "plt.title('Employee Satisfaction to Turnover / No Turnover')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc11f098",
   "metadata": {},
   "source": [
    "Summary: \n",
    "\n",
    "1. Low employee satisfaction levels (0.3~0.5) had the highest attrition rate.\n",
    "2. Low employee satisfaction levels (0.2 or less) had the next highest attrition rate.\n",
    "3. Conversely, high employee satisfaction levels (0.7 or more) had the third highest attrition rate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4ca13d6",
   "metadata": {},
   "source": [
    "# Employee Turnover to Tenure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7186239b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Percent')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x='Tenure', y='Tenure', hue='Tenure', data=df, estimator=lambda x: len(x) / len(df) * 100)\n",
    "ax.set(ylabel='Percent')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b98c7d",
   "metadata": {},
   "source": [
    "Summary: \n",
    "1. Employees with under 3 years of tenure had the highest attrition rate. \n",
    "2. Employees with under 1 year of tenure had next highest attrition rate.\n",
    "3. Employees with under 4 years of tenure had next highest attrition rate.\n",
    "4. Employees with under 5 years of tenure had next highest attrition rate.\n",
    "5. Employees reaching just over 6 years of tenure began a downward attrition rate \n",
    "until it increased again at 10+ years of tenure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a5ea90",
   "metadata": {},
   "source": [
    "# Employee Work Accidents to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7b9d3e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Work_accident'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = 'Work_accident',hue='Turnover', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d158dd9",
   "metadata": {},
   "source": [
    "# Employee Promotion to Turnover"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "12105ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='count', ylabel='Promotion'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y = 'Promotion',hue='Turnover', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e33c47",
   "metadata": {},
   "source": [
    "Summary: \n",
    "    \n",
    "1. Employees who received a promotion tended to stay at the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66bf920",
   "metadata": {},
   "source": [
    "# Employee Project Count to Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ee34d7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ProjectCount', ylabel='Evaluation'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"ProjectCount\", y=\"Evaluation\", hue=\"Turnover\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e973f964",
   "metadata": {},
   "source": [
    "Summary: \n",
    "1. Employees with an evaluation-average of roughly 70% tended to stay at the company.\n",
    "2. Skew in employee turnover after 3 projects. \n",
    "3. Employees with two projects and a lower evaluation left the company.\n",
    "4. Employees with more than 3 projects and a higher evaluation left the company."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd63b72",
   "metadata": {},
   "source": [
    "# Project Count to Average Monthly Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "954805b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ProjectCount', ylabel='AvgMonthlyHours'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"ProjectCount\", y=\"AvgMonthlyHours\", hue=\"Turnover\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef8a38d",
   "metadata": {},
   "source": [
    "Summary: \n",
    "1. The average employees worked about 200hours/month remained at the company. \n",
    "2. Employee turnover for around 150hours/month or 250hours/month worked resulted in a higher attrition rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c174e58a",
   "metadata": {},
   "source": [
    "# Employee Satisfaction to Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "096c8a1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f977d590850>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 411.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='Satisfaction', y='Evaluation', data=df,\n",
    "           fit_reg=False, # no regression line\n",
    "           hue='Turnover')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11150894",
   "metadata": {},
   "source": [
    "Summary: \n",
    "\n",
    "Three distinct employee clusters -  \n",
    "\n",
    "Cluster 1: (Worked and Dissatisfied with...)\n",
    "Employee satisfaction was below 0.2; evaluations were greater than 0.75, could indicate an employee who put in the work but was unsatisfied with the job, the work environment and/or the company. \n",
    "\n",
    "Cluster 2: (Worked and Dissatisfied because...)\n",
    "Employee satisfaction between about 0.35~0.45; evaluations were below ~0.58, could indicate an employee who received a poor evaluation and as a result felt bad about it or for being at the company itself. \n",
    "\n",
    "Cluster 3: (Worked and Satisfied about...)\n",
    "Employee satisfaction between 0.7~1.0; evaluations were greater than 0.8, could indicate an employee who was optimal due to being both satisfied with this job who also received a high performance evaluation (which could be self-reinforcing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4b096c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Employee Turnover Cluster\n",
    "kmeans = KMeans(n_clusters=3,random_state=2)\n",
    "kmeans.fit(df[df.Turnover==1][[\"Satisfaction\",\"Evaluation\"]])\n",
    "\n",
    "kmeans_colors = ['green' if c == 0 else 'blue' if c == 2 else 'red' for c in kmeans.labels_]\n",
    "\n",
    "fig = plt.figure(figsize=(10, 6))\n",
    "plt.scatter(x=\"Satisfaction\",y=\"Evaluation\", data=df[df.Turnover==1],\n",
    "            alpha=0.25,color = kmeans_colors)\n",
    "plt.xlabel(\"Satisfaction\")\n",
    "plt.ylabel(\"Evaluation\")\n",
    "plt.scatter(x=kmeans.cluster_centers_[:,0],y=kmeans.cluster_centers_[:,1],color=\"black\",marker=\"X\",s=100)\n",
    "plt.title(\"Employee Turnover Cluster\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6ba1bfcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Dummy Variables\n",
    "department = pd.get_dummies(data = df['Department'],prefix ='Dep')\n",
    "salary = pd.get_dummies(data = df['Salary'], prefix = 'Sal')\n",
    "df.drop(['Department','Salary'],axis = 1, inplace = True)\n",
    "df = pd.concat([df, department, salary],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b35fbe5c",
   "metadata": {},
   "outputs": [
    {
     "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>Satisfaction</th>\n",
       "      <th>Evaluation</th>\n",
       "      <th>ProjectCount</th>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>Promotion</th>\n",
       "      <th>Dep_IT</th>\n",
       "      <th>Dep_RandD</th>\n",
       "      <th>...</th>\n",
       "      <th>Dep_hr</th>\n",
       "      <th>Dep_management</th>\n",
       "      <th>Dep_marketing</th>\n",
       "      <th>Dep_product_mng</th>\n",
       "      <th>Dep_sales</th>\n",
       "      <th>Dep_support</th>\n",
       "      <th>Dep_technical</th>\n",
       "      <th>Sal_high</th>\n",
       "      <th>Sal_low</th>\n",
       "      <th>Sal_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Satisfaction</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105021</td>\n",
       "      <td>-0.142970</td>\n",
       "      <td>-0.020048</td>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.025605</td>\n",
       "      <td>0.006373</td>\n",
       "      <td>0.006615</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012841</td>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.005715</td>\n",
       "      <td>0.006919</td>\n",
       "      <td>0.004007</td>\n",
       "      <td>0.009185</td>\n",
       "      <td>-0.009345</td>\n",
       "      <td>0.029708</td>\n",
       "      <td>-0.047415</td>\n",
       "      <td>0.031367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evaluation</th>\n",
       "      <td>0.105021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>-0.005471</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009645</td>\n",
       "      <td>0.009662</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>-0.001989</td>\n",
       "      <td>-0.023031</td>\n",
       "      <td>0.017104</td>\n",
       "      <td>0.013742</td>\n",
       "      <td>-0.020628</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>0.006191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ProjectCount</th>\n",
       "      <td>-0.142970</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>0.003287</td>\n",
       "      <td>0.009703</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.027356</td>\n",
       "      <td>0.009728</td>\n",
       "      <td>-0.023064</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>-0.013388</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.028596</td>\n",
       "      <td>-0.008726</td>\n",
       "      <td>-0.002504</td>\n",
       "      <td>0.007377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgMonthlyHours</th>\n",
       "      <td>-0.020048</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>-0.001177</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010783</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>-0.005494</td>\n",
       "      <td>-0.001718</td>\n",
       "      <td>-0.002444</td>\n",
       "      <td>0.013638</td>\n",
       "      <td>-0.007101</td>\n",
       "      <td>-0.001050</td>\n",
       "      <td>0.005007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tenure</th>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>-0.006053</td>\n",
       "      <td>-0.021116</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022194</td>\n",
       "      <td>0.115436</td>\n",
       "      <td>0.012096</td>\n",
       "      <td>-0.003919</td>\n",
       "      <td>0.015150</td>\n",
       "      <td>-0.030111</td>\n",
       "      <td>-0.027991</td>\n",
       "      <td>0.039953</td>\n",
       "      <td>-0.040110</td>\n",
       "      <td>0.018299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>-0.009293</td>\n",
       "      <td>0.017167</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015649</td>\n",
       "      <td>0.011242</td>\n",
       "      <td>0.011367</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>-0.004955</td>\n",
       "      <td>0.012079</td>\n",
       "      <td>-0.006070</td>\n",
       "      <td>0.009040</td>\n",
       "      <td>-0.006813</td>\n",
       "      <td>0.001856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turnover</th>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061788</td>\n",
       "      <td>-0.010925</td>\n",
       "      <td>-0.046596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028249</td>\n",
       "      <td>-0.046035</td>\n",
       "      <td>-0.000859</td>\n",
       "      <td>-0.011029</td>\n",
       "      <td>0.009923</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>0.020076</td>\n",
       "      <td>-0.120929</td>\n",
       "      <td>0.134722</td>\n",
       "      <td>-0.068833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Promotion</th>\n",
       "      <td>0.025605</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>-0.061788</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.038942</td>\n",
       "      <td>0.021268</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001531</td>\n",
       "      <td>0.128087</td>\n",
       "      <td>0.049253</td>\n",
       "      <td>-0.037288</td>\n",
       "      <td>0.012353</td>\n",
       "      <td>-0.035605</td>\n",
       "      <td>-0.035799</td>\n",
       "      <td>0.076756</td>\n",
       "      <td>-0.082832</td>\n",
       "      <td>0.040985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_IT</th>\n",
       "      <td>0.006373</td>\n",
       "      <td>0.001269</td>\n",
       "      <td>0.003287</td>\n",
       "      <td>0.006967</td>\n",
       "      <td>-0.006053</td>\n",
       "      <td>-0.009293</td>\n",
       "      <td>-0.010925</td>\n",
       "      <td>-0.038942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.070240</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.067949</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>-0.073524</td>\n",
       "      <td>-0.075503</td>\n",
       "      <td>-0.184302</td>\n",
       "      <td>-0.124705</td>\n",
       "      <td>-0.140484</td>\n",
       "      <td>-0.016089</td>\n",
       "      <td>0.005116</td>\n",
       "      <td>0.003775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_RandD</th>\n",
       "      <td>0.006615</td>\n",
       "      <td>-0.005471</td>\n",
       "      <td>0.009703</td>\n",
       "      <td>-0.001177</td>\n",
       "      <td>-0.021116</td>\n",
       "      <td>0.017167</td>\n",
       "      <td>-0.046596</td>\n",
       "      <td>0.021268</td>\n",
       "      <td>-0.070240</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.053570</td>\n",
       "      <td>-0.049274</td>\n",
       "      <td>-0.057965</td>\n",
       "      <td>-0.059525</td>\n",
       "      <td>-0.145300</td>\n",
       "      <td>-0.098315</td>\n",
       "      <td>-0.110755</td>\n",
       "      <td>-0.015115</td>\n",
       "      <td>-0.011887</td>\n",
       "      <td>0.020402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_accounting</th>\n",
       "      <td>-0.028649</td>\n",
       "      <td>0.002193</td>\n",
       "      <td>0.004189</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>0.003909</td>\n",
       "      <td>-0.012836</td>\n",
       "      <td>0.015201</td>\n",
       "      <td>-0.004852</td>\n",
       "      <td>-0.069293</td>\n",
       "      <td>-0.054629</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.052848</td>\n",
       "      <td>-0.048610</td>\n",
       "      <td>-0.057183</td>\n",
       "      <td>-0.058723</td>\n",
       "      <td>-0.143341</td>\n",
       "      <td>-0.096989</td>\n",
       "      <td>-0.109262</td>\n",
       "      <td>0.011821</td>\n",
       "      <td>-0.009759</td>\n",
       "      <td>0.003285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_hr</th>\n",
       "      <td>-0.012841</td>\n",
       "      <td>-0.009645</td>\n",
       "      <td>-0.027356</td>\n",
       "      <td>-0.010783</td>\n",
       "      <td>-0.022194</td>\n",
       "      <td>-0.015649</td>\n",
       "      <td>0.028249</td>\n",
       "      <td>-0.001531</td>\n",
       "      <td>-0.067949</td>\n",
       "      <td>-0.053570</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.047667</td>\n",
       "      <td>-0.056075</td>\n",
       "      <td>-0.057584</td>\n",
       "      <td>-0.140562</td>\n",
       "      <td>-0.095109</td>\n",
       "      <td>-0.107143</td>\n",
       "      <td>-0.017858</td>\n",
       "      <td>-0.015690</td>\n",
       "      <td>0.025765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_management</th>\n",
       "      <td>0.007172</td>\n",
       "      <td>0.009662</td>\n",
       "      <td>0.009728</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.115436</td>\n",
       "      <td>0.011242</td>\n",
       "      <td>-0.046035</td>\n",
       "      <td>0.128087</td>\n",
       "      <td>-0.062500</td>\n",
       "      <td>-0.049274</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.051578</td>\n",
       "      <td>-0.052966</td>\n",
       "      <td>-0.129289</td>\n",
       "      <td>-0.087482</td>\n",
       "      <td>-0.098551</td>\n",
       "      <td>0.209077</td>\n",
       "      <td>-0.084641</td>\n",
       "      <td>-0.030716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_marketing</th>\n",
       "      <td>0.005715</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>-0.023064</td>\n",
       "      <td>-0.008210</td>\n",
       "      <td>0.012096</td>\n",
       "      <td>0.011367</td>\n",
       "      <td>-0.000859</td>\n",
       "      <td>0.049253</td>\n",
       "      <td>-0.073524</td>\n",
       "      <td>-0.057965</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.056075</td>\n",
       "      <td>-0.051578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.062308</td>\n",
       "      <td>-0.152093</td>\n",
       "      <td>-0.102911</td>\n",
       "      <td>-0.115933</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>-0.009479</td>\n",
       "      <td>0.004213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_product_mng</th>\n",
       "      <td>0.006919</td>\n",
       "      <td>-0.001989</td>\n",
       "      <td>0.000829</td>\n",
       "      <td>-0.005494</td>\n",
       "      <td>-0.003919</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>-0.011029</td>\n",
       "      <td>-0.037288</td>\n",
       "      <td>-0.075503</td>\n",
       "      <td>-0.059525</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.057584</td>\n",
       "      <td>-0.052966</td>\n",
       "      <td>-0.062308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.156187</td>\n",
       "      <td>-0.105682</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>-0.006514</td>\n",
       "      <td>0.006191</td>\n",
       "      <td>-0.002632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_sales</th>\n",
       "      <td>0.004007</td>\n",
       "      <td>-0.023031</td>\n",
       "      <td>-0.013388</td>\n",
       "      <td>-0.001718</td>\n",
       "      <td>0.015150</td>\n",
       "      <td>-0.004955</td>\n",
       "      <td>0.009923</td>\n",
       "      <td>0.012353</td>\n",
       "      <td>-0.184302</td>\n",
       "      <td>-0.145300</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.140562</td>\n",
       "      <td>-0.129289</td>\n",
       "      <td>-0.152093</td>\n",
       "      <td>-0.156187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.257967</td>\n",
       "      <td>-0.290608</td>\n",
       "      <td>-0.039273</td>\n",
       "      <td>0.023766</td>\n",
       "      <td>-0.002174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_support</th>\n",
       "      <td>0.009185</td>\n",
       "      <td>0.017104</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>-0.002444</td>\n",
       "      <td>-0.030111</td>\n",
       "      <td>0.012079</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>-0.035605</td>\n",
       "      <td>-0.124705</td>\n",
       "      <td>-0.098315</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.095109</td>\n",
       "      <td>-0.087482</td>\n",
       "      <td>-0.102911</td>\n",
       "      <td>-0.105682</td>\n",
       "      <td>-0.257967</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196636</td>\n",
       "      <td>-0.029184</td>\n",
       "      <td>0.022038</td>\n",
       "      <td>-0.006035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dep_technical</th>\n",
       "      <td>-0.009345</td>\n",
       "      <td>0.013742</td>\n",
       "      <td>0.028596</td>\n",
       "      <td>0.013638</td>\n",
       "      <td>-0.027991</td>\n",
       "      <td>-0.006070</td>\n",
       "      <td>0.020076</td>\n",
       "      <td>-0.035799</td>\n",
       "      <td>-0.140484</td>\n",
       "      <td>-0.110755</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.107143</td>\n",
       "      <td>-0.098551</td>\n",
       "      <td>-0.115933</td>\n",
       "      <td>-0.119054</td>\n",
       "      <td>-0.290608</td>\n",
       "      <td>-0.196636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.014672</td>\n",
       "      <td>0.015674</td>\n",
       "      <td>-0.007673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sal_high</th>\n",
       "      <td>0.029708</td>\n",
       "      <td>-0.020628</td>\n",
       "      <td>-0.008726</td>\n",
       "      <td>-0.007101</td>\n",
       "      <td>0.039953</td>\n",
       "      <td>0.009040</td>\n",
       "      <td>-0.120929</td>\n",
       "      <td>0.076756</td>\n",
       "      <td>-0.016089</td>\n",
       "      <td>-0.015115</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.017858</td>\n",
       "      <td>0.209077</td>\n",
       "      <td>0.009642</td>\n",
       "      <td>-0.006514</td>\n",
       "      <td>-0.039273</td>\n",
       "      <td>-0.029184</td>\n",
       "      <td>-0.014672</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.292560</td>\n",
       "      <td>-0.260274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sal_low</th>\n",
       "      <td>-0.047415</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>-0.002504</td>\n",
       "      <td>-0.001050</td>\n",
       "      <td>-0.040110</td>\n",
       "      <td>-0.006813</td>\n",
       "      <td>0.134722</td>\n",
       "      <td>-0.082832</td>\n",
       "      <td>0.005116</td>\n",
       "      <td>-0.011887</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015690</td>\n",
       "      <td>-0.084641</td>\n",
       "      <td>-0.009479</td>\n",
       "      <td>0.006191</td>\n",
       "      <td>0.023766</td>\n",
       "      <td>0.022038</td>\n",
       "      <td>0.015674</td>\n",
       "      <td>-0.292560</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.847144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sal_medium</th>\n",
       "      <td>0.031367</td>\n",
       "      <td>0.006191</td>\n",
       "      <td>0.007377</td>\n",
       "      <td>0.005007</td>\n",
       "      <td>0.018299</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>-0.068833</td>\n",
       "      <td>0.040985</td>\n",
       "      <td>0.003775</td>\n",
       "      <td>0.020402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025765</td>\n",
       "      <td>-0.030716</td>\n",
       "      <td>0.004213</td>\n",
       "      <td>-0.002632</td>\n",
       "      <td>-0.002174</td>\n",
       "      <td>-0.006035</td>\n",
       "      <td>-0.007673</td>\n",
       "      <td>-0.260274</td>\n",
       "      <td>-0.847144</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Satisfaction  Evaluation  ProjectCount  AvgMonthlyHours  \\\n",
       "Satisfaction         1.000000    0.105021     -0.142970        -0.020048   \n",
       "Evaluation           0.105021    1.000000      0.349333         0.339742   \n",
       "ProjectCount        -0.142970    0.349333      1.000000         0.417211   \n",
       "AvgMonthlyHours     -0.020048    0.339742      0.417211         1.000000   \n",
       "Tenure              -0.100866    0.131591      0.196786         0.127755   \n",
       "Work_accident        0.058697   -0.007104     -0.004741        -0.010143   \n",
       "Turnover            -0.388375    0.006567      0.023787         0.071287   \n",
       "Promotion            0.025605   -0.008684     -0.006064        -0.003544   \n",
       "Dep_IT               0.006373    0.001269      0.003287         0.006967   \n",
       "Dep_RandD            0.006615   -0.005471      0.009703        -0.001177   \n",
       "Dep_accounting      -0.028649    0.002193      0.004189         0.000524   \n",
       "Dep_hr              -0.012841   -0.009645     -0.027356        -0.010783   \n",
       "Dep_management       0.007172    0.009662      0.009728         0.000834   \n",
       "Dep_marketing        0.005715   -0.000311     -0.023064        -0.008210   \n",
       "Dep_product_mng      0.006919   -0.001989      0.000829        -0.005494   \n",
       "Dep_sales            0.004007   -0.023031     -0.013388        -0.001718   \n",
       "Dep_support          0.009185    0.017104      0.000303        -0.002444   \n",
       "Dep_technical       -0.009345    0.013742      0.028596         0.013638   \n",
       "Sal_high             0.029708   -0.020628     -0.008726        -0.007101   \n",
       "Sal_low             -0.047415    0.005221     -0.002504        -0.001050   \n",
       "Sal_medium           0.031367    0.006191      0.007377         0.005007   \n",
       "\n",
       "                   Tenure  Work_accident  Turnover  Promotion    Dep_IT  \\\n",
       "Satisfaction    -0.100866       0.058697 -0.388375   0.025605  0.006373   \n",
       "Evaluation       0.131591      -0.007104  0.006567  -0.008684  0.001269   \n",
       "ProjectCount     0.196786      -0.004741  0.023787  -0.006064  0.003287   \n",
       "AvgMonthlyHours  0.127755      -0.010143  0.071287  -0.003544  0.006967   \n",
       "Tenure           1.000000       0.002120  0.144822   0.067433 -0.006053   \n",
       "Work_accident    0.002120       1.000000 -0.154622   0.039245 -0.009293   \n",
       "Turnover         0.144822      -0.154622  1.000000  -0.061788 -0.010925   \n",
       "Promotion        0.067433       0.039245 -0.061788   1.000000 -0.038942   \n",
       "Dep_IT          -0.006053      -0.009293 -0.010925  -0.038942  1.000000   \n",
       "Dep_RandD       -0.021116       0.017167 -0.046596   0.021268 -0.070240   \n",
       "Dep_accounting   0.003909      -0.012836  0.015201  -0.004852 -0.069293   \n",
       "Dep_hr          -0.022194      -0.015649  0.028249  -0.001531 -0.067949   \n",
       "Dep_management   0.115436       0.011242 -0.046035   0.128087 -0.062500   \n",
       "Dep_marketing    0.012096       0.011367 -0.000859   0.049253 -0.073524   \n",
       "Dep_product_mng -0.003919       0.001246 -0.011029  -0.037288 -0.075503   \n",
       "Dep_sales        0.015150      -0.004955  0.009923   0.012353 -0.184302   \n",
       "Dep_support     -0.030111       0.012079  0.010700  -0.035605 -0.124705   \n",
       "Dep_technical   -0.027991      -0.006070  0.020076  -0.035799 -0.140484   \n",
       "Sal_high         0.039953       0.009040 -0.120929   0.076756 -0.016089   \n",
       "Sal_low         -0.040110      -0.006813  0.134722  -0.082832  0.005116   \n",
       "Sal_medium       0.018299       0.001856 -0.068833   0.040985  0.003775   \n",
       "\n",
       "                 Dep_RandD  ...    Dep_hr  Dep_management  Dep_marketing  \\\n",
       "Satisfaction      0.006615  ... -0.012841        0.007172       0.005715   \n",
       "Evaluation       -0.005471  ... -0.009645        0.009662      -0.000311   \n",
       "ProjectCount      0.009703  ... -0.027356        0.009728      -0.023064   \n",
       "AvgMonthlyHours  -0.001177  ... -0.010783        0.000834      -0.008210   \n",
       "Tenure           -0.021116  ... -0.022194        0.115436       0.012096   \n",
       "Work_accident     0.017167  ... -0.015649        0.011242       0.011367   \n",
       "Turnover         -0.046596  ...  0.028249       -0.046035      -0.000859   \n",
       "Promotion         0.021268  ... -0.001531        0.128087       0.049253   \n",
       "Dep_IT           -0.070240  ... -0.067949       -0.062500      -0.073524   \n",
       "Dep_RandD         1.000000  ... -0.053570       -0.049274      -0.057965   \n",
       "Dep_accounting   -0.054629  ... -0.052848       -0.048610      -0.057183   \n",
       "Dep_hr           -0.053570  ...  1.000000       -0.047667      -0.056075   \n",
       "Dep_management   -0.049274  ... -0.047667        1.000000      -0.051578   \n",
       "Dep_marketing    -0.057965  ... -0.056075       -0.051578       1.000000   \n",
       "Dep_product_mng  -0.059525  ... -0.057584       -0.052966      -0.062308   \n",
       "Dep_sales        -0.145300  ... -0.140562       -0.129289      -0.152093   \n",
       "Dep_support      -0.098315  ... -0.095109       -0.087482      -0.102911   \n",
       "Dep_technical    -0.110755  ... -0.107143       -0.098551      -0.115933   \n",
       "Sal_high         -0.015115  ... -0.017858        0.209077       0.009642   \n",
       "Sal_low          -0.011887  ... -0.015690       -0.084641      -0.009479   \n",
       "Sal_medium        0.020402  ...  0.025765       -0.030716       0.004213   \n",
       "\n",
       "                 Dep_product_mng  Dep_sales  Dep_support  Dep_technical  \\\n",
       "Satisfaction            0.006919   0.004007     0.009185      -0.009345   \n",
       "Evaluation             -0.001989  -0.023031     0.017104       0.013742   \n",
       "ProjectCount            0.000829  -0.013388     0.000303       0.028596   \n",
       "AvgMonthlyHours        -0.005494  -0.001718    -0.002444       0.013638   \n",
       "Tenure                 -0.003919   0.015150    -0.030111      -0.027991   \n",
       "Work_accident           0.001246  -0.004955     0.012079      -0.006070   \n",
       "Turnover               -0.011029   0.009923     0.010700       0.020076   \n",
       "Promotion              -0.037288   0.012353    -0.035605      -0.035799   \n",
       "Dep_IT                 -0.075503  -0.184302    -0.124705      -0.140484   \n",
       "Dep_RandD              -0.059525  -0.145300    -0.098315      -0.110755   \n",
       "Dep_accounting         -0.058723  -0.143341    -0.096989      -0.109262   \n",
       "Dep_hr                 -0.057584  -0.140562    -0.095109      -0.107143   \n",
       "Dep_management         -0.052966  -0.129289    -0.087482      -0.098551   \n",
       "Dep_marketing          -0.062308  -0.152093    -0.102911      -0.115933   \n",
       "Dep_product_mng         1.000000  -0.156187    -0.105682      -0.119054   \n",
       "Dep_sales              -0.156187   1.000000    -0.257967      -0.290608   \n",
       "Dep_support            -0.105682  -0.257967     1.000000      -0.196636   \n",
       "Dep_technical          -0.119054  -0.290608    -0.196636       1.000000   \n",
       "Sal_high               -0.006514  -0.039273    -0.029184      -0.014672   \n",
       "Sal_low                 0.006191   0.023766     0.022038       0.015674   \n",
       "Sal_medium             -0.002632  -0.002174    -0.006035      -0.007673   \n",
       "\n",
       "                 Sal_high   Sal_low  Sal_medium  \n",
       "Satisfaction     0.029708 -0.047415    0.031367  \n",
       "Evaluation      -0.020628  0.005221    0.006191  \n",
       "ProjectCount    -0.008726 -0.002504    0.007377  \n",
       "AvgMonthlyHours -0.007101 -0.001050    0.005007  \n",
       "Tenure           0.039953 -0.040110    0.018299  \n",
       "Work_accident    0.009040 -0.006813    0.001856  \n",
       "Turnover        -0.120929  0.134722   -0.068833  \n",
       "Promotion        0.076756 -0.082832    0.040985  \n",
       "Dep_IT          -0.016089  0.005116    0.003775  \n",
       "Dep_RandD       -0.015115 -0.011887    0.020402  \n",
       "Dep_accounting   0.011821 -0.009759    0.003285  \n",
       "Dep_hr          -0.017858 -0.015690    0.025765  \n",
       "Dep_management   0.209077 -0.084641   -0.030716  \n",
       "Dep_marketing    0.009642 -0.009479    0.004213  \n",
       "Dep_product_mng -0.006514  0.006191   -0.002632  \n",
       "Dep_sales       -0.039273  0.023766   -0.002174  \n",
       "Dep_support     -0.029184  0.022038   -0.006035  \n",
       "Dep_technical   -0.014672  0.015674   -0.007673  \n",
       "Sal_high         1.000000 -0.292560   -0.260274  \n",
       "Sal_low         -0.292560  1.000000   -0.847144  \n",
       "Sal_medium      -0.260274 -0.847144    1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recheck correlations to confirm these dummy variables are working\n",
    "corr = df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ff8ebe",
   "metadata": {},
   "source": [
    "# Data Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0174dcad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score,classification_report, precision_score, recall_score, confusion_matrix,precision_recall_curve\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d92b641b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Turnover']\n",
    "X = df.drop('Turnover',axis = 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.15 , random_state = 123, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4c2a928a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.79\n",
      "[[1580  134]\n",
      " [ 343  193]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rajeski/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:763: 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",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression(penalty ='l2', C = 1)\n",
    "model.fit(X_train,y_train)\n",
    "print('Accuracy',round(accuracy_score(y_test, model.predict(X_test)),2))\n",
    "print(confusion_matrix(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8e1abc95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Decision Tree Model\n",
      "Decision Tree Accuracy = 0.944\n",
      "Decision Tree AUC = 0.93\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.96      1714\n",
      "           1       0.88      0.89      0.88       536\n",
      "\n",
      "    accuracy                           0.94      2250\n",
      "   macro avg       0.92      0.93      0.92      2250\n",
      "weighted avg       0.94      0.94      0.94      2250\n",
      "\n",
      "[[1646   68]\n",
      " [  58  478]]\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Classifier\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = tree.DecisionTreeClassifier(max_depth=3, min_weight_fraction_leaf=0.01)\n",
    "dtree = dtree.fit(X_train,y_train)\n",
    "print ('\\n\\n Decision Tree Model')\n",
    "dt_accuracy = accuracy_score(y_test, dtree.predict(X_test))\n",
    "print('Decision Tree Accuracy =' ,dt_accuracy)\n",
    "dt_roc_auc = roc_auc_score(y_test, dtree.predict(X_test))\n",
    "print ('Decision Tree AUC = %2.2f' % dt_roc_auc)\n",
    "print(classification_report(y_test, dtree.predict(X_test)))\n",
    "print(confusion_matrix(y_test, dtree.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cd27d504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Random Forest Model\n",
      "Accuracy = 0.9862222222222222\n",
      "Random Forest AUC = 0.98\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99      1714\n",
      "           1       0.99      0.96      0.97       536\n",
      "\n",
      "    accuracy                           0.99      2250\n",
      "   macro avg       0.99      0.98      0.98      2250\n",
      "weighted avg       0.99      0.99      0.99      2250\n",
      "\n",
      "[[1707    7]\n",
      " [  24  512]]\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(\n",
    "    n_estimators=1000, \n",
    "    max_depth=None, \n",
    "    min_samples_split=10, \n",
    "    class_weight='balanced'\n",
    "    )\n",
    "rf.fit(X_train, y_train)\n",
    "print ('\\n\\n Random Forest Model')\n",
    "print('Accuracy =', accuracy_score(y_test, rf.predict(X_test)))\n",
    "rf_roc_auc = roc_auc_score(y_test, rf.predict(X_test))\n",
    "print ('Random Forest AUC = %2.2f' % rf_roc_auc)\n",
    "print(classification_report(y_test, rf.predict(X_test)))\n",
    "print(confusion_matrix(y_test, rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3e0918ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " AdaBoost Model\n",
      "Accuracy = 0.9506666666666667\n",
      "AdaBoost AUC = 0.93\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.97      0.97      1714\n",
      "           1       0.91      0.88      0.89       536\n",
      "\n",
      "    accuracy                           0.95      2250\n",
      "   macro avg       0.94      0.93      0.93      2250\n",
      "weighted avg       0.95      0.95      0.95      2250\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1667,   47],\n",
       "       [  64,  472]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ada Boost Classifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada = AdaBoostClassifier(n_estimators=400, learning_rate=0.1)\n",
    "ada.fit(X_train,y_train)\n",
    "print ('\\n\\n AdaBoost Model')\n",
    "print('Accuracy =', accuracy_score(y_test,ada.predict(X_test)))\n",
    "ada_roc_auc = roc_auc_score(y_test, ada.predict(X_test))\n",
    "print ('AdaBoost AUC = %2.2f' % ada_roc_auc)\n",
    "print(classification_report(y_test, ada.predict(X_test)))\n",
    "confusion_matrix(y_test, ada.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "11c73da4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Accuracy Comparison *\n",
      "Accuracy of AdaBoost Model 0.9506666666666667\n",
      "Accuracy of Decision Tree Model 0.944\n",
      "Accuracy of Logistic Regression Model 0.788\n",
      "Accuracy of Random Forest Model 0.9862222222222222\n"
     ]
    }
   ],
   "source": [
    "print('* Accuracy Comparison *')\n",
    "print('Accuracy of AdaBoost Model', accuracy_score(y_test,ada.predict(X_test)))\n",
    "print('Accuracy of Decision Tree Model', accuracy_score(y_test,dtree.predict(X_test)))\n",
    "print('Accuracy of Logistic Regression Model', accuracy_score(y_test,model.predict(X_test)))\n",
    "print('Accuracy of Random Forest Model', accuracy_score(y_test,rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ad8c9ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eTWN57NlnrRciBP+yhEeOTeOSyJZMvXqqBglVIhokVEXlh1dMOT+daSHSEhJIWvg5APW++pw7V4ylckhlZvSdQdWwC5u4XlUszmMR+syDqqj8L1CIQJNuboscGPZXAC567lnW8DvHUo4xe8BsLqpyUVnUUPkpVwPUzre66t1MqqLyv0BRhMwDBzBZWYD1AN3OuNcJCwqjc73Ovq2YKpdctRicB6g1OCgVYIHC5Oby+wBr3od6f/s/JCiInad20iqyFaFBoT6unSpPXD0cp0FBKdcCKlBkHbSeqA6pW5dao0djjGHHqR30a9rPxzVTvlRUl5IGB6XcC6hAYXJzAaj35JOICEeTj3I28yztarXzcc2UL9mfmI5qcD4flwYIpTwXUIEifetW60VONgA7TlkP2rWt3dZXVVI+ZG9J6BPTSl2YgAoUBAUDEB4VBcDOUzsJkiBaR7b2Za1UGSpscFqfmFaq5AIrUNgfxLPNYb0zcSfNqjcjIiTCh5VSZSV/ag3tXlKqdARUoEhZvw4ACQsDrK6ny+tf7m4TFSD0qWmlvMf/Uni4EVzdmicirFFDTqef5s/UP3UguwLQIKGUdwVUoACQCKubSQeyKwYNEkp5X0AFivQtWxARwBrIBrRFEcA0SChVNgJmjCL75ElSN24ktIl1sdiZuJMGVRrotKUBKP9T1RoklPKugAkUmbansiPvuhOwup7a1tJuJ3+nT1Ur5XsBEyiyj58AIKxRI1KzUjlw9gDXN7/ex7VSxVFUULDTAKFU2QqYQIFtbCK0cRN2nt6NwWiLwg9o9lalyj+vBgoRuQ54EwgG3jPGTMn3fg3gv0ATW12mGmPmlORYOWeTHK/tdzy1q60D2eVRYcFBg4JS5ZPXAoWIBAMzgH7AYWCjiCw1xmx3KvYwsN0YM1hE6gK7RGS+MSazuMdL+vwLAIKqVGbnwZ3UrFST+pXrX/gHUaVKn55Wyv94s0XRBfjNGLMXQEQWADcCzoHCANXEuqe1KnAKyC7JwYKqVSOoenXCGjVixyZrINt+q6zynfzjDnqnklL+x5uBoiFwyGn5MNA1X5npwFLgKFANGGqMyc2/IxG5H7gf4LIGwYUeMKxZM7Jys/jtzG/8td1fL6z2qsTcjTtoC0Ip/+PNQOHq67zJtzwAiAeuAS4BvhORn4wxZ/NsZMwsYBZATMPQ/PvIY++ZvWTlZulAdhnReaaVCnzeDBSHgcZOy42wWg7ORgFTjDEG+E1E9gFtgQ3FOVBuWhopP/1Epah2mrqjDOidSkpVLN4MFBuBViLSHDgC3AHcla/MQaAv8JOI1AfaAHvd77ZggyLnrNUACW/Vip2ndhIREkHTak0vsPqqMM6TAWlQUCrweS1QGGOyRWQssBzr9tjZxphtIjLG9v5M4AVgrohsweqqetIYc7KIHUN2hsu3Ii6/nB2JX9E6sjXBQYWPZajic25F6IxxSlUsXn2OwhizDFiWb91Mp9dHgf7F3nFN199ejTHsOr2LG1rcUOxdqsLlv6U1qkF1nTFOqQrEP5/MrnZRnsWzX34FwOmM06SYFM0YW4o0Q6tSKiDSjKfGxgKwv6k1F4UOZJcee3eTBgmlKi7/bFHkJ0Kldu3YWu0MIRJCq5qtfF0jv5Z/PKJr81oaJJSqwAIiUGTu24dUqsSOUztoUbMFYcFhvq6SX8o/z4OORyilwF8DhTl/i2xOcgqZ+/YR0qABOxNP07NhTx9WzH/lH7DWW16VUnb+GShqNHK8NJnWrbKhgweQmP5fHcguJp0tTilVFP8MFFXqFlh1vFI6gKbu8JCrbiZtRSilXPHPQOEkNS4OgBOnj0BVDRTuFJZ6QwOEUsodjwOFiFQxxqR4szIlkbHDyu20qWkujas0pmpYVR/XqHzSeSCUUiVVZKAQkR7Ae1jzRTQRkU7AA8aYh7xdOU+kbU4AYF3YYdrWivJxbconfWhOKXUhPGlR/BsrHfhSAGPMZhG52qu1KoagqlYL4kD6EW6sdauPa1O+6EC1Uqo0eNT1ZIw5lG+2uBzvVKf4zi1fTm7ji4CTOj6BjkMopUqfJ4HikK37yYhIGDAe2OHdankmJ9kaMslJSgKgXW29NVZTgCulSpsngWIM8CbW1KaHgW+BcjE+gW3W1M3XtaROxAnqRNTxcYV8x96S0BTgSqnS5kmgaGOMGea8QkR6Amu8U6Xi+zP1zwo3kJ1/CtL83UxKKVVaPAkUbwGXebDOZxLTEyvcE9nOrQfQcQillPcUGihEpDvQA6grIo85vVUda8Y6nzKZmRx+eKz12pgKM5CtXUxKqbLmrkURhvXsRAhQzWn9WeA2b1aqKMYY9t99N+m2ZyhiWwoPV4AWhavEfUop5W2FBgpjzI/AjyIy1xhzoAzrVLTcXEeQ+PLNW0hJXEHDaoF90dSH5pRSvuLJGEWqiLwGtAfC7SuNMdd4rVZFyDx8DIDa995DfM4m2tRqQ5AExGR9BehDc0opX/MkUMwHPgFuwLpVdgRwwpuVKkrO2XMAhEVFsef0p9zaKvCeyNbsrkqp8sKTQFHbGPO+iDzi1B31o7cr5k7i/M8BOCnJpGWnBdxAtk4ipJQqTzwJFFm238dEZBBwFGjkprzXpW3fBcCuVhFwPLBSi+tYhFKqvPEkULwoIjWAx7Gen6gOPOrNShUltE5tgipXZeeZ3YQFhdGiZgtfVqfUaJBQSpVHRQYKY8yXtpdJQB9wPJntOyKEtWjOjlM7aBnZktCgUJ9WpzRokFBKlVeF3iokIsEicqeI/J+IdLCtu0FEfgGml1kNC2Ng56mdAfFEtgYJpVR55q5F8T7QGNgATBORA0B3YKIxZnEZ1M2tjJwMkjKS/Hp8Qm99VUr5A3eBIgaINsbkikg4cBJoaYz5o2yqVggD6Tv3kN6lPeC/A9l6Z5NSyl+4CxSZxlh5vI0x6SKy2+dBAjC51gRKScGZCELryNY+rlHJ2DO/aitCKVXeuQsUbUUkwfZagEtsywIYY0y012vnxo7OtbioSjqVQyv7shol8tH6g6zfd4quzWtpkFBKlXvuAkW5HiXOys2kcoj/BQk435rQpH5KKX/gLilg+UoEmE9GbibhIeFFFyxHnFOEa2tCKeUvvJpJT0SuE5FdIvKbiEwspExvEYkXkW3FSQ2SlZPld4HCeR4JbU0opfyFJ09ml4iIBAMzgH5Yc21vFJGlxpjtTmVqAm8D1xljDopIPU/3n5mTSXiwfwUKQCcbUkr5HY9aFCISISJtirnvLsBvxpi9xphMYAFwY74ydwFfGGMOAhhjjnu686xc/2tRKKWUPyoyUIjIYCAe+Ma23FlElnqw74bAIaflw7Z1zloDkSKySkTiRGS4R7UGsnL8a4zCfqeTUkr5G09aFJOwWgdnAIwx8UAzD7YTF+tMvuUQ4HJgEDAAeEZECjwYISL3i0isiMTa12XmZvlN15Pzw3U6NqGU8jeeBIpsY0xSCfZ9GCsFiF0jrBTl+ct8Y4xJMcacBFYDnfLvyBgzyxgTY4yJsa/L8qO7nvThOqWUP/MkUGwVkbuAYBFpJSJvAb94sN1GoJWINBeRMOAOIH+X1RLgKhEJEZHKQFdghycVz8zxnxYFoLfDKqX8lieBYhzWfNkZwEdY6cYfLWojY0w2MBZYjnXx/9QYs01ExojIGFuZHVhjHwlYyQffM8Zs9aTiOSan3LcoPlp/kKHvrmX7sbO+ropSSpWYJ7fHtjHGPA08XdydG2OWAcvyrZuZb/k14LXi7hsgIiSiJJuVCVdJ/5RSyh95EiheF5EGwGfAAmPMNi/XyWPluetJxyWUUoGiyK4nY0wfoDdwApglIltE5B/erpgnKoVU8nUV3NJxCaVUIPDogTtjzB/GmGnAGKxnKp71ZqU8Vd7HKJRSKhB48sBdOxGZJCJbsaZA/QXrVlefiwguv2MUSikVKDwZo5gDfAz0N8bkfw7Cp8pji8I5Q2xUg+q+ro5SSl2wIgOFMaZbWVTEU7k51gPfAlQKLn9jFJohVikVaAoNFCLyqTHmdhHZQt7UG76d4c5Wk9NVpNzeHqsZYpVSgcRdi+IR2+8byqIixXW6WvnqetIuJ6VUoCp0MNsYc8z28iFjzAHnH+Chsqmee+XpOQrtclJKBSpPbo/t52LdwNKuSEmUpxYFnO9y0mcnlFKBpNBAISIP2sYn2ohIgtPPPqzcTD5XXgKFzjWhlApk7sYoPgK+BiYDzvNdnzPG+PyqKAhhQWG+rgZwPl2HdjkppQKRu0BhjDH7ReTh/G+ISC1fB4tKwZUQcTU3UtmytyY0XYdSKlAV1aK4AYjDuinV+apsgBZerFehjK0a5eUZCm1NKKUCXaGBwhhzg+1387KrTtFMtu2Bu4jyMT4BmvxPKRXYPMn11FNEqthe/1VEXhcR310VxXriLqdGFZ9VwU4HsZVSFYEnt8e+A6SKSCfgCeAAMM+rtSpCatUQnz9D4TwxkXY7KaUCmSeBItsYY4AbgTeNMW8C1bxbLTcMGPHt7HbOQUInJlJKBTpPsseeE5G/A3cDV4lIMBDq3Wq5l4tvB7N19jqlVEXiSYtiKJABjDbG/AE0pIRzXJcWI8ZnD9vp7bBKqYrGk6lQ/wDmAzVE5AYg3Rjzoddr5kYuvnsqW2+HVUpVNJ7c9XQ7sAH4C3A7sF5EbvN2xdzUiFwxPhmj0NaEUqoi8mSM4mngCmPMcQARqQt8Dyz0ZsXcycX4ZIxCWxNKqYrIkzGKIHuQsEn0cDuvyRXfdT1pa0IpVdF40qL4RkSWY82bDdbg9jLvVck9g9WiiAgun7PbKaVUoPFkMPtvwLtANNAJmGWMedLbFXNbJx+0KPQpbKVUReVuzuxWwFTgEmAL8H/GmCNlVbHCGKxAUdZjFDo+oZSqqNy1KGYDXwK3YmWQfatMauQBg2+ezNbxCaVUReQuUFQzxvzHGLPLGDMVaFZGdXLL3qIoy64n7XZSSlVk7gazw0XkUs7PQxHhvGyM+dXblXPJ2O56KsOkgNrtpJSqyNwFimPA607LfzgtG+Aab1XKHQMgUCmkbMcotNtJKVVRuZu4qE9ZVqQ4cn2cPVYppSoSnz44VxKOMQofz0ehlFIVhVcDhYhcJyK7ROQ3EZnoptwVIpLjWQ4pwVB2g9k6kK2Uqui8Fihs81bMAAYCUcCdIhJVSLlXgOWe7NfeoiirricdyFZKVXSeZI8V21zZz9qWm4hIFw/23QX4zRiz1xiTCSzAmiUvv3HA58BxF++5VNYP3OlAtlKqIvOkRfE20B2407Z8DqulUJSGwCGn5cO2dQ4i0hC4GZjpbkcicr+IxIpIrIEy63rSbiellPIsUHQ1xjwMpAMYY04DYR5sJy7WmXzLbwBPGmNy3O3IGDPLGBNjjImBsmtRaLeTUkp5lj02yzaOYD3CYM1HkevBdoeBxk7LjYCj+crEAAtEBKAOcL2IZBtjFrvdc1AQQVI2N2xpt5NSqqLzJFBMAxYB9UTkJeA24B8ebLcRaCUizYEjwB3AXc4FjDHN7a9FZC7wZVFBwlhlPTi8Ukqp0lBkoDDGzBeROKAvVnfSTcaYHR5sly0iY7HuZgoGZhtjtonIGNv7bscl3JEg77cmnKc9VUqpiqzIQCEiTYBU4H/O64wxB4va1hizjHyTHBUWIIwxI4va3/njez9Q6PiEUkpZPOl6+gpbjw8QDjQHdgHtvVgvt4K83KJwbk3o+IRSqqLzpOupo/OyiFwGPOC1GhXBAHg5UGhrQimlziv2FdeWXvwKL9TFwwpQJnc8aWtCKaUsnoxRPOa0GARcBpzwWo2KIt4NFDqIrZRSeXkyRlHN6XU21pjF596pTtEM3g0U2u2klFJ5uQ0Utgftqhpj/lZG9fGAEOSl5yh0EFsppQoq9Ku5iITYUmtcVob1KVKlTAg23mlRaGtCKaUKctei2IAVJOJFZCnwGZBif9MY84WX6+aSEQh2mUaqdGhrQiml8vJkjKIWkIg1R7b9eQoD+CRQACS3qOurQyulVIXjLlDUs93xtJXzAcIufxbYMhVcRgkBVfmTlZXF4cOHSU9P93VVlCqXwsPDadSoEaGhoaW2T3eBIhioimfpwsuMGAgJ8qQhpALR4cOHqVatGs2aNdPkkErlY4whMTGRw4cP07x586I38JC7K+4xY8w/S+1IpSg029c1UL6Snp6uQUKpQogItWvX5sSJ0n3UzV0fTrn9n5h63eW+roLyIQ0SShXOG/8/3AWKvqV+tFISVobzZSulVEVXaKAwxpTbyaIrBXkyE6tS3lG1atUL3kdsbCzjx48v9P39+/fz0UcfeVw+v969e9OmTRs6derEFVdcQXx8/IVUt1QtXbqUKVOmlMq+0tLS6NWrFzk5bmdT9qnJkyfTsmVL2rRpw/Llywst99Zbb9GmTRvat2/PE088kee9gwcPUrVqVaZOnepYd+2113L69Gmv1duZX44KVwouvdF8O83xpMpSTEwMMTExhb5vDxR33XWXR+VdmT9/PjExMcyZM4e//e1vfPfddxdUZ4CcnByCg4MvaB9DhgxhyJAhF1wXgNmzZ3PLLbd4XCdjDMYYr09VYLd9+3YWLFjAtm3bOHr0KNdeey27d+8uUN+VK1eyZMkSEhISqFSpEsePH8/z/oQJExg4cGCedXfffTdvv/02Tz/9tNc/h38GCi+0KPSpbP/z/P+2sf3o2VLdZ9TF1XlucPGnWomPj2fMmDGkpqZyySWXMHv2bCIjI9m4cSP33HMPVapU4corr+Trr79m69atrFq1iqlTp/Lll1/y448/8sgjjwBW//Lq1auZOHEiO3bsoHPnzowYMYJLL73UUT45OZlx48YRGxuLiPDcc89x6623Flq37t2789prrwGQkpLCuHHj2LJlC9nZ2UyaNIkbb7yR1NRURo4cyc6dO2nXrh379+9nxowZxMTEULVqVR577DGWL1/Ov/71L/bv38+0adPIzMyka9euvP322wDcc889jjqNHj2aCRMmMG3aNGbOnElISAhRUVEsWLCAuXPnEhsby/Tp0zlw4ACjR4/mxIkT1K1blzlz5tCkSRNGjhxJ9erViY2N5Y8//uDVV1/ltttuK/DZ5s+f72h5JScnc+ONN3L69GmysrJ48cUXufHGG9m/fz8DBw6kT58+rF27lsWLF/Ppp5/y6aefkpGRwc0338zzzz8PwE033cShQ4dIT0/nkUce4f777y/234KzJUuWcMcdd1CpUiWaN29Oy5Yt2bBhA927d89T7p133mHixIlUqmR1q9erV8/x3uLFi2nRogVVqlTJs82QIUO46qqryiRQ+OUDCZWCvdP1pE9lq5IaPnw4r7zyCgkJCXTs2NFx4Rk1ahQzZ85k7dq1hX7rnTp1KjNmzCA+Pp6ffvqJiIgIpkyZwlVXXUV8fDwTJkzIU/6FF16gRo0abNmyhYSEBK655hq3dfvmm2+46aabAHjppZe45ppr2LhxIytXruRvf/sbKSkpvP3220RGRpKQkMAzzzxDXFycY/uUlBQ6dOjA+vXrqV27Np988glr1qwhPj6e4OBg5s+fT3x8PEeOHGHr1q1s2bKFUaNGATBlyhQ2bdpEQkICM2cWnNxy7NixDB8+nISEBIYNG5ane+3YsWP8/PPPfPnll0ycOLHAtpmZmezdu5dmzZoB1vMDixYt4tdff2XlypU8/vjjGGPdyb9r1y6GDx/Opk2b2LVrF3v27GHDhg3Ex8cTFxfH6tWrAauFEhcXR2xsLNOmTSMxMbHAcSdMmEDnzp0L/LjqTjty5AiNGzd2LDdq1IgjR44UKLd7925++uknunbtSq9evdi4caPj3L/yyis899xzBbaJjIwkIyPDZR1Lm1+2KMKCSr/rSfmfknzz94akpCTOnDlDr169ABgxYgR/+ctfOHPmDOfOnaNHjx4A3HXXXXz55ZcFtu/ZsyePPfYYw4YN45ZbbqFRo0Zuj/f999+zYMECx3JkZKTLcsOGDSMlJYWcnBx+/fVXAL799luWLl3q6OtOT0/n4MGD/Pzzz45WTYcOHYiOjnbsJzg42NFi+eGHH4iLi+OKK6wpadLS0qhXrx6DBw9m7969jBs3jkGDBtG/f38AoqOjGTZsGDfddJMjWDlbu3YtX3xhJXm4++678/TN33TTTQQFBREVFcWff/5ZYNuTJ09Ss2ZNx7IxhqeeeorVq1cTFBTEkSNHHNs1bdqUbt26Oc7Bt99+y6WXXgpYLZE9e/Zw9dVXM23aNBYtWgTAoUOH2LNnD7Vr185z3H//+98uz7cr9kDlzNVdSdnZ2Zw+fZp169axceNGbr/9dvbu3ctzzz3HhAkTCh0Xq1evHkePHi1Qx9Lml4GitFsUOj6hvMHVRcKViRMnMmjQIJYtW0a3bt34/vvvi9yvJ7dAzp8/n06dOjFx4kQefvhhvvjiC4wxfP7557Rp08bjuoaHhztaQ8YYRowYweTJkwuU27x5M8uXL2fGjBl8+umnzJ49m6+++orVq1ezdOlSXnjhBbZt2+a2zs6fy94NU1j9IiIi8jyhP3/+fE6cOEFcXByhoaE0a9bM8b5zt40xhr///e888EDeiTpXrVrF999/z9q1a6lcuTK9e/d2mQFgwoQJrFy5ssD6O+64o0DLp1GjRhw6dMixfPjwYS6++OIC2zZq1IhbbrkFEaFLly4EBQVx8uRJ1q9fz8KFC3niiSc4c+YMQUFBhIeHM3bsWMAK9BEREQX2V9r8suupco2apbo/HZ9QF6JGjRpERkby008/ATBv3jx69epFZGQk1apVY926dQB5WgHOfv/9dzp27MiTTz5JTEwMO3fupFq1apw7d85l+f79+zN9+nTHsrs7X0JDQ3nxxRdZt24dO3bsYMCAAbz11luOC++mTZsAuPLKK/n0008BawB2y5YtLvfXt29fFi5c6BhsPXXqFAcOHODkyZPk5uZy66238sILL/Drr7+Sm5vLoUOH6NOnD6+++ipnzpwhOTk5z/569OjhOC/z58/nyiuvLPSz5BcZGUlOTo7jYp6UlES9evUIDQ1l5cqVHDhwwOV2AwYMYPbs2Y66HDlyhOPHj5OUlERkZCSVK1dm586djn+3/P79738THx9f4MdV99iQIUNYsGABGRkZ7Nu3jz179tClS5cC5W666SZWrFgBWN1QmZmZ1KlTh59++on9+/ezf/9+Hn30UZ566ilHkDDG8Mcffzi63rzJ71oUBqhcu/STAur4hPJUampqnu6hxx57jA8++MAxmN2iRQvmzJkDwPvvv899991HlSpV6N27NzVq1CiwvzfeeIOVK1cSHBxMVFQUAwcOJCgoiJCQEDp16sTIkSMd3SQA//jHP3j44Yfp0KEDwcHBPPfcc9xyyy2F1jciIoLHH3+cqVOnMn36dB599FGio6MxxtCsWTO+/PJLHnroIUaMGEF0dDSXXnop0dHRLusaFRXFiy++SP/+/cnNzSU0NJQZM2YQERHBqFGjyM3NBaxbQnNycvjrX/9KUlISxhgmTJiQp6sIYNq0aYwePZrXXnvNMZhdHP379+fnn3/m2muvZdiwYQwePJiYmBg6d+5M27ZtC91mx44djgHlqlWr8t///pfrrruOmTNnEh0dTZs2bRxdVReiffv23H777URFRRESEsKMGTMcrbN7772XMWPGEBMTw+jRoxk9ejQdOnQgLCyMDz74oMhWY1xcHN26dSMkxPuXcfG0eVxeRFWOMNsPbYXal5TaPoe+uxaATx7oXkRJ5Ws7duygXbt2vq6Gx5KTkx39y1OmTOHYsWO8+eabPq5VQTk5OWRlZREeHs7vv/9O37592b17N2Fh5fuZpU2bNvH6668zb948X1elzD3yyCMMGTKEvn0LPhvt6v+JiMQZY4p3j7WN37UolPInX331FZMnTyY7O5umTZsyd+5cX1fJpdTUVPr06UNWVhbGGN55551yHyQALr30Uvr06VMqz3f4mw4dOrgMEt5Q4VsUH60/yFOLttC1eS1tUfgBf2tRKOULpd2i8MvB7NKkA9lKKeVehQ4UzrfF6kC2Ukq5VqEDhbYmlFKqaBU2UGhrQimlPFPhAsVH6w8y9N21PLXIeqBIWxOquIKDg+ncuTMdOnRg8ODBnDlzplT2O3fuXMfDVKXJnnLcnpNo4cKFpX4MKJgaPb9jx45xww03eOXYpcEYw/jx42nZsiXR0dGOtCf5rVixgssuu4wOHTowYsQIsrOtKTeTkpIYPHgwnTp1on379o5nQjIzM7n66qsd5fxRhQoU9juc7C2Jl2/uqK0JVWwRERHEx8ezdetWatWqxYwZM3xdpSLZE/fFx8e7zMLqSnEvbEUFitdff5377rvP4/2V9RwTX3/9NXv27GHPnj3MmjWLBx98sECZ3NxcRowYwYIFC9i6dStNmzblgw8+AGDGjBlERUWxefNmVq1axeOPP05mZiZhYWH07duXTz75pEw/T2mqMM9R2IMEoAEiUHw9Ef5wnWqixC7qCAM9n1Sne/fuJCQkALBhwwYeffRR0tLSiIiIYM6cObRp04a5c+eydOlSUlNT+f3337n55pt59dVXAZgzZw6TJ0+mQYMGtG7d2pHfyF367YiICHbu3MmBAweYM2cOH3zwAWvXrqVr164eP6dx6tQpRo8ezd69e6lcuTKzZs0iOjqaSZMmcfToUfbv30+dOnV48803GTNmDAcPHgSsp8h79uzpUWr0/FlvP//8c1588UXACip33303KSkpAEyfPp0ePXqwatUqnn/+eRo0aEB8fDxbtmxh4sSJrFq1ioyMDB5++GEeeOCBQlOKX4glS5YwfPhwRIRu3bpx5swZjh07RoMGDRxlEhMTqVSpEq1btwagX79+TJ48mXvuuQcR4dy5cxhjSE5OplatWo6npm+66Sb+/ve/M2zYsAuqo694NVCIyHXAm0Aw8J4xZkq+94cBT9oWk4EHjTGbvVEX+8C1BglVWnJycvjhhx+45557AGjbti2rV68mJCSE77//nqeeeorPP/8csOar2LRpE5UqVaJNmzaMGzeOkJAQnnvuOeLi4qhRowZ9+vRxpOqwp98eMWIEs2fPZvz48SxevBiwcjutWLGCpUuXMnjwYNasWcN7773nmMmuc+fOBeo6bNgwR/K4H374gUmTJnHppZeyePFiVqxYwfDhwx2z4MXFxfHzzz8TERHBXXfdxYQJE7jyyis5ePAgAwYMYMeOHY7U6D179iQ5OZnw8HCmTJnimDMjv3379hEZGZlnvoXvvvuO8PBw9uzZw5133klsbCxgBdytW7fSvHlzZs2aRY0aNdi4cSMZGRn07NmT/v3707hxYxYtWkT16tU5efIk3bp1Y8iQIQXSXgwdOpRdu3YVqM9jjz3G8OHD86wrLCW4c6CoU6cOWVlZxMbGEhMTw8KFCx1J/8aOHcuQIUO4+OKLOXfuHJ988oljgqQOHTo4Uof7I68FChEJBmYA/YDDwEYRWWqM2e5UbB/QyxhzWkQGArOArqVdFx24DlDF+OZfmtLS0ujcuTP79+/n8ssvp1+/foDVRz1ixAj27NmDiJCVleXYpm/fvo7cSVFRUY5Eer1796ZuXSt32dChQ9m9ezfgPv324MGDERE6duxI/fr16dixI2DlFdq/f7/LQGGf7c7u559/dgSxa665hsTERJKSkgArkZ09qHz//fds337+v+zZs2c5d+5csVOjHzt2zPE5AbKyshg7dqxjTgv75wbo0qULzZs3B6yU4AkJCY5xlaSkJPbs2UOjRo1cphS/6KKL8hy3ON09nqQEFxEWLFjAhAkTyMjIoH///o5Ww/Lly+ncuTMrVqzg999/p1+/flx11VVUr16d4OBgwsLCOHfuHNWqVfO4TuWFN1sUXYDfjDF7AURkAXAj4PirM8b84lR+HeD+r62E9DZYVZrsYxRJSUnccMMNzJgxg/Hjx/PMM8/Qp08fFi1axP79++ndu7djG+eU2cHBwY7+f0/ShecvZ99XUFBQnv0GBQV5PK7g7qLonJI7NzeXtWvXFkhlXdzU6PlTgv/73/+mfv36bN68mdzcXMLDwx3v5U8J/tZbbzFgwIA8+5s7d26hKcWdFadF4WlK8O7duzsyBX/77beOIDdnzhwmTpyIiNCyZUuaN2/Ozp07HdliMzIy8nxOf+LNweyGwCGn5cO2dYW5B/ja1Rsicr+IxIpIbEkro60JVdpq1KjBtGnTmDp1KllZWSQlJdGwofUn7slYQdeuXVm1ahWJiYlkZWXx2WefOd67kPTbnrj66quZP38+YM3DUKdOHapXr16gXP6U5vbuqeKmRm/dujX79+93LCclJdGgQQOCgoKYN29eoQPXAwYM4J133nG0znbv3k1KSorHKcU/+eQTlynB8wcJsFpSH374IcYY1q1bR40aNfJ0O9nZU6xnZGTwyiuvMGbMGACaNGnCDz/8AMCff/7Jrl27aNGiBWCNbdStW5fQUP+cdM2bgcLVVyWXiaVEpA9WoHjS1fvGmFnGmJiS5ilRylsuvfRSOnXqxIIFC3jiiSf4+9//Ts+ePT26Y6dBgwZMmjSJ7t27c+2113LZZZc53ps2bRpz5swhOjqaefPmlXrG2UmTJhEbG0t0dDQTJ0503LmT37Rp0xzloqKiHNOZvvHGG3To0IFOnToRERHBwIEDiY6OdqRGzz8LXJUqVbjkkkv47bffAHjooYf44IMP6NatG7t37y4wH7TdvffeS1RUlON21AceeIDs7GyGDRvmGCeYP39+oSnFi+P666+nRYsWtGzZkvvuu88xF7j9vaNHjwLw2muv0a5dO6Kjoxk8eLBjKtpnnnmGX375hY4dO9K3b19eeeUV6tSpA8DKlSu5/vrrL7iOvuK1pIAi0h2YZIwZYFv+O4AxZnK+ctHAImCgMWZ3gR3lU5KkgJpGPHBoUkD/tWjRIuLi4hx3PlUkt9xyC5MnTy4ws6C3+FOa8Y1AKxFpDhwB7gDuci4gIk2AL4C7PQkSSin/dfPNN5OYmOjrapS5zMxMbrrppjILEt7gta4nY0w2MBZYDuwAPjXGbBORMSIyxlbsWaA28LaIxF/IGERh7Hc8KaV879577/V1FcpcWFiYyzERf+LV5yiMMcuAZfnWzXR6fS/g1b8cveNJKaUuTECn8NDnJ5RS6sIFdKDQ1oRSSl24gA4UoM9PKKXUhQr4QKFUabOnGW/fvj2dOnXi9ddfJzc3t0T7evbZZ90+1Txz5kw+/PDDklYVgC1btjhSjNeqVYvmzZvTuXNnrr322gvab36LFy/mn//8Z6nuszSdOnWKfv360apVK/r168fp06ddlnvzzTfp0KED7du354033nCsf+aZZ4iOjqZz587079/f8VzFli1bGDlyZBl8Ah8yxvjVT7uIcGNO/mY8cfvMX8ztM3/xqKzyD9u3b/d1FUyVKlUcr//880/Tt29f8+yzz/qwRp4bMWKE+eyzzwqsz8rKuuB9d+/e3Zw4ccLj8qVxzOL429/+ZiZPnmyMMWby5MnmiSeeKFBmy5Ytpn379iYlJcVkZWWZvn37mt27dxtjjElKSnKUe/PNN80DDzzgWO7bt685cOCAlz+B51z9PwFiTQmvuxUmzbgKPK9seIWdp3aW6j7b1mrLk11cJghwqV69esyaNYsrrriCSZMmkZub6zItNsCrr77KvHnzCAoKYuDAgUyZMoWRI0dyww03cNtttzFx4kSWLl1KSEgI/fv3Z+rUqUyaNImqVavyf//3f8THxzNmzBhSU1O55JJLmD17NpGRkfTu3ZuuXbuycuVKzpw5w/vvv89VV11VZN179+5Njx49WLNmDUOGDKF379489thjJCcnU6dOHebOnUuDBg34/fffefjhhzlx4gSVK1fmP//5T4EnoXfv3k2lSpUcTyL/73//48UXXyQzM5PatWszf/586tev73Ea88JStl+IJUuWsGrVKgBGjBhB7969eeWVV/KU2bFjB926daNy5coA9OrVi0WLFvHEE0/kSXGSkpKSJ//W4MGDHU/nB6KADRTOdzwp5U0tWrQgNzeX48ePs2TJEpdpsXfu3MnixYtZv349lStX5tSpvM/2nDp1ikWLFrFz505ExOWsecOHD+ett96iV69ePPvsszz//POOrpHs7Gw2bNjAsmXLeP7554tM0md35swZfvzxR7KysujVqxdLliyhbt26fPLJJzz99NPMnj2b+++/n5kzZ9KqVSvWr1/PQw89xIoVK/LsZ82aNXlSkFx55ZWsW7cOEeG9997j1Vdf5V//+hfgWRpzdynb7c6dO1doQPzoo4+IiorKs+7PP/905G5q0KCBI2eTsw4dOvD000+TmJhIREQEy5Yty5N19+mnn+bDDz+kRo0arFy50rE+JiaGKVOmaKDwN3rHU+Arzjd/bzO2VDiFpcX+/vvvGTVqlOObaq1aeb/AVK9enfDwcO69914GDRpUYMrQpKQkzpw5Q69evQDrG/Ff/vIXx/u33HILAJdffnme5HtFGTp0KAC7du1i69atjpTpOTk5NGjQgOTkZH755Zc8x8rIyCiwn/xpxA8fPszQoUM5duwYmZmZjrTh4Fkac3cp2+2qVavmSFJYWtq1a8eTTz5Jv379qFq1Kp06dXKkEQd46aWXeOmll5g8eTLTp0/n+eefB6yWpX3MIhAFbKAAveNJlY29e/cSHBxMvXr1Ck2L/c0337hNKR4SEsKGDRv44YcfWLBgAdOnTy/wrd0de7px5xTmnrAn4zPG0L59e9auXZvn/bNnz1KzZs0iL8gRERGO+SwAxo0bx2OPPcaQIUNYtWoVkyZNKnBMKDyN+bhx4wpN2W5X3BZF/fr1HTPWHTt2jHr16rnc9p577nFMRvXUU0+5nGvjrrvuYtCgQY5AkZ6eXuAzBJKAvOtJ03aosnLixAnGjBnD2LFjEZFC02L379+f2bNnk5qaClCg6yk5OZmkpCSuv/563njjjQIX5ho1ahAZGemYB2HevHmO1kVpaNOmDSdOnHAEiqysLLZt20b16tVp3ry5IwW6MYbNmwtOQtmuXTtHZlggT8r1wjLTQuFpzD1J2W5vUbj6yR8kwGrJ2OvywQcfFDp1qr1L6uDBg3zxxRfceeedAOzZs8dRZunSpXnGaXbv3k2HDh0K/Zz+LiBbFNrtpLzJPsNdVlYWISEh3H333Tz22GOAlcto//79XHbZZRhjqFu3LosXL+a6664jPj6emJgYwsLCuP7663n55Zcd+zx37hw33ngj6enpGGMKpOkG6+JmH8xu0aIFc+bMKbXPFBYWxsKFCxk/fjxJSUlkZ2fz6KOP0r59e+bPn8+DDz7Iiy++SFZWFnfccQedOnXKs/3VV1/N448/jjEGEWHSpEn85S9/oWHDhnTr1o19+/a5PO60adN4+OGHiY6OJjs7m6uvvpqZM2fyxBNPMGLECF5//XVHGu8LNXHiRG6//Xbef/99mjRp4gh+R48e5d5772XZMivb0K233kpiYiKhoaHMmDGDyMhIx/a7du0iKCiIpk2bOlKug5VGfNCgQaVSz/LIa2nGvcWTNOOaVjxwaZrx8uuRRx5h8ODBpf58RnmXkZFBr169+Pnnn/OMZ/hSaacZD8iuJ6VU2XvqqaccXWsVycGDB5kyZUq5CRLeEHCBQscnlPKN+vXrM2TIEF9Xo8y1atXK5WB7IPHPQCGFV1vHJ5RSqnQFXKAAvS1WKaVKU0AGCqWUUqXHP6+4GiiUUqrM+OcVNyjY1zVQFdyiRYsQEXbuLDwpYe/evYmNdT8NfO/evWnTpg2dO3emXbt2zJo1q1TrOXfuXLepJR599FFWr15dqscsTXFxcXTs2JGWLVsyfvx4XN3On5mZyahRo+jYsSOdOnVyJP4DuO666+jUqRPt27dnzJgx5OTkADB9+vRSfQ4l0PlnoCikRaF3PKmy8vHHH3PllVeyYMGCC97X/PnziY+PZ82aNTz55JNkZmaWQg0t7gLFqVOnWLduHVdffbXH+ytOepDS8OCDDzJr1iz27NnDnj17+OabbwqU+c9//gNY80J89913PP744475QT799FM2b97M1q1bOXHihOMhu9GjRzNt2rSy+yB+zj9v/C0kUOgdTxXLHy+/TMaO0k0zXqldWy566im3ZZKTk1mzZg0rV65kyJAhjjxGaWlpjBo1iu3bt9OuXTvS0tIc2zz44INs3LiRtLQ0brvtNkeOoPz7rVKlCsHBVov5448/5uWXX8YYw6BBgxwpsV2tz8nJ4Z577iE2NhYRYfTo0TRu3JjY2FiGDRtGREREgZxKCxcu5LrrrnMs//Of/+R///sfaWlp9OjRg3fffRcR8Tgd+X/+8x9mzZpFZmYmLVu2ZN68eY4kiCVx7Ngxzp49S/fu1oOzw4cPZ/HixQwcODBPue3bt9O3b1/ASs5Xs2ZNYmNj6dKliyM1eHZ2NpmZmY58W5UrV6ZZs2Zs2LCBLl26lLiOFUVAtShA73hS3mdPydG6dWtq1arFr7/+CsA777xD5cqVSUhI4OmnnyYuLs6xzUsvvURsbCwJCQn8+OOPJCQkON4bNmwY0dHRtGnThmeeeYbg4GCOHj3Kk08+yYoVK4iPj2fjxo0sXry40PXx8fEcOXKErVu3smXLFkaNGsVtt91GTEyMo8WSP2ndmjVruPzyyx3LY8eOZePGjWzdupW0tDS+/PJLx3v2dOTjx49n3LhxLFy4kLi4OEaPHs3TTz8NWBlsN27cyObNm2nXrh3vv/9+gXO3cuVKx2x7zj89evQoUPbIkSN5EvI1atSII0eOFCjXqVMnlixZQnZ2Nvv27SMuLo5Dhw453h8wYAD16tWjWrVq3HbbbY71MTExjtxZyr2AaVHo/BMVT1Hf/L3l448/5tFHHwXgjjvu4OOPP+ayyy5j9erVjB8/HoDo6Giio6Md23z66afMmjWL7Oxsjh07xvbt2x3vz58/n5iYGE6cOEGPHj0ceaF69+7tSN09bNgwVq9e7fiGn3/9M888w969exk3bhyDBg2if//+RX6O/KnBV65cyauvvkpqaiqnTp2iffv2DB48GCg6HTnA1q1b+cc//sGZM2dITk4ukEEXoE+fPh6nBnc1HuEqA+/o0aPZsWMHMTExNG3alB49euR5Snr58uWkp6czbNgwVqxY4ah7vXr13I4xqfMCJlBot5MqC4mJiaxYsYKtW7ciIuTk5CAivPrqq4DrC9m+ffuYOnUqGzduJDIykpEjR5Kenl6gXN26dbnssstYv349YWFhLo9fWG62yMhINm/ezPLly5kxYwaffvops2fPdvtZIiIiHPVIT0/noYceIjY2lsaNGzNp0qQ8dSwqHTnAyJEjWbx4MZ06dWLu3Ll5BpXtVq5cyYQJEwqsr1y5Mr/88kuedY0aNeLw4cOO5cOHD3PxxRcX2DYkJCRPEsUePXrQqlWrPGXCw8MZMmQIS5YscQSKQE8NXpoCqutJu52Uty1cuJDhw4dz4MAB9u/fz6FDh2jevDk///wzV199NfPnzwesb9f27qWzZ89SpUoVatSowZ9//snXX3/tct+pqals2rSJSy65hK5du/Ljjz9y8uRJcnJy+Pjjj+nVq1eh60+ePElubi633norL7zwgqM7rFq1apw7d87l8ZxTg9uDQp06dUhOTnZMvJRfYenIwcqA26BBA7KyshznIT97iyL/T/4gAdYsdNWqVWPdunUYY/jwww9dpgZPTU0lJSUFgO+++46QkBCioqJITk7m2LFjgDVGsWzZsgqVGrw0BUyLQqmy8PHHHzNx4sQ862699VY++ugjXn/9dUaNGkV0dDSdO3d2DJJ26tSJSy+9lPbt29OiRQt69uyZZ3v7YHNGRgYjR450jBtMnjyZPn36YIzh+uuvd1wkXa3fvHkzo0aNctztM3nyZMD6lj9mzBiXg9mDBg3i3Xff5d5776VmzZrcd999dOzYkWbNmnHFFVe4/Pzu0pG/8MILdO3alaZNm9KxY8dCA1RxvPPOO4wcOZK0tDQGDhzoGMheunQpsbGx/POf/+T48eMMGDCAoKAgGjZsyLx58wBrXushQ4aQkZFBTk4O11xzDWPGjHHse82aNTz33HMXXMeKwD/TjCedhtDwPOs1tXjFoGnGS9eVV17Jl19+Sc2aNX1dlTK1adMmXn/9dUdQCTSaZhz0gTulSsm//vUvDh486OtqlLmTJ0/ywgsv+LoafkO7npSqwLp27errKviEfUBbecY/r7j5AoU+kV2x+Ft3qVJlyRv/P/w0UOS9BVFvja04wsPDSUxM1GChlAvGGBITEwkPDy+6cDH4Z9eTC3prbMVgv7f+xIkTvq6KUuVSeHh4nifaS4PfBwp9IrtiCQ0NpXnz5r6uhlIVile7nkTkOhHZJSK/ichEF++LiEyzvZ8gIpcVZ/8frT/IU4u2ANrtpJRS3uK1QCEiwcAMYCAQBdwpIlH5ig0EWtl+7gfe8XT/zkHi5Zs7areTUkp5iTe7nroAvxlj9gKIyALgRmC7U5kbgQ+NNTK5TkRqikgDY8wxdzse+u5ax11OGiSUUsq7vBkoGgKHnJYPA/lv2nZVpiGQJ1CIyP1YLQ6AjB1jemy1vzfsFRhWWjX2P3WAk76uRDmh5+I8PRfn6bk4r01JN/RmoCiYRhPy39PoSRmMMbOAWQAiElvSx9ADjZ6L8/RcnKfn4jw9F+eJiPt5ed3w5mD2YaCx03IjIP+cjJ6UUUop5UPeDBQbgVYi0lxEwoA7gKX5yiwFhtvufuoGJBU1PqGUUqpsea3ryRiTLSJjgeVAMDDbGLNNRMbY3p8JLAOuB34DUoFRHux6lpeq7I/0XJyn5+I8PRfn6bk4r8Tnwu/SjCullCpb/pnrSSmlVJnRQKGUUsqtchsovJ3+w594cC6G2c5Bgoj8IiKdfFHPslDUuXAqd4WI5IjIbWVZv7LkybkQkd4iEi8i20Tkx7KuY1nx4P9IDRH5n4hstp0LT8ZD/Y6IzBaR4yKytZD3S3bdNMaUux+swe/fgRZAGLAZiMpX5nrga6xnMboB631dbx+eix5ApO31wIp8LpzKrcC6WeI2X9fbh38XNbEyITSxLdfzdb19eC6eAl6xva4LnALCfF13L5yLq4HLgK2FvF+i62Z5bVE40n8YYzIBe/oPZ470H8aYdUBNEWlQ1hUtA0WeC2PML8aY07bFdVjPowQiT/4uAMYBnwPHy7JyZcyTc3EX8IUx5iCAMSZQz4cn58IA1UREgKpYgSK7bKvpfcaY1VifrTAlum6W10BRWGqP4pYJBMX9nPdgfWMIREWeCxFpCNwMzCzDevmCJ38XrYFIEVklInEiMrzMale2PDkX04F2WA/0bgEeMcbklk31ypUSXTfL63wUpZb+IwB4/DlFpA9WoLjSqzXyHU/OxRvAk8aYHBFXxQOGJ+ciBLgc6AtEAGtFZJ0xZre3K1fGPDkXA4B44BrgEuA7EfnJGHPWy3Urb0p03SyvgULTf5zn0ecUkWjgPWCgMSaxjOpW1jw5FzHAAluQqANcLyLZxpjFZVLDsuPp/5GTxpgUIEVEVgOdgEALFJ6ci1HAFGN11P8mIvuAtsCGsqliuVGi62Z57XrS9B/nFXkuRKQJ8AVwdwB+W3RW5LkwxjQ3xjQzxjQDFgIPBWCQAM/+jywBrhKREBGpjJW9eUcZ17MseHIuDmK1rBCR+liZVPeWaS3LhxJdN8tli8J4L/2H3/HwXDwL1Abetn2TzjYBmDHTw3NRIXhyLowxO0TkGyAByAXeM8a4vG3Sn3n4d/ECMFdEtmB1vzxpjAm49OMi8jHQG6gjIoeB54BQuLDrpqbwUEop5VZ57XpSSilVTmigUEop5ZYGCqWUUm5poFBKKeWWBgqllFJuaaBQ5ZIt82u8008zN2WTS+F4c0Vkn+1Yv4pI9xLs4z0RibK9firfe79caB1t+7Gfl622bKg1iyjfWUSuL41jq4pLb49V5ZKIJBtjqpZ2WTf7mAt8aYxZKCL9ganGmOgL2N8F16mo/YrIB8BuY8xLbsqPBGKMMWNLuy6q4tAWhfILIlJVRH6wfdvfIiIFssaKSAMRWe30jfsq2/r+IrLWtu1nIlLUBXw10NK27WO2fW0VkUdt66qIyFe2uQ22ishQ2/pVIhIjIlOACFs95tveS7b9/sT5G76tJXOriASLyGsislGseQIe8OC0rMWW0E1Euog1F8km2+82tqeU/wkMtdVlqK3us23H2eTqPCpVgK/zp+uP/rj6AXKwkrjFA4uwsghUt71XB+vJUnuLONn2+3HgadvrYKCarexqoIpt/ZPAsy6ONxfb3BXAX4D1WAn1tgBVsFJTbwMuBW4F/uO0bQ3b71VY394ddXIqY6/jzcAHttdhWJk8I4D7gX/Y1lcCYoHmLuqZ7PT5PgOusy1XB0Jsr68FPre9HglMd9r+ZeCvttc1sfI+VfH1v7f+lO+fcpnCQykgzRjT2b4gIqHAyyJyNVY6ioZAfeAPp202ArNtZRcbY+JFpBcQBayxpTcJw/om7sprIvIP4ARWFt6+wCJjJdVDRL4ArgK+AaaKyCtY3VU/FeNzfQ1ME5FKwHXAamNMmq27K1rOz8hXA2gF7Mu3fYSIxAPNgDjgO6fyH4hIK6xsoKGFHL8/MERE/s+2HA40ITBzQKlSooFC+YthWDOTXW6MyRKR/VgXOQdjzGpbIBkEzBOR14DTwHfGmDs9OMbfjDEL7Qsicq2rQsaY3SJyOVbOnMki8q0x5p+efAhjTLqIrMJKez0U+Nh+OGCcMWZ5EbtIM8Z0FpEawJfAw8A0rFxGK40xN9sG/lcVsr0AtxpjdnlSX6VAxyiU/6gBHLcFiT5A0/wFRKSprcx/gPexpoRcB/QUEfuYQ2URae3hMVcDN9m2qYLVbfSTiFwMpBpj/gtMtR0nvyxby8aVBVjJ2K7CSmSH7feD9m1EpLXtmC4ZY5KA8cD/2bapARyxvT3Sqeg5rC44u+XAOLE1r0Tk0sKOoZSdBgrlL+YDMSISi9W62OmiTG8gXkQ2YY0jvGmMOYF14fxYRBKwAkdbTw5ojPkVa+xiA9aYxXvGmE1AR2CDrQvoaeBFF5vPAhLsg9n5fIs1t/H3xpq6E6y5RLYDv4rIVuBdimjx2+qyGSut9qtYrZs1WOMXdiuBKPtgNlbLI9RWt622ZaXc0ttjlVJKuaUtCqWUUm5poFBKKeWWBgqllFJuaaBQSinllgYKpZRSbmmgUEop5ZYGCqWUUm79P+bpih1PDJQvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC Graph\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, model.predict_proba(X_test)[:,1])\n",
    "rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, rf.predict_proba(X_test)[:,1])\n",
    "dt_fpr, dt_tpr, dt_thresholds = roc_curve(y_test, dtree.predict_proba(X_test)[:,1])\n",
    "ada_fpr, ada_tpr, ada_thresholds = roc_curve(y_test, ada.predict_proba(X_test)[:,1])\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "# Plot Logistic Regression ROC\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % roc_auc_score(y_test, model.predict(X_test)))\n",
    "\n",
    "# Plot Random Forest ROC\n",
    "plt.plot(rf_fpr, rf_tpr, label='Random Forest (area = %0.2f)' % roc_auc_score(y_test, rf.predict(X_test)))\n",
    "\n",
    "# Plot Decision Tree ROC\n",
    "plt.plot(dt_fpr, dt_tpr, label='Decision Tree (area = %0.2f)' % roc_auc_score(y_test, dtree.predict(X_test)))\n",
    "\n",
    "# Plot AdaBoost ROC\n",
    "plt.plot(ada_fpr, ada_tpr, label='AdaBoost (area = %0.2f)' % roc_auc_score(y_test, ada.predict(X_test)))\n",
    "\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Graph')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5b7b703b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature Importance\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12,6)\n",
    "\n",
    "## plot the importances ##\n",
    "importances = dtree.feature_importances_\n",
    "feat_names = df.drop(['Turnover'],axis=1).columns\n",
    "\n",
    "indices = np.argsort(importances)[::-1]\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title('Feature Importance by Decision Tree Classifier')\n",
    "plt.bar(range(len(indices)), importances[indices], color='lightblue',  align=\"center\")\n",
    "plt.step(range(len(indices)), np.cumsum(importances[indices]), where='mid', label='Cumulative')\n",
    "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical',fontsize=14)\n",
    "plt.xlim([-1, len(indices)])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c72177",
   "metadata": {},
   "source": [
    "Conclusions:\n",
    "\n",
    "Employee Satisfaction, Years At Company, Evaluation are the biggest factors regarding staff turnover.\n",
    "\n",
    "1. Low to medium salaried employees had higher attrition rates. Conversely, higher-salaried employees has lower attrition rates. \n",
    "2. Highest employee turnover (ranked order) Sales, Technical, Support. Conversely, Management lowest employee turnover.\n",
    "3. High employee turnover 2, 6, 7 projects; most employees with 3, 4, 5 projects remained; all employees with 7 projects left the company.\n",
    "4. Low to high performance employees tended to leave the most; employee evaluations between 0.6-0.8 tended to remain at the company. \n",
    "5. Employees with less hours of work (150 hours or less) or (250 hours or more) had higher attrition rates.\n",
    "6. Low satisfaction level employees (0.2 or less) or (0.3~0.5) and high satisfaction level employees (0.7 or more) all left the company at higher rates. \n",
    "7. More than 1/2 of employees with 4 and 5 years tenure left the company.\n",
    "8. An average evaluation of roughly 70% meant an employee was likely to stay (even with difference project counts); the data skews towards a high turnover rate after 3 projects or two projects and a lower employee evaluation; employees with more than 3 projects and higher employee evaluations also had higher attrition rates. \n",
    "9. Using a Random Forest was the most accurate for data modeling purposes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aa43027",
   "metadata": {},
   "source": [
    "Suggestions: \n",
    "\n",
    "Use the Kirkpatrick Evaluation Model as part of conducting a Root-cause Analysis \n",
    "\n",
    "1. Reaction - Conduct a company-wide survey of all employees\n",
    "2. Learning - Based on the survey's results implement on-the-job training aimed at reducing attrition rates\n",
    "3. Behavior - Monitor if desired improvements are being incorporated or if there is still cultural resistance\n",
    "4. Results - Reduced employee turnover; increased employee evaluation scores \n",
    "\n",
    "The above should center on, what are the knowledge, motivation, and organizational solutions to these barriers?\n",
    "\n",
    "Areas to address include: \n",
    "\n",
    "1. Overall attrition-rates\n",
    "2. Sales, Technical and Support turnover rates, i.e., KPI's; Salary; Management; Career Mobility; finding the possible causes / solutions?\n",
    "3. Project-related turnover rates; lack of support; level of difficulties; timelines; again, finding the possible causes / solutions?\n",
    "4. Employee satisfaction; what (if anything) is being done about this? Re-assess management (including Human Resources' policies to find the possible areas where this can be addressed and improved).\n",
    "5. Employee retention, i.e., in the 4 to 5 year-segment means knowledge, motivation, and organizational is being lost (aside from the people who left with such).\n",
    "6. Examining why project count; employee evaluation and attrition are correlated and determining causation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "727e4b70",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "language": "python",
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