Amexpert_2019_Part_2.ipynb

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  "metadata": {
    "colab": {
      "name": "Amexpert-2019- Part 2.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/buntys2010/AV-AmExpert-2019-ML-Hackathon/blob/master/Amexpert_2019_Part_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GwgW0_E_XhF",
        "colab_type": "text"
      },
      "source": [
        "## Get Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4hdPxaO_HPv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pV4iu5gp_c-T",
        "colab_type": "code",
        "outputId": "cb9515c9-10df-46ea-d26f-d242967f27a5",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-99f4f1b3-ab04-4e55-8208-da23467c23fa\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-99f4f1b3-ab04-4e55-8208-da23467c23fa\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle (1).json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"buntyshah\",\"key\":\"9f654aca594a996bd14418483b21a6af\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC52sCHj_lLT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q kaggle\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oV7nhECU_qd2",
        "colab_type": "code",
        "outputId": "14741780-fbdf-4677-aa34-4fa0883f869b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!kaggle datasets download buntyshah/amexpert-2019"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading amexpert-2019.zip to /content\n",
            " 78% 9.00M/11.5M [00:00<00:00, 16.7MB/s]\n",
            "100% 11.5M/11.5M [00:00<00:00, 21.4MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ah6V9pcd_xxJ",
        "colab_type": "code",
        "outputId": "d40e0aa3-b1eb-41ef-8b01-4b14ab2ea661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "!unzip \"../content/amexpert-2019.zip\" "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  ../content/amexpert-2019.zip\n",
            "  inflating: Submission.csv          \n",
            "  inflating: test_QyjYwdj.csv        \n",
            "  inflating: train_AUpWtIz (1)/campaign_data.csv  \n",
            "  inflating: train_AUpWtIz (1)/coupon_item_mapping.csv  \n",
            "  inflating: train_AUpWtIz (1)/customer_demographics.csv  \n",
            "  inflating: train_AUpWtIz (1)/customer_transaction_data.csv  \n",
            "  inflating: train_AUpWtIz (1)/item_data.csv  \n",
            "  inflating: train_AUpWtIz (1)/train.csv  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DI5gEnIQ__1I",
        "colab_type": "text"
      },
      "source": [
        "## Read Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajvJtUvE_4BS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#  Libraries\n",
        "import numpy as np \n",
        "import pandas as pd \n",
        "# Data processing, metrics and modeling\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold,KFold\n",
        "\n",
        "from datetime import datetime\n",
        "from sklearn.metrics import precision_score, recall_score, confusion_matrix, accuracy_score, roc_auc_score, f1_score, roc_curve, auc,precision_recall_curve\n",
        "from sklearn import metrics\n",
        "from sklearn import preprocessing\n",
        "# Lgbm\n",
        "import lightgbm as lgb\n",
        "# Suppr warning\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import itertools\n",
        "from scipy import interp\n",
        "\n",
        "# Plots\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from matplotlib import rcParams"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTOR4SNYABZ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train= pd.read_csv(\"../content/train_AUpWtIz (1)/train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tBqPY-VAJF3",
        "colab_type": "code",
        "outputId": "3981954a-2c76-444d-87ad-cd6e3b42c740",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "train.redemption_status.value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    77640\n",
              "1      729\n",
              "Name: redemption_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-Hi_jjAAXEY",
        "colab_type": "code",
        "outputId": "bb1a02aa-a656-4f4f-cb79-43ebd18852c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(78369, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMZUubeJAYzJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test = pd.read_csv(\"../content/test_QyjYwdj.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzhEhicDAdZ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Read 5 common files\n",
        "\n",
        "campaign_data = pd.read_csv(\"../content/train_AUpWtIz (1)/campaign_data.csv\")\n",
        "coupon_item_mapping =  pd.read_csv(\"../content/train_AUpWtIz (1)/coupon_item_mapping.csv\")\n",
        "customer_demographics = pd.read_csv(\"../content/train_AUpWtIz (1)/customer_demographics.csv\")\n",
        "customer_transaction_data = pd.read_csv(\"../content/train_AUpWtIz (1)/customer_transaction_data.csv\")\n",
        "item_data = pd.read_csv(\"../content/train_AUpWtIz (1)/item_data.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oo3-AK58BHpm",
        "colab_type": "text"
      },
      "source": [
        "![alt text](https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2019/09/Screenshot-2019-09-28-at-8.58.32-PM.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8W0SH0mLBQkf",
        "colab_type": "text"
      },
      "source": [
        "## Lets Combine campaign_id with Train/test as it doesnt have any dependency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MmGI4sGUArEs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train,campaign_data,on='campaign_id',how='left')\n",
        "test_df = pd.merge(test,campaign_data,on='campaign_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31gLZntnBdyA",
        "colab_type": "text"
      },
      "source": [
        "## Lets Combine customer_demographics with train and test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TnodhGPBb92",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train_df , customer_demographics ,on='customer_id',how='left')\n",
        "test_df = pd.merge(test_df , customer_demographics ,on='customer_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YXwbckP1IEuB",
        "colab_type": "text"
      },
      "source": [
        "## Create new DF to combine remaining blocks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7q4uxGPBlcV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.merge(customer_transaction_data , item_data ,on='item_id',how='left')\n",
        "df = pd.merge(df , coupon_item_mapping ,on='item_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4AShL_BIM1L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df.fillna(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "31-HimIIIP2r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['date'] = df['date'].astype('datetime64[ns]') \n",
        "#df['C_date_year'] = df['date'].dt.year\n",
        "df['C_date_month'] = df['date'].dt.month\n",
        "df['C_date_day'] = df['date'].dt.day\n",
        "# df = df.drop('date',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOorOK90IvQi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Feature engg\n",
        "df['per_item_price'] = df['selling_price'] / df['quantity']\n",
        "df['item_price_after_discount'] = (df['selling_price'] + df['other_discount'] + df['coupon_discount']) / df['quantity']\n",
        "df['discount'] = df['other_discount'] + df['coupon_discount']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QiCMfbanZfFz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['NF_coupon_month'] = df['coupon_id'].astype(str) + \"_\" + df['C_date_month'].astype(str)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yKMQzBLaCVr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "for f in df.columns:\n",
        "    if  df[f].dtype=='object': \n",
        "        lbl = preprocessing.LabelEncoder()\n",
        "        lbl.fit(list(df[f].values))\n",
        "        df[f] = lbl.transform(list(df[f].values))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RMREBJ8ae4F",
        "colab_type": "text"
      },
      "source": [
        "## Creating Aggrigate features for *customer_id*\n",
        "\n",
        "Note : Created Additional Mode Feature apart from Part 1 Solution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NdH7TGg3acgD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "['mean','sum','min','max','median','quantile','mad','std','count','nunique']\n",
        "ag_cat=['count','nunique','min','max','last']\n",
        "ag_num=['mean','sum','min','max','median','quantile','mad','std']\n",
        "\n",
        "agg_func = {     # find the sum of the durations for each group\n",
        "                                    'item_id' : ag_cat,\n",
        "                                    'quantity':ag_num,\n",
        "                                    'selling_price': ag_num,\n",
        "                                    'other_discount':ag_num,\n",
        "                                    'coupon_discount':ag_num,\n",
        "                                    'brand':ag_cat,\n",
        "                                    'brand_type':ag_cat,\n",
        "                                    'category':ag_cat,\n",
        "                                    'coupon_id':ag_cat,\n",
        "                                    #'C_date_year':ag_cat,\n",
        "                                    'C_date_month':ag_cat,\n",
        "                                    'C_date_day':ag_cat,\n",
        "                                    'per_item_price':ag_num,\n",
        "                                    'item_price_after_discount':ag_num,\n",
        "                                    'NF_coupon_month':ag_cat\n",
        "                                }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FhN9RwWHavLs",
        "colab_type": "code",
        "outputId": "24ebca2b-d3a7-41a9-884f-29116eb10c34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "df_customer_groupby = df.groupby(by=['customer_id']).agg(agg_func)\n",
        "df_customer_groupby.columns=['agg_cust_id_' + '_'.join(col).strip() for col in df_customer_groupby.columns.values]\n",
        "df_customer_groupby.reset_index(inplace=True)\n",
        "df_customer_groupby.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>agg_cust_id_item_id_count</th>\n",
              "      <th>agg_cust_id_item_id_nunique</th>\n",
              "      <th>agg_cust_id_item_id_min</th>\n",
              "      <th>agg_cust_id_item_id_max</th>\n",
              "      <th>agg_cust_id_item_id_last</th>\n",
              "      <th>agg_cust_id_quantity_mean</th>\n",
              "      <th>agg_cust_id_quantity_sum</th>\n",
              "      <th>agg_cust_id_quantity_min</th>\n",
              "      <th>agg_cust_id_quantity_max</th>\n",
              "      <th>agg_cust_id_quantity_median</th>\n",
              "      <th>agg_cust_id_quantity_quantile</th>\n",
              "      <th>agg_cust_id_quantity_mad</th>\n",
              "      <th>agg_cust_id_quantity_std</th>\n",
              "      <th>agg_cust_id_selling_price_mean</th>\n",
              "      <th>agg_cust_id_selling_price_sum</th>\n",
              "      <th>agg_cust_id_selling_price_min</th>\n",
              "      <th>agg_cust_id_selling_price_max</th>\n",
              "      <th>agg_cust_id_selling_price_median</th>\n",
              "      <th>agg_cust_id_selling_price_quantile</th>\n",
              "      <th>agg_cust_id_selling_price_mad</th>\n",
              "      <th>agg_cust_id_selling_price_std</th>\n",
              "      <th>agg_cust_id_other_discount_mean</th>\n",
              "      <th>agg_cust_id_other_discount_sum</th>\n",
              "      <th>agg_cust_id_other_discount_min</th>\n",
              "      <th>agg_cust_id_other_discount_max</th>\n",
              "      <th>agg_cust_id_other_discount_median</th>\n",
              "      <th>agg_cust_id_other_discount_quantile</th>\n",
              "      <th>agg_cust_id_other_discount_mad</th>\n",
              "      <th>agg_cust_id_other_discount_std</th>\n",
              "      <th>agg_cust_id_coupon_discount_mean</th>\n",
              "      <th>agg_cust_id_coupon_discount_sum</th>\n",
              "      <th>agg_cust_id_coupon_discount_min</th>\n",
              "      <th>agg_cust_id_coupon_discount_max</th>\n",
              "      <th>agg_cust_id_coupon_discount_median</th>\n",
              "      <th>agg_cust_id_coupon_discount_quantile</th>\n",
              "      <th>agg_cust_id_coupon_discount_mad</th>\n",
              "      <th>agg_cust_id_coupon_discount_std</th>\n",
              "      <th>agg_cust_id_brand_count</th>\n",
              "      <th>agg_cust_id_brand_nunique</th>\n",
              "      <th>...</th>\n",
              "      <th>agg_cust_id_category_nunique</th>\n",
              "      <th>agg_cust_id_category_min</th>\n",
              "      <th>agg_cust_id_category_max</th>\n",
              "      <th>agg_cust_id_category_last</th>\n",
              "      <th>agg_cust_id_coupon_id_count</th>\n",
              "      <th>agg_cust_id_coupon_id_nunique</th>\n",
              "      <th>agg_cust_id_coupon_id_min</th>\n",
              "      <th>agg_cust_id_coupon_id_max</th>\n",
              "      <th>agg_cust_id_coupon_id_last</th>\n",
              "      <th>agg_cust_id_C_date_month_count</th>\n",
              "      <th>agg_cust_id_C_date_month_nunique</th>\n",
              "      <th>agg_cust_id_C_date_month_min</th>\n",
              "      <th>agg_cust_id_C_date_month_max</th>\n",
              "      <th>agg_cust_id_C_date_month_last</th>\n",
              "      <th>agg_cust_id_C_date_day_count</th>\n",
              "      <th>agg_cust_id_C_date_day_nunique</th>\n",
              "      <th>agg_cust_id_C_date_day_min</th>\n",
              "      <th>agg_cust_id_C_date_day_max</th>\n",
              "      <th>agg_cust_id_C_date_day_last</th>\n",
              "      <th>agg_cust_id_per_item_price_mean</th>\n",
              "      <th>agg_cust_id_per_item_price_sum</th>\n",
              "      <th>agg_cust_id_per_item_price_min</th>\n",
              "      <th>agg_cust_id_per_item_price_max</th>\n",
              "      <th>agg_cust_id_per_item_price_median</th>\n",
              "      <th>agg_cust_id_per_item_price_quantile</th>\n",
              "      <th>agg_cust_id_per_item_price_mad</th>\n",
              "      <th>agg_cust_id_per_item_price_std</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_mean</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_sum</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_min</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_max</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_median</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_quantile</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_mad</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_std</th>\n",
              "      <th>agg_cust_id_NF_coupon_month_count</th>\n",
              "      <th>agg_cust_id_NF_coupon_month_nunique</th>\n",
              "      <th>agg_cust_id_NF_coupon_month_min</th>\n",
              "      <th>agg_cust_id_NF_coupon_month_max</th>\n",
              "      <th>agg_cust_id_NF_coupon_month_last</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2211</td>\n",
              "      <td>463</td>\n",
              "      <td>4315</td>\n",
              "      <td>73348</td>\n",
              "      <td>45986</td>\n",
              "      <td>1.134328</td>\n",
              "      <td>2508</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.240588</td>\n",
              "      <td>0.454791</td>\n",
              "      <td>95.070873</td>\n",
              "      <td>210201.70</td>\n",
              "      <td>12.11</td>\n",
              "      <td>445.25</td>\n",
              "      <td>89.05</td>\n",
              "      <td>89.05</td>\n",
              "      <td>35.683296</td>\n",
              "      <td>53.433822</td>\n",
              "      <td>-20.346404</td>\n",
              "      <td>-44985.90</td>\n",
              "      <td>-372.23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-10.69</td>\n",
              "      <td>-10.69</td>\n",
              "      <td>21.518545</td>\n",
              "      <td>36.210123</td>\n",
              "      <td>-1.940900</td>\n",
              "      <td>-4291.33</td>\n",
              "      <td>-106.86</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.595625</td>\n",
              "      <td>8.661971</td>\n",
              "      <td>2211</td>\n",
              "      <td>163</td>\n",
              "      <td>...</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>2211</td>\n",
              "      <td>160</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1089.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2211</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>2211</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>30</td>\n",
              "      <td>86.604146</td>\n",
              "      <td>191481.767500</td>\n",
              "      <td>12.110000</td>\n",
              "      <td>391.46</td>\n",
              "      <td>89.05</td>\n",
              "      <td>89.05</td>\n",
              "      <td>31.372843</td>\n",
              "      <td>43.240596</td>\n",
              "      <td>68.600825</td>\n",
              "      <td>151676.423833</td>\n",
              "      <td>-35.265</td>\n",
              "      <td>391.46</td>\n",
              "      <td>63.760</td>\n",
              "      <td>63.760</td>\n",
              "      <td>36.339287</td>\n",
              "      <td>46.937958</td>\n",
              "      <td>2211</td>\n",
              "      <td>725</td>\n",
              "      <td>0</td>\n",
              "      <td>12108</td>\n",
              "      <td>3738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>823</td>\n",
              "      <td>352</td>\n",
              "      <td>137</td>\n",
              "      <td>73242</td>\n",
              "      <td>73242</td>\n",
              "      <td>1.117861</td>\n",
              "      <td>920</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.213669</td>\n",
              "      <td>0.417919</td>\n",
              "      <td>103.368943</td>\n",
              "      <td>85072.64</td>\n",
              "      <td>14.25</td>\n",
              "      <td>640.80</td>\n",
              "      <td>89.05</td>\n",
              "      <td>89.05</td>\n",
              "      <td>45.778112</td>\n",
              "      <td>71.076889</td>\n",
              "      <td>-16.798141</td>\n",
              "      <td>-13824.87</td>\n",
              "      <td>-312.03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>20.244751</td>\n",
              "      <td>33.011891</td>\n",
              "      <td>-0.302965</td>\n",
              "      <td>-249.34</td>\n",
              "      <td>-89.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.602985</td>\n",
              "      <td>4.721158</td>\n",
              "      <td>823</td>\n",
              "      <td>153</td>\n",
              "      <td>...</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>823</td>\n",
              "      <td>138</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1109.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>823</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>823</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "      <td>26</td>\n",
              "      <td>95.445093</td>\n",
              "      <td>78551.311667</td>\n",
              "      <td>7.125000</td>\n",
              "      <td>640.80</td>\n",
              "      <td>88.34</td>\n",
              "      <td>88.34</td>\n",
              "      <td>39.491361</td>\n",
              "      <td>63.636083</td>\n",
              "      <td>81.248777</td>\n",
              "      <td>66867.743500</td>\n",
              "      <td>-78.010</td>\n",
              "      <td>640.80</td>\n",
              "      <td>70.880</td>\n",
              "      <td>70.880</td>\n",
              "      <td>43.799330</td>\n",
              "      <td>67.801977</td>\n",
              "      <td>823</td>\n",
              "      <td>302</td>\n",
              "      <td>1</td>\n",
              "      <td>12220</td>\n",
              "      <td>3982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1351</td>\n",
              "      <td>406</td>\n",
              "      <td>4308</td>\n",
              "      <td>73698</td>\n",
              "      <td>73698</td>\n",
              "      <td>6.861584</td>\n",
              "      <td>9270</td>\n",
              "      <td>1</td>\n",
              "      <td>6949</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.358366</td>\n",
              "      <td>189.016329</td>\n",
              "      <td>113.680792</td>\n",
              "      <td>153582.75</td>\n",
              "      <td>8.90</td>\n",
              "      <td>862.00</td>\n",
              "      <td>89.05</td>\n",
              "      <td>89.05</td>\n",
              "      <td>59.687796</td>\n",
              "      <td>85.572038</td>\n",
              "      <td>-24.400081</td>\n",
              "      <td>-32964.51</td>\n",
              "      <td>-463.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-9.26</td>\n",
              "      <td>-9.26</td>\n",
              "      <td>25.908501</td>\n",
              "      <td>40.760823</td>\n",
              "      <td>-2.578209</td>\n",
              "      <td>-3483.16</td>\n",
              "      <td>-142.48</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.797644</td>\n",
              "      <td>11.568789</td>\n",
              "      <td>1351</td>\n",
              "      <td>114</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>1351</td>\n",
              "      <td>136</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1109.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1351</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1351</td>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>77.733098</td>\n",
              "      <td>105017.415082</td>\n",
              "      <td>0.102570</td>\n",
              "      <td>862.00</td>\n",
              "      <td>70.88</td>\n",
              "      <td>70.88</td>\n",
              "      <td>36.123117</td>\n",
              "      <td>64.565806</td>\n",
              "      <td>60.573072</td>\n",
              "      <td>81834.220831</td>\n",
              "      <td>-70.520</td>\n",
              "      <td>487.63</td>\n",
              "      <td>49.865</td>\n",
              "      <td>49.865</td>\n",
              "      <td>34.352176</td>\n",
              "      <td>52.264127</td>\n",
              "      <td>1351</td>\n",
              "      <td>408</td>\n",
              "      <td>0</td>\n",
              "      <td>12183</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>451</td>\n",
              "      <td>125</td>\n",
              "      <td>4617</td>\n",
              "      <td>65281</td>\n",
              "      <td>61047</td>\n",
              "      <td>1.283814</td>\n",
              "      <td>579</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.440509</td>\n",
              "      <td>0.624805</td>\n",
              "      <td>143.631774</td>\n",
              "      <td>64777.93</td>\n",
              "      <td>14.25</td>\n",
              "      <td>1330.05</td>\n",
              "      <td>106.86</td>\n",
              "      <td>106.86</td>\n",
              "      <td>72.674528</td>\n",
              "      <td>147.361484</td>\n",
              "      <td>-15.565854</td>\n",
              "      <td>-7020.20</td>\n",
              "      <td>-390.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>18.483995</td>\n",
              "      <td>31.132399</td>\n",
              "      <td>-0.197450</td>\n",
              "      <td>-89.05</td>\n",
              "      <td>-89.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.394025</td>\n",
              "      <td>4.193201</td>\n",
              "      <td>451</td>\n",
              "      <td>72</td>\n",
              "      <td>...</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>11</td>\n",
              "      <td>451</td>\n",
              "      <td>67</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1098.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>451</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>451</td>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>117.863659</td>\n",
              "      <td>53156.510000</td>\n",
              "      <td>14.250000</td>\n",
              "      <td>1330.05</td>\n",
              "      <td>106.50</td>\n",
              "      <td>106.50</td>\n",
              "      <td>50.081832</td>\n",
              "      <td>136.483424</td>\n",
              "      <td>106.223135</td>\n",
              "      <td>47906.634000</td>\n",
              "      <td>-6.768</td>\n",
              "      <td>1330.05</td>\n",
              "      <td>88.695</td>\n",
              "      <td>88.695</td>\n",
              "      <td>54.810286</td>\n",
              "      <td>139.111221</td>\n",
              "      <td>451</td>\n",
              "      <td>207</td>\n",
              "      <td>1</td>\n",
              "      <td>11726</td>\n",
              "      <td>3981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1594</td>\n",
              "      <td>490</td>\n",
              "      <td>4314</td>\n",
              "      <td>71182</td>\n",
              "      <td>69320</td>\n",
              "      <td>59.345671</td>\n",
              "      <td>94597</td>\n",
              "      <td>1</td>\n",
              "      <td>14638</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>115.211085</td>\n",
              "      <td>826.140617</td>\n",
              "      <td>128.931066</td>\n",
              "      <td>205516.12</td>\n",
              "      <td>12.11</td>\n",
              "      <td>1485.35</td>\n",
              "      <td>99.38</td>\n",
              "      <td>99.38</td>\n",
              "      <td>77.382276</td>\n",
              "      <td>121.436279</td>\n",
              "      <td>-13.463024</td>\n",
              "      <td>-21460.06</td>\n",
              "      <td>-216.21</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>18.705007</td>\n",
              "      <td>28.304603</td>\n",
              "      <td>-0.126255</td>\n",
              "      <td>-201.25</td>\n",
              "      <td>-71.24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.251717</td>\n",
              "      <td>2.660559</td>\n",
              "      <td>1594</td>\n",
              "      <td>168</td>\n",
              "      <td>...</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>1594</td>\n",
              "      <td>122</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1101.0</td>\n",
              "      <td>668.0</td>\n",
              "      <td>1594</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>1594</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>29</td>\n",
              "      <td>103.868092</td>\n",
              "      <td>165565.738810</td>\n",
              "      <td>0.086868</td>\n",
              "      <td>640.80</td>\n",
              "      <td>83.35</td>\n",
              "      <td>83.35</td>\n",
              "      <td>62.031263</td>\n",
              "      <td>89.910336</td>\n",
              "      <td>92.955942</td>\n",
              "      <td>148171.772333</td>\n",
              "      <td>-21.020</td>\n",
              "      <td>640.80</td>\n",
              "      <td>63.760</td>\n",
              "      <td>63.760</td>\n",
              "      <td>63.187028</td>\n",
              "      <td>90.713374</td>\n",
              "      <td>1594</td>\n",
              "      <td>412</td>\n",
              "      <td>0</td>\n",
              "      <td>12103</td>\n",
              "      <td>8249</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 89 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id  ...  agg_cust_id_NF_coupon_month_last\n",
              "0            1  ...                              3738\n",
              "1            2  ...                              3982\n",
              "2            3  ...                                 8\n",
              "3            4  ...                              3981\n",
              "4            5  ...                              8249\n",
              "\n",
              "[5 rows x 89 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kmiYxtcNa0o-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train_df , df_customer_groupby ,on='customer_id',how='left')\n",
        "test_df = pd.merge(test_df , df_customer_groupby ,on='customer_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHUf-6oz13w7",
        "colab_type": "code",
        "outputId": "c86fbad7-a316-435f-eb5b-a22de502a847",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "# Add Mode Values\n",
        "\n",
        "import scipy.stats\n",
        "df_customer_groupby_mode = df.groupby(by=['customer_id']).agg(lambda x: scipy.stats.mode(x)[0][0])\n",
        "df_customer_groupby_mode.columns= ['agg_cust_id_' + col +'_mode' for col in df_customer_groupby_mode.columns.values]\n",
        "df_customer_groupby_mode.reset_index(inplace=True)\n",
        "df_customer_groupby_mode.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>agg_cust_id_date_mode</th>\n",
              "      <th>agg_cust_id_item_id_mode</th>\n",
              "      <th>agg_cust_id_quantity_mode</th>\n",
              "      <th>agg_cust_id_selling_price_mode</th>\n",
              "      <th>agg_cust_id_other_discount_mode</th>\n",
              "      <th>agg_cust_id_coupon_discount_mode</th>\n",
              "      <th>agg_cust_id_brand_mode</th>\n",
              "      <th>agg_cust_id_brand_type_mode</th>\n",
              "      <th>agg_cust_id_category_mode</th>\n",
              "      <th>agg_cust_id_coupon_id_mode</th>\n",
              "      <th>agg_cust_id_C_date_month_mode</th>\n",
              "      <th>agg_cust_id_C_date_day_mode</th>\n",
              "      <th>agg_cust_id_per_item_price_mode</th>\n",
              "      <th>agg_cust_id_item_price_after_discount_mode</th>\n",
              "      <th>agg_cust_id_discount_mode</th>\n",
              "      <th>agg_cust_id_NF_coupon_month_mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2012-12-03</td>\n",
              "      <td>45986</td>\n",
              "      <td>1</td>\n",
              "      <td>89.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1075</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>89.05</td>\n",
              "      <td>106.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2012-06-13</td>\n",
              "      <td>30981</td>\n",
              "      <td>1</td>\n",
              "      <td>71.24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6</td>\n",
              "      <td>13</td>\n",
              "      <td>71.24</td>\n",
              "      <td>106.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2012-11-16</td>\n",
              "      <td>22883</td>\n",
              "      <td>1</td>\n",
              "      <td>70.88</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>35.62</td>\n",
              "      <td>70.88</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2012-07-18</td>\n",
              "      <td>47968</td>\n",
              "      <td>1</td>\n",
              "      <td>89.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>18</td>\n",
              "      <td>89.05</td>\n",
              "      <td>71.60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2012-04-28</td>\n",
              "      <td>35304</td>\n",
              "      <td>1</td>\n",
              "      <td>35.26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>28</td>\n",
              "      <td>35.26</td>\n",
              "      <td>35.26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id  ... agg_cust_id_NF_coupon_month_mode\n",
              "0            1  ...                                7\n",
              "1            2  ...                                8\n",
              "2            3  ...                                9\n",
              "3            4  ...                                7\n",
              "4            5  ...                                8\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xe4d4bTj8Q-l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train_df , df_customer_groupby_mode ,on='customer_id',how='left')\n",
        "test_df = pd.merge(test_df , df_customer_groupby_mode ,on='customer_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df4BKWKga47J",
        "colab_type": "text"
      },
      "source": [
        "## Creating Aggrigate features for coupon_id\n",
        "\n",
        "Note : Created Additional Mode Feature apart from Part 1 Solution\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KS9jDvGEa3U_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "['mean','sum','min','max','median','quantile','mad','std','count','nunique']\n",
        "ag_cat=['count','nunique','min','max','last']\n",
        "ag_num=['mean','sum','min','max','median','quantile','mad','std']\n",
        "\n",
        "agg_func_1 = {     # find the sum of the durations for each group\n",
        "                                    'customer_id' : ag_cat,\n",
        "                                    'quantity':ag_num,\n",
        "                                    'selling_price': ag_num,\n",
        "                                    'other_discount':ag_num,\n",
        "                                    'coupon_discount':ag_num,\n",
        "                                    'brand':ag_cat,\n",
        "                                    'brand_type':ag_cat,\n",
        "                                    'category':ag_cat,\n",
        "                                    'item_id':ag_cat,\n",
        "                                    #'C_date_year':ag_cat,\n",
        "                                    'C_date_month':ag_cat,\n",
        "                                    'C_date_day':ag_cat,\n",
        "                                    'per_item_price':ag_num,\n",
        "                                    'item_price_after_discount':ag_num,\n",
        "                                    'NF_coupon_month':ag_cat\n",
        "                                }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cJv7Q__bEnv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_coupon_id_groupby = df.groupby(by=['coupon_id']).agg(agg_func_1)\n",
        "df_coupon_id_groupby.columns=['agg_cp_id_' + '_'.join(col).strip() for col in df_coupon_id_groupby.columns.values]\n",
        "df_coupon_id_groupby.reset_index(inplace=True)\n",
        "train_df = pd.merge(train_df , df_coupon_id_groupby ,on='coupon_id',how='left')\n",
        "test_df = pd.merge(test_df , df_coupon_id_groupby ,on='coupon_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WR4yUF7a8Vux",
        "colab_type": "code",
        "outputId": "b3f6984b-f6d0-4e9e-aa3c-120aa79a03dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "# Add Mode Values\n",
        "\n",
        "import scipy.stats\n",
        "df_coupon_id_groupby_mode = df.groupby(by=['coupon_id']).agg(lambda x: scipy.stats.mode(x)[0][0])\n",
        "df_coupon_id_groupby_mode.columns= ['agg_cp_id_' + col +'_mode' for col in df_coupon_id_groupby_mode.columns.values]\n",
        "df_coupon_id_groupby_mode.reset_index(inplace=True)\n",
        "df_coupon_id_groupby_mode.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>coupon_id</th>\n",
              "      <th>agg_cp_id_date_mode</th>\n",
              "      <th>agg_cp_id_customer_id_mode</th>\n",
              "      <th>agg_cp_id_item_id_mode</th>\n",
              "      <th>agg_cp_id_quantity_mode</th>\n",
              "      <th>agg_cp_id_selling_price_mode</th>\n",
              "      <th>agg_cp_id_other_discount_mode</th>\n",
              "      <th>agg_cp_id_coupon_discount_mode</th>\n",
              "      <th>agg_cp_id_brand_mode</th>\n",
              "      <th>agg_cp_id_brand_type_mode</th>\n",
              "      <th>agg_cp_id_category_mode</th>\n",
              "      <th>agg_cp_id_C_date_month_mode</th>\n",
              "      <th>agg_cp_id_C_date_day_mode</th>\n",
              "      <th>agg_cp_id_per_item_price_mode</th>\n",
              "      <th>agg_cp_id_item_price_after_discount_mode</th>\n",
              "      <th>agg_cp_id_discount_mode</th>\n",
              "      <th>agg_cp_id_NF_coupon_month_mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2012-09-03</td>\n",
              "      <td>711</td>\n",
              "      <td>49009</td>\n",
              "      <td>1</td>\n",
              "      <td>35.62</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>35.62</td>\n",
              "      <td>70.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2013-01-12</td>\n",
              "      <td>435</td>\n",
              "      <td>45529</td>\n",
              "      <td>1</td>\n",
              "      <td>71.24</td>\n",
              "      <td>-35.26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1475</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>29</td>\n",
              "      <td>71.24</td>\n",
              "      <td>35.98</td>\n",
              "      <td>-35.26</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2012-01-17</td>\n",
              "      <td>1466</td>\n",
              "      <td>12901</td>\n",
              "      <td>1</td>\n",
              "      <td>89.05</td>\n",
              "      <td>-21.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2084</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>89.05</td>\n",
              "      <td>68.03</td>\n",
              "      <td>-21.02</td>\n",
              "      <td>2523</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2012-07-06</td>\n",
              "      <td>1064</td>\n",
              "      <td>59117</td>\n",
              "      <td>1</td>\n",
              "      <td>106.86</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>278</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>106.86</td>\n",
              "      <td>78.72</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>2012-11-23</td>\n",
              "      <td>140</td>\n",
              "      <td>51242</td>\n",
              "      <td>1</td>\n",
              "      <td>177.74</td>\n",
              "      <td>-35.62</td>\n",
              "      <td>0.0</td>\n",
              "      <td>544</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>26</td>\n",
              "      <td>177.74</td>\n",
              "      <td>142.12</td>\n",
              "      <td>-35.62</td>\n",
              "      <td>5017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   coupon_id  ... agg_cp_id_NF_coupon_month_mode\n",
              "0        0.0  ...                              7\n",
              "1        1.0  ...                             19\n",
              "2        2.0  ...                           2523\n",
              "3        3.0  ...                           3718\n",
              "4        4.0  ...                           5017\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1EURrIJ28Vk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train_df , df_coupon_id_groupby_mode ,on='coupon_id',how='left')\n",
        "test_df = pd.merge(test_df , df_coupon_id_groupby_mode ,on='coupon_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_DU8UsabN2L",
        "colab_type": "text"
      },
      "source": [
        "## Aggrigate with customer_id and coupon_id combine\n",
        "\n",
        "Note : Created Additional Mode Feature apart from Part 1 Solution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiGsxfUFbL6o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "['mean','sum','min','max','median','quantile','mad','std','count','nunique']\n",
        "ag_cat=['count','nunique','min','max','last']\n",
        "ag_num=['mean','sum','min','max','median','quantile','mad','std']\n",
        "\n",
        "agg_func_2 = {     # find the sum of the durations for each group\n",
        "                                   # 'customer_id' : ag_cat,\n",
        "                                    'quantity':ag_num,\n",
        "                                    'selling_price': ag_num,\n",
        "                                   # 'other_discount':ag_num,\n",
        "                                   # 'coupon_discount':ag_num,\n",
        "                                    'brand':ag_cat,\n",
        "                                    'brand_type':ag_cat,\n",
        "                                    'category':ag_cat,\n",
        "                                    'item_id':ag_cat,\n",
        "                                    #'C_date_year':ag_cat,\n",
        "                                    'C_date_month':ag_cat,\n",
        "                                    'C_date_day':ag_cat,\n",
        "                                    #'per_item_price':ag_num,\n",
        "                                    #'item_price_after_discount':ag_num\n",
        "                                    'NF_coupon_month':ag_cat\n",
        "                                }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGAamyChbYQv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_cust_cp_id_groupby = df.groupby(by=['customer_id','coupon_id']).agg(agg_func_2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRu_sWOrbaDA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_cust_cp_id_groupby.columns=['agg_cust_cp_id_' + '_'.join(col).strip() for col in df_cust_cp_id_groupby.columns.values]\n",
        "df_cust_cp_id_groupby.reset_index(inplace=True)\n",
        "train_df = pd.merge(train_df , df_cust_cp_id_groupby ,on=['customer_id','coupon_id'],how='left')\n",
        "test_df = pd.merge(test_df , df_cust_cp_id_groupby ,on=['customer_id','coupon_id'],how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZepbOVI9K3O",
        "colab_type": "code",
        "outputId": "40e6694a-f1a5-41f0-9afb-64b574252716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "# Add Mode Values\n",
        "\n",
        "import scipy.stats\n",
        "df_cust_cp_id_groupby_mode = df.groupby(by=['customer_id','coupon_id']).agg(lambda x: scipy.stats.mode(x)[0][0])\n",
        "df_cust_cp_id_groupby_mode.columns= ['agg_cust_cp_id_' + col +'_mode' for col in df_cust_cp_id_groupby_mode.columns.values]\n",
        "df_cust_cp_id_groupby_mode.reset_index(inplace=True)\n",
        "df_cust_cp_id_groupby_mode.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_id</th>\n",
              "      <th>coupon_id</th>\n",
              "      <th>agg_cust_cp_id_date_mode</th>\n",
              "      <th>agg_cust_cp_id_item_id_mode</th>\n",
              "      <th>agg_cust_cp_id_quantity_mode</th>\n",
              "      <th>agg_cust_cp_id_selling_price_mode</th>\n",
              "      <th>agg_cust_cp_id_other_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_coupon_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_brand_mode</th>\n",
              "      <th>agg_cust_cp_id_brand_type_mode</th>\n",
              "      <th>agg_cust_cp_id_category_mode</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_mode</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_mode</th>\n",
              "      <th>agg_cust_cp_id_per_item_price_mode</th>\n",
              "      <th>agg_cust_cp_id_item_price_after_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2012-08-12</td>\n",
              "      <td>8307</td>\n",
              "      <td>1</td>\n",
              "      <td>106.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>106.50</td>\n",
              "      <td>106.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2012-10-18</td>\n",
              "      <td>59842</td>\n",
              "      <td>1</td>\n",
              "      <td>71.24</td>\n",
              "      <td>-35.26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1475</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "      <td>71.24</td>\n",
              "      <td>35.98</td>\n",
              "      <td>-35.26</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2012-06-19</td>\n",
              "      <td>30818</td>\n",
              "      <td>1</td>\n",
              "      <td>44.52</td>\n",
              "      <td>-19.23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>946</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>44.52</td>\n",
              "      <td>32.41</td>\n",
              "      <td>-19.23</td>\n",
              "      <td>7408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2012-06-14</td>\n",
              "      <td>8539</td>\n",
              "      <td>1</td>\n",
              "      <td>89.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1487</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>89.05</td>\n",
              "      <td>71.60</td>\n",
              "      <td>0.00</td>\n",
              "      <td>8644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2012-02-21</td>\n",
              "      <td>17047</td>\n",
              "      <td>1</td>\n",
              "      <td>142.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1898</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>89.05</td>\n",
              "      <td>142.12</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9832</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   customer_id  ...  agg_cust_cp_id_NF_coupon_month_mode\n",
              "0            1  ...                                    7\n",
              "1            1  ...                                   13\n",
              "2            1  ...                                 7408\n",
              "3            1  ...                                 8644\n",
              "4            1  ...                                 9832\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZMJHdY09l8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train_df , df_cust_cp_id_groupby_mode ,on=['customer_id','coupon_id'],how='left')\n",
        "test_df = pd.merge(test_df , df_cust_cp_id_groupby_mode ,on=['customer_id','coupon_id'],how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxJUeVUNbhQu",
        "colab_type": "text"
      },
      "source": [
        "## Changes in Train and Test DF\n",
        "\n",
        "Note : Created additional feature to find difference between campaign start date to transaction date"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tf5roZrubfkw",
        "colab_type": "code",
        "outputId": "85170b11-12ae-4a95-b484-346d012c4a6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "# Creating New Feature of Trasaction date to campain date\n",
        "train_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>campaign_id</th>\n",
              "      <th>coupon_id</th>\n",
              "      <th>customer_id</th>\n",
              "      <th>redemption_status</th>\n",
              "      <th>campaign_type</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>age_range</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>rented</th>\n",
              "      <th>family_size</th>\n",
              "      <th>no_of_children</th>\n",
              "      <th>income_bracket</th>\n",
              "      <th>agg_cust_id_item_id_count</th>\n",
              "      <th>agg_cust_id_item_id_nunique</th>\n",
              "      <th>agg_cust_id_item_id_min</th>\n",
              "      <th>agg_cust_id_item_id_max</th>\n",
              "      <th>agg_cust_id_item_id_last</th>\n",
              "      <th>agg_cust_id_quantity_mean</th>\n",
              "      <th>agg_cust_id_quantity_sum</th>\n",
              "      <th>agg_cust_id_quantity_min</th>\n",
              "      <th>agg_cust_id_quantity_max</th>\n",
              "      <th>agg_cust_id_quantity_median</th>\n",
              "      <th>agg_cust_id_quantity_quantile</th>\n",
              "      <th>agg_cust_id_quantity_mad</th>\n",
              "      <th>agg_cust_id_quantity_std</th>\n",
              "      <th>agg_cust_id_selling_price_mean</th>\n",
              "      <th>agg_cust_id_selling_price_sum</th>\n",
              "      <th>agg_cust_id_selling_price_min</th>\n",
              "      <th>agg_cust_id_selling_price_max</th>\n",
              "      <th>agg_cust_id_selling_price_median</th>\n",
              "      <th>agg_cust_id_selling_price_quantile</th>\n",
              "      <th>agg_cust_id_selling_price_mad</th>\n",
              "      <th>agg_cust_id_selling_price_std</th>\n",
              "      <th>agg_cust_id_other_discount_mean</th>\n",
              "      <th>agg_cust_id_other_discount_sum</th>\n",
              "      <th>agg_cust_id_other_discount_min</th>\n",
              "      <th>agg_cust_id_other_discount_max</th>\n",
              "      <th>agg_cust_id_other_discount_median</th>\n",
              "      <th>...</th>\n",
              "      <th>agg_cust_cp_id_category_count</th>\n",
              "      <th>agg_cust_cp_id_category_nunique</th>\n",
              "      <th>agg_cust_cp_id_category_min</th>\n",
              "      <th>agg_cust_cp_id_category_max</th>\n",
              "      <th>agg_cust_cp_id_category_last</th>\n",
              "      <th>agg_cust_cp_id_item_id_count</th>\n",
              "      <th>agg_cust_cp_id_item_id_nunique</th>\n",
              "      <th>agg_cust_cp_id_item_id_min</th>\n",
              "      <th>agg_cust_cp_id_item_id_max</th>\n",
              "      <th>agg_cust_cp_id_item_id_last</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_count</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_nunique</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_min</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_max</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_last</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_count</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_nunique</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_min</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_max</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_last</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_count</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_nunique</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_min</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_max</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_last</th>\n",
              "      <th>agg_cust_cp_id_date_mode</th>\n",
              "      <th>agg_cust_cp_id_item_id_mode</th>\n",
              "      <th>agg_cust_cp_id_quantity_mode</th>\n",
              "      <th>agg_cust_cp_id_selling_price_mode</th>\n",
              "      <th>agg_cust_cp_id_other_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_coupon_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_brand_mode</th>\n",
              "      <th>agg_cust_cp_id_brand_type_mode</th>\n",
              "      <th>agg_cust_cp_id_category_mode</th>\n",
              "      <th>agg_cust_cp_id_C_date_month_mode</th>\n",
              "      <th>agg_cust_cp_id_C_date_day_mode</th>\n",
              "      <th>agg_cust_cp_id_per_item_price_mode</th>\n",
              "      <th>agg_cust_cp_id_item_price_after_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_discount_mode</th>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>27</td>\n",
              "      <td>1053</td>\n",
              "      <td>0</td>\n",
              "      <td>X</td>\n",
              "      <td>19/05/13</td>\n",
              "      <td>05/07/13</td>\n",
              "      <td>46-55</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>610</td>\n",
              "      <td>208</td>\n",
              "      <td>4308</td>\n",
              "      <td>73371</td>\n",
              "      <td>49009</td>\n",
              "      <td>173.785246</td>\n",
              "      <td>106009</td>\n",
              "      <td>1</td>\n",
              "      <td>12444</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>336.017119</td>\n",
              "      <td>1159.978943</td>\n",
              "      <td>165.690918</td>\n",
              "      <td>101071.46</td>\n",
              "      <td>17.45</td>\n",
              "      <td>5164.54</td>\n",
              "      <td>71.24</td>\n",
              "      <td>71.24</td>\n",
              "      <td>143.999325</td>\n",
              "      <td>368.818706</td>\n",
              "      <td>-37.773820</td>\n",
              "      <td>-23042.03</td>\n",
              "      <td>-890.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-10.69</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>13</td>\n",
              "      <td>116</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>X</td>\n",
              "      <td>19/05/13</td>\n",
              "      <td>05/07/13</td>\n",
              "      <td>36-45</td>\n",
              "      <td>Married</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>846</td>\n",
              "      <td>244</td>\n",
              "      <td>4525</td>\n",
              "      <td>71973</td>\n",
              "      <td>64341</td>\n",
              "      <td>15.053191</td>\n",
              "      <td>12735</td>\n",
              "      <td>1</td>\n",
              "      <td>11586</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.354484</td>\n",
              "      <td>398.289120</td>\n",
              "      <td>165.157400</td>\n",
              "      <td>139723.16</td>\n",
              "      <td>7.12</td>\n",
              "      <td>1758.92</td>\n",
              "      <td>89.05</td>\n",
              "      <td>89.05</td>\n",
              "      <td>133.840650</td>\n",
              "      <td>281.595271</td>\n",
              "      <td>-28.870816</td>\n",
              "      <td>-24424.71</td>\n",
              "      <td>-372.23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-10.69</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>635</td>\n",
              "      <td>205</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>11/03/13</td>\n",
              "      <td>12/04/13</td>\n",
              "      <td>46-55</td>\n",
              "      <td>Married</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2016</td>\n",
              "      <td>533</td>\n",
              "      <td>4368</td>\n",
              "      <td>73695</td>\n",
              "      <td>30289</td>\n",
              "      <td>1.426091</td>\n",
              "      <td>2875</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.634910</td>\n",
              "      <td>1.021600</td>\n",
              "      <td>120.835823</td>\n",
              "      <td>243605.02</td>\n",
              "      <td>17.45</td>\n",
              "      <td>1056.13</td>\n",
              "      <td>99.38</td>\n",
              "      <td>99.38</td>\n",
              "      <td>61.183193</td>\n",
              "      <td>100.879205</td>\n",
              "      <td>-19.046171</td>\n",
              "      <td>-38397.08</td>\n",
              "      <td>-468.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-3.56</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>644</td>\n",
              "      <td>1050</td>\n",
              "      <td>0</td>\n",
              "      <td>X</td>\n",
              "      <td>19/05/13</td>\n",
              "      <td>05/07/13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>454</td>\n",
              "      <td>216</td>\n",
              "      <td>4740</td>\n",
              "      <td>71849</td>\n",
              "      <td>29771</td>\n",
              "      <td>1.325991</td>\n",
              "      <td>602</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.515554</td>\n",
              "      <td>0.748440</td>\n",
              "      <td>98.992401</td>\n",
              "      <td>44942.55</td>\n",
              "      <td>13.89</td>\n",
              "      <td>708.48</td>\n",
              "      <td>71.24</td>\n",
              "      <td>71.24</td>\n",
              "      <td>62.803231</td>\n",
              "      <td>96.414604</td>\n",
              "      <td>-18.019978</td>\n",
              "      <td>-8181.07</td>\n",
              "      <td>-312.03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-7.12</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>1017</td>\n",
              "      <td>1489</td>\n",
              "      <td>0</td>\n",
              "      <td>X</td>\n",
              "      <td>16/02/13</td>\n",
              "      <td>05/04/13</td>\n",
              "      <td>46-55</td>\n",
              "      <td>Married</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1000</td>\n",
              "      <td>327</td>\n",
              "      <td>4308</td>\n",
              "      <td>73135</td>\n",
              "      <td>46442</td>\n",
              "      <td>139.702000</td>\n",
              "      <td>139702</td>\n",
              "      <td>1</td>\n",
              "      <td>15420</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>272.294344</td>\n",
              "      <td>1241.859979</td>\n",
              "      <td>111.164700</td>\n",
              "      <td>111164.70</td>\n",
              "      <td>8.90</td>\n",
              "      <td>1424.80</td>\n",
              "      <td>71.24</td>\n",
              "      <td>71.24</td>\n",
              "      <td>70.674769</td>\n",
              "      <td>138.614915</td>\n",
              "      <td>-22.526890</td>\n",
              "      <td>-22526.89</td>\n",
              "      <td>-320.22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-7.12</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 256 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ...  agg_cust_cp_id_NF_coupon_month_mode\n",
              "0   1  ...                                  NaN\n",
              "1   2  ...                                  NaN\n",
              "2   6  ...                                  NaN\n",
              "3   7  ...                                  NaN\n",
              "4   9  ...                                  NaN\n",
              "\n",
              "[5 rows x 256 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvD6lwszbuLW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['start_date'] = pd.to_datetime(train_df['start_date'])\n",
        "test_df['start_date'] = pd.to_datetime(test_df['start_date'])\n",
        "\n",
        "train_df['end_date'] = pd.to_datetime(train_df['end_date'])\n",
        "test_df['end_date'] = pd.to_datetime(test_df['end_date'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4-qOqRZ-NKv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['agg_cust_id_date_mode'] = pd.to_datetime(train_df['agg_cust_id_date_mode'])\n",
        "train_df['agg_cust_cp_id_date_mode'] = pd.to_datetime(train_df['agg_cust_cp_id_date_mode'])\n",
        "train_df['agg_cp_id_date_mode'] = pd.to_datetime(train_df['agg_cp_id_date_mode'])\n",
        "\n",
        "test_df['agg_cust_id_date_mode'] = pd.to_datetime(test_df['agg_cust_id_date_mode'])\n",
        "test_df['agg_cust_cp_id_date_mode'] = pd.to_datetime(test_df['agg_cust_cp_id_date_mode'])\n",
        "test_df['agg_cp_id_date_mode'] = pd.to_datetime(test_df['agg_cp_id_date_mode'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DTNJ8kKcIOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train_df['start_date_year'] = train_df['start_date'].dt.year\n",
        "train_df['start_date_month'] = train_df['start_date'].dt.month\n",
        "train_df['start_date_day'] = train_df['start_date'].dt.day\n",
        "\n",
        "#test_df['start_date_year'] = test_df['start_date'].dt.year\n",
        "test_df['start_date_month'] = test_df['start_date'].dt.month\n",
        "test_df['start_date_day'] = test_df['start_date'].dt.day"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wKCD_Yf4cJ5O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train_df['end_date_year'] = train_df['end_date'].dt.year\n",
        "train_df['end_date_month'] = train_df['end_date'].dt.month\n",
        "train_df['end_date_day'] = train_df['end_date'].dt.day\n",
        "\n",
        "#test_df['end_date_year'] = test_df['end_date'].dt.year\n",
        "test_df['end_date_month'] = test_df['end_date'].dt.month\n",
        "test_df['end_date_day'] = test_df['end_date'].dt.day"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hv5IbuIm_KTl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['agg_cust_id_date_mode_month'] = train_df['agg_cust_id_date_mode'].dt.month\n",
        "train_df['agg_cust_cp_id_date_mode_month'] = train_df['agg_cust_cp_id_date_mode'].dt.month\n",
        "train_df['agg_cp_id_date_mode_month'] = train_df['agg_cp_id_date_mode'].dt.month\n",
        "\n",
        "train_df['agg_cust_id_date_mode_day'] = train_df['agg_cust_id_date_mode'].dt.day\n",
        "train_df['agg_cust_cp_id_date_mode_day'] = train_df['agg_cust_cp_id_date_mode'].dt.day\n",
        "train_df['agg_cp_id_date_mode_day'] = train_df['agg_cp_id_date_mode'].dt.day\n",
        "\n",
        "test_df['agg_cust_id_date_mode_month'] = test_df['agg_cust_id_date_mode'].dt.month\n",
        "test_df['agg_cust_cp_id_date_mode_month'] = test_df['agg_cust_cp_id_date_mode'].dt.month\n",
        "test_df['agg_cp_id_date_mode_month'] = test_df['agg_cp_id_date_mode'].dt.month\n",
        "\n",
        "test_df['agg_cust_id_date_mode_day'] = test_df['agg_cust_id_date_mode'].dt.day\n",
        "test_df['agg_cust_cp_id_date_mode_day'] = test_df['agg_cust_cp_id_date_mode'].dt.day\n",
        "test_df['agg_cp_id_date_mode_day'] = test_df['agg_cp_id_date_mode'].dt.day"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zf20_KPW_J_S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['agg_cust_id_date_mode_diff'] = (train_df['agg_cust_id_date_mode']  - train_df['start_date'])/np.timedelta64(1,'D')\n",
        "train_df['agg_cust_cp_id_date_mode_diff'] = (train_df['agg_cust_cp_id_date_mode']  - train_df['start_date'])/np.timedelta64(1,'D')\n",
        "train_df['agg_cp_id_date_mode_diff'] = (train_df['agg_cp_id_date_mode']  - train_df['start_date'])/np.timedelta64(1,'D')\n",
        "\n",
        "test_df['agg_cust_id_date_mode_diff'] = (test_df['agg_cust_id_date_mode']  - test_df['start_date'])/np.timedelta64(1,'D')\n",
        "test_df['agg_cust_cp_id_date_mode_diff'] = (test_df['agg_cust_cp_id_date_mode']  - test_df['start_date'])/np.timedelta64(1,'D')\n",
        "test_df['agg_cp_id_date_mode_diff'] = (test_df['agg_cp_id_date_mode']  - test_df['start_date'])/np.timedelta64(1,'D')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwGYPs2YcMBQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['campaign_run'] = (train_df['end_date']  - train_df['start_date'])/np.timedelta64(1,'D')\n",
        "test_df['campaign_run'] = (test_df['end_date']  - test_df['start_date'])/ np.timedelta64(1,'D')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cyPF2UOJcSVO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.drop(['start_date','end_date','no_of_children','id','agg_cust_cp_id_date_mode'],axis=1)\n",
        "test_df = test_df.drop(['start_date','end_date','no_of_children','id','agg_cust_cp_id_date_mode'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sFvZlttiJzG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for f in train_df.columns:\n",
        "    if  train_df[f].dtype=='object': \n",
        "        lbl = preprocessing.LabelEncoder()\n",
        "        lbl.fit(list(train_df[f].values))\n",
        "        train_df[f] = lbl.transform(list(train_df[f].values))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XyLg9-5ajSmj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for f in test_df.columns:\n",
        "    if  test_df[f].dtype=='object': \n",
        "        lbl = preprocessing.LabelEncoder()\n",
        "        lbl.fit(list(test_df[f].values))\n",
        "        test_df[f] = lbl.transform(list(test_df[f].values))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwfwvWRAjUNy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Function to calculate missing values by column# Funct \n",
        "def missing_values_table(df):\n",
        "        # Total missing values\n",
        "        mis_val = df.isnull().sum()\n",
        "        \n",
        "        # Percentage of missing values\n",
        "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
        "        \n",
        "        # Make a table with the results\n",
        "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
        "        \n",
        "        # Rename the columns\n",
        "        mis_val_table_ren_columns = mis_val_table.rename(\n",
        "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
        "        \n",
        "        # Sort the table by percentage of missing descending\n",
        "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
        "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
        "        '% of Total Values', ascending=False).round(1)\n",
        "        \n",
        "        # Print some summary information\n",
        "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
        "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
        "              \" columns that have missing values.\")\n",
        "        \n",
        "        # Return the dataframe with missing information\n",
        "        return mis_val_table_ren_columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npM_gARWjWJ2",
        "colab_type": "code",
        "outputId": "9954a6e3-d9f4-4b28-b343-5213a4fb591b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# Missing values statistics of Customers\n",
        "missing_values = missing_values_table(train_df)\n",
        "missing_values.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 259 columns.\n",
            "There are 76 columns that have missing values.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_selling_price_std</th>\n",
              "      <td>69506</td>\n",
              "      <td>88.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_quantity_std</th>\n",
              "      <td>69506</td>\n",
              "      <td>88.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_category_last</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_max</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_nunique</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_count</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_last</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_max</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_min</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_nunique</th>\n",
              "      <td>64489</td>\n",
              "      <td>82.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Missing Values  % of Total Values\n",
              "agg_cust_cp_id_selling_price_std                 69506               88.7\n",
              "agg_cust_cp_id_quantity_std                      69506               88.7\n",
              "agg_cust_cp_id_category_last                     64489               82.3\n",
              "agg_cust_cp_id_NF_coupon_month_max               64489               82.3\n",
              "agg_cust_cp_id_NF_coupon_month_nunique           64489               82.3\n",
              "agg_cust_cp_id_NF_coupon_month_count             64489               82.3\n",
              "agg_cust_cp_id_C_date_day_last                   64489               82.3\n",
              "agg_cust_cp_id_C_date_day_max                    64489               82.3\n",
              "agg_cust_cp_id_C_date_day_min                    64489               82.3\n",
              "agg_cust_cp_id_C_date_day_nunique                64489               82.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBG8Cxv8jXiF",
        "colab_type": "code",
        "outputId": "1ff3d86e-c0ff-46f5-cc53-4e2e93bad8d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "# Missing values statistics of Customers\n",
        "missing_values = missing_values_table(test_df)\n",
        "missing_values.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 258 columns.\n",
            "There are 76 columns that have missing values.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_selling_price_std</th>\n",
              "      <td>41686</td>\n",
              "      <td>83.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_quantity_std</th>\n",
              "      <td>41686</td>\n",
              "      <td>83.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_category_last</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_max</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_nunique</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_NF_coupon_month_count</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_last</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_max</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_min</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agg_cust_cp_id_C_date_day_nunique</th>\n",
              "      <td>37305</td>\n",
              "      <td>74.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Missing Values  % of Total Values\n",
              "agg_cust_cp_id_selling_price_std                 41686               83.0\n",
              "agg_cust_cp_id_quantity_std                      41686               83.0\n",
              "agg_cust_cp_id_category_last                     37305               74.3\n",
              "agg_cust_cp_id_NF_coupon_month_max               37305               74.3\n",
              "agg_cust_cp_id_NF_coupon_month_nunique           37305               74.3\n",
              "agg_cust_cp_id_NF_coupon_month_count             37305               74.3\n",
              "agg_cust_cp_id_C_date_day_last                   37305               74.3\n",
              "agg_cust_cp_id_C_date_day_max                    37305               74.3\n",
              "agg_cust_cp_id_C_date_day_min                    37305               74.3\n",
              "agg_cust_cp_id_C_date_day_nunique                37305               74.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Cw-IALLjh_Y",
        "colab_type": "text"
      },
      "source": [
        "## Lets Make aggrigate features for campain ID"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yNobufkje8u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target = train_df['redemption_status']\n",
        "train_df=train_df.drop('redemption_status',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gfk8L7mvGF6h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "frames = [train_df,test_df]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G8ynFA7tGF3w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_df = pd.concat(frames,join='inner',ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_m0DWnlGF09",
        "colab_type": "code",
        "outputId": "e5010e37-2a84-4056-8c33-ffa3b7b55090",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "all_df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128595, 258)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t_oPqAvoGFxU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "['mean','sum','min','max','median','quantile','mad','std','count','nunique']\n",
        "ag_cat=['sum', 'mean','count','nunique','min','max']\n",
        "ag_num=['mean','sum','min','max','median','quantile','mad','std']\n",
        "agg_func4 = {\n",
        "    'coupon_id':ag_cat,\n",
        "'customer_id':ag_cat,\n",
        "'campaign_type':ag_cat,\n",
        "'start_date_month':ag_cat,\n",
        "'start_date_day':ag_cat,\n",
        "'campaign_run':ag_num,\n",
        "'age_range':ag_cat,\n",
        "'marital_status':ag_cat,\n",
        "'rented':ag_cat,\n",
        "'family_size':ag_cat,\n",
        "#'no_of_children':ag_cat,\n",
        "'income_bracket':ag_cat\n",
        "    }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVvqZHzTIH8p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "campaign_id_groupby = all_df.groupby(by=['campaign_id']).agg(agg_func4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpsYRIjkIH54",
        "colab_type": "code",
        "outputId": "a7af4a5a-4a7c-42ca-be00-f6514518ed70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        }
      },
      "source": [
        "campaign_id_groupby.columns=['agg_campaign_id' + '_'.join(col).strip() for col in campaign_id_groupby.columns.values]\n",
        "campaign_id_groupby.reset_index(inplace=True)\n",
        "campaign_id_groupby.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>campaign_id</th>\n",
              "      <th>agg_campaign_idcoupon_id_sum</th>\n",
              "      <th>agg_campaign_idcoupon_id_mean</th>\n",
              "      <th>agg_campaign_idcoupon_id_count</th>\n",
              "      <th>agg_campaign_idcoupon_id_nunique</th>\n",
              "      <th>agg_campaign_idcoupon_id_min</th>\n",
              "      <th>agg_campaign_idcoupon_id_max</th>\n",
              "      <th>agg_campaign_idcustomer_id_sum</th>\n",
              "      <th>agg_campaign_idcustomer_id_mean</th>\n",
              "      <th>agg_campaign_idcustomer_id_count</th>\n",
              "      <th>agg_campaign_idcustomer_id_nunique</th>\n",
              "      <th>agg_campaign_idcustomer_id_min</th>\n",
              "      <th>agg_campaign_idcustomer_id_max</th>\n",
              "      <th>agg_campaign_idcampaign_type_sum</th>\n",
              "      <th>agg_campaign_idcampaign_type_mean</th>\n",
              "      <th>agg_campaign_idcampaign_type_count</th>\n",
              "      <th>agg_campaign_idcampaign_type_nunique</th>\n",
              "      <th>agg_campaign_idcampaign_type_min</th>\n",
              "      <th>agg_campaign_idcampaign_type_max</th>\n",
              "      <th>agg_campaign_idstart_date_month_sum</th>\n",
              "      <th>agg_campaign_idstart_date_month_mean</th>\n",
              "      <th>agg_campaign_idstart_date_month_count</th>\n",
              "      <th>agg_campaign_idstart_date_month_nunique</th>\n",
              "      <th>agg_campaign_idstart_date_month_min</th>\n",
              "      <th>agg_campaign_idstart_date_month_max</th>\n",
              "      <th>agg_campaign_idstart_date_day_sum</th>\n",
              "      <th>agg_campaign_idstart_date_day_mean</th>\n",
              "      <th>agg_campaign_idstart_date_day_count</th>\n",
              "      <th>agg_campaign_idstart_date_day_nunique</th>\n",
              "      <th>agg_campaign_idstart_date_day_min</th>\n",
              "      <th>agg_campaign_idstart_date_day_max</th>\n",
              "      <th>agg_campaign_idcampaign_run_mean</th>\n",
              "      <th>agg_campaign_idcampaign_run_sum</th>\n",
              "      <th>agg_campaign_idcampaign_run_min</th>\n",
              "      <th>agg_campaign_idcampaign_run_max</th>\n",
              "      <th>agg_campaign_idcampaign_run_median</th>\n",
              "      <th>agg_campaign_idcampaign_run_quantile</th>\n",
              "      <th>agg_campaign_idcampaign_run_mad</th>\n",
              "      <th>agg_campaign_idcampaign_run_std</th>\n",
              "      <th>agg_campaign_idage_range_sum</th>\n",
              "      <th>agg_campaign_idage_range_mean</th>\n",
              "      <th>agg_campaign_idage_range_count</th>\n",
              "      <th>agg_campaign_idage_range_nunique</th>\n",
              "      <th>agg_campaign_idage_range_min</th>\n",
              "      <th>agg_campaign_idage_range_max</th>\n",
              "      <th>agg_campaign_idmarital_status_sum</th>\n",
              "      <th>agg_campaign_idmarital_status_mean</th>\n",
              "      <th>agg_campaign_idmarital_status_count</th>\n",
              "      <th>agg_campaign_idmarital_status_nunique</th>\n",
              "      <th>agg_campaign_idmarital_status_min</th>\n",
              "      <th>agg_campaign_idmarital_status_max</th>\n",
              "      <th>agg_campaign_idrented_sum</th>\n",
              "      <th>agg_campaign_idrented_mean</th>\n",
              "      <th>agg_campaign_idrented_count</th>\n",
              "      <th>agg_campaign_idrented_nunique</th>\n",
              "      <th>agg_campaign_idrented_min</th>\n",
              "      <th>agg_campaign_idrented_max</th>\n",
              "      <th>agg_campaign_idfamily_size_sum</th>\n",
              "      <th>agg_campaign_idfamily_size_mean</th>\n",
              "      <th>agg_campaign_idfamily_size_count</th>\n",
              "      <th>agg_campaign_idfamily_size_nunique</th>\n",
              "      <th>agg_campaign_idfamily_size_min</th>\n",
              "      <th>agg_campaign_idfamily_size_max</th>\n",
              "      <th>agg_campaign_idincome_bracket_sum</th>\n",
              "      <th>agg_campaign_idincome_bracket_mean</th>\n",
              "      <th>agg_campaign_idincome_bracket_count</th>\n",
              "      <th>agg_campaign_idincome_bracket_nunique</th>\n",
              "      <th>agg_campaign_idincome_bracket_min</th>\n",
              "      <th>agg_campaign_idincome_bracket_max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>97448</td>\n",
              "      <td>681.454545</td>\n",
              "      <td>143</td>\n",
              "      <td>11</td>\n",
              "      <td>312</td>\n",
              "      <td>1112</td>\n",
              "      <td>69432</td>\n",
              "      <td>485.538462</td>\n",
              "      <td>143</td>\n",
              "      <td>13</td>\n",
              "      <td>46</td>\n",
              "      <td>1387</td>\n",
              "      <td>143</td>\n",
              "      <td>1</td>\n",
              "      <td>143</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1716</td>\n",
              "      <td>12</td>\n",
              "      <td>143</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1716</td>\n",
              "      <td>12</td>\n",
              "      <td>143</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>37.0</td>\n",
              "      <td>5291.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>561</td>\n",
              "      <td>3.923077</td>\n",
              "      <td>143</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>253</td>\n",
              "      <td>1.769231</td>\n",
              "      <td>143</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>440</td>\n",
              "      <td>3.076923</td>\n",
              "      <td>143</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>165.0</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>66</td>\n",
              "      <td>4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>564528</td>\n",
              "      <td>735.062500</td>\n",
              "      <td>768</td>\n",
              "      <td>16</td>\n",
              "      <td>347</td>\n",
              "      <td>1107</td>\n",
              "      <td>580160</td>\n",
              "      <td>755.416667</td>\n",
              "      <td>768</td>\n",
              "      <td>48</td>\n",
              "      <td>10</td>\n",
              "      <td>1555</td>\n",
              "      <td>768</td>\n",
              "      <td>1</td>\n",
              "      <td>768</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9216</td>\n",
              "      <td>12</td>\n",
              "      <td>768</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>13056</td>\n",
              "      <td>17</td>\n",
              "      <td>768</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>32.0</td>\n",
              "      <td>24576.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3600</td>\n",
              "      <td>4.687500</td>\n",
              "      <td>768</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1232</td>\n",
              "      <td>1.604167</td>\n",
              "      <td>768</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>112.0</td>\n",
              "      <td>0.368421</td>\n",
              "      <td>304</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2608</td>\n",
              "      <td>3.395833</td>\n",
              "      <td>768</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1328.0</td>\n",
              "      <td>4.368421</td>\n",
              "      <td>304</td>\n",
              "      <td>8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>255624</td>\n",
              "      <td>626.529412</td>\n",
              "      <td>408</td>\n",
              "      <td>34</td>\n",
              "      <td>263</td>\n",
              "      <td>864</td>\n",
              "      <td>342312</td>\n",
              "      <td>839.000000</td>\n",
              "      <td>408</td>\n",
              "      <td>12</td>\n",
              "      <td>67</td>\n",
              "      <td>1534</td>\n",
              "      <td>408</td>\n",
              "      <td>1</td>\n",
              "      <td>408</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4896</td>\n",
              "      <td>12</td>\n",
              "      <td>408</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>8976</td>\n",
              "      <td>22</td>\n",
              "      <td>408</td>\n",
              "      <td>1</td>\n",
              "      <td>22</td>\n",
              "      <td>22</td>\n",
              "      <td>56.0</td>\n",
              "      <td>22848.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1530</td>\n",
              "      <td>3.750000</td>\n",
              "      <td>408</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>578</td>\n",
              "      <td>1.416667</td>\n",
              "      <td>408</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>306</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>918</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>408</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>3.333333</td>\n",
              "      <td>306</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>572103</td>\n",
              "      <td>588.583333</td>\n",
              "      <td>972</td>\n",
              "      <td>12</td>\n",
              "      <td>16</td>\n",
              "      <td>935</td>\n",
              "      <td>725124</td>\n",
              "      <td>746.012346</td>\n",
              "      <td>972</td>\n",
              "      <td>81</td>\n",
              "      <td>7</td>\n",
              "      <td>1574</td>\n",
              "      <td>972</td>\n",
              "      <td>1</td>\n",
              "      <td>972</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>6804</td>\n",
              "      <td>7</td>\n",
              "      <td>972</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>972</td>\n",
              "      <td>1</td>\n",
              "      <td>972</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>32.0</td>\n",
              "      <td>31104.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3480</td>\n",
              "      <td>3.580247</td>\n",
              "      <td>972</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1152</td>\n",
              "      <td>1.185185</td>\n",
              "      <td>972</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.018182</td>\n",
              "      <td>660</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2400</td>\n",
              "      <td>2.469136</td>\n",
              "      <td>972</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3552.0</td>\n",
              "      <td>5.381818</td>\n",
              "      <td>660</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1397413</td>\n",
              "      <td>764.867542</td>\n",
              "      <td>1827</td>\n",
              "      <td>11</td>\n",
              "      <td>279</td>\n",
              "      <td>1105</td>\n",
              "      <td>1610410</td>\n",
              "      <td>881.450465</td>\n",
              "      <td>1827</td>\n",
              "      <td>166</td>\n",
              "      <td>7</td>\n",
              "      <td>1574</td>\n",
              "      <td>1827</td>\n",
              "      <td>1</td>\n",
              "      <td>1827</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>21924</td>\n",
              "      <td>12</td>\n",
              "      <td>1827</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1827</td>\n",
              "      <td>1</td>\n",
              "      <td>1827</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-289.0</td>\n",
              "      <td>-528003.0</td>\n",
              "      <td>-289.0</td>\n",
              "      <td>-289.0</td>\n",
              "      <td>-289.0</td>\n",
              "      <td>-289.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6657</td>\n",
              "      <td>3.643678</td>\n",
              "      <td>1827</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2630</td>\n",
              "      <td>1.439518</td>\n",
              "      <td>1827</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>99.0</td>\n",
              "      <td>0.089029</td>\n",
              "      <td>1112</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5292</td>\n",
              "      <td>2.896552</td>\n",
              "      <td>1827</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5427.0</td>\n",
              "      <td>4.880396</td>\n",
              "      <td>1112</td>\n",
              "      <td>10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   campaign_id  ...  agg_campaign_idincome_bracket_max\n",
              "0            1  ...                                6.0\n",
              "1            2  ...                               12.0\n",
              "2            3  ...                                5.0\n",
              "3            4  ...                               12.0\n",
              "4            5  ...                               10.0\n",
              "\n",
              "[5 rows x 69 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyH1_gSnIH26",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = pd.merge(train_df, campaign_id_groupby, on='campaign_id',how='left')\n",
        "test_df = pd.merge(test_df, campaign_id_groupby, on='campaign_id',how='left')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5U5mS-6I380",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df['redemption_status'] = target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FKxj1Mj1LEtm",
        "colab_type": "text"
      },
      "source": [
        "## Apply LGB\n",
        "\n",
        "Note : LGB and CatBooster didnt provided good result on LB but those score had 92.76 AUC on Private LB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIZFN8NXLF7a",
        "colab_type": "code",
        "outputId": "cfab429b-deb0-4cda-979f-281e60f228fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# Align train and test\n",
        "train_labels = train_df['redemption_status']\n",
        "\n",
        "# Align the training and testing data, keep only columns present in both dataframes\n",
        "train_df, test_df = train_df.align(test_df, join = 'inner', axis = 1)\n",
        "\n",
        "# Add the target back in\n",
        "train_df['redemption_status'] = train_labels\n",
        "\n",
        "print('Training Features shape: ', train_df.shape)\n",
        "print('Testing Features shape: ', test_df.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Features shape:  (78369, 327)\n",
            "Testing Features shape:  (50226, 326)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liUddXTALXZD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = train_df['redemption_status']\n",
        "X = train_df.drop('redemption_status',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chwMJwJXLaSw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,stratify=y.values,random_state=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLabfs7rLdHH",
        "colab_type": "text"
      },
      "source": [
        "## LGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcSRSl6CPTcm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "def lgb_f1_score(y_hat, data):\n",
        "    y_true = data.get_label()\n",
        "    y_hat = np.round(y_hat) # scikits f1 doesn't like probabilities\n",
        "    return 'f1', f1_score(y_true, y_hat), True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpE_o9KeLcAR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def run_lgb(X_train, X_test, y_train, y_test, test_df):\n",
        "    params = {\n",
        "        \"objective\" : \"binary\",\n",
        "       \"n_estimators\":10000,\n",
        "        \"learning_rate\" :0.007,\n",
        "        \"num_leaves\" :90,\n",
        "        \"min_data_in_leaf\":3,\n",
        "        'max_bin':200,\n",
        "        'max_depth':3,\n",
        "        \"metric\" : \"auc\",\n",
        "        \"reg_alpha\" :3,\n",
        "        \"reg_lambda\":5,\n",
        "        'feature_fraction':0.8999999999999121,\n",
        "        'min_gain_to_split':0.2,\n",
        "        'bagging_fraction':0.8999999999997461,\n",
        "        'bagging_freq':50,\n",
        "        #'scale_pos_weight':15.0\n",
        "        \n",
        "    }\n",
        "    \n",
        "    lgtrain = lgb.Dataset(X_train, label=y_train)\n",
        "    lgval = lgb.Dataset(X_test, label=y_test)\n",
        "    evals_result = {}\n",
        "    model = lgb.train(params, lgtrain, 10000, \n",
        "                      valid_sets=[lgtrain, lgval], \n",
        "                      early_stopping_rounds=50, \n",
        "                      verbose_eval=50, \n",
        "                      evals_result=evals_result)\n",
        "    \n",
        "    pred_test_y = model.predict(test_df, num_iteration=model.best_iteration)\n",
        "    return pred_test_y, model, evals_result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34jgrNHjLgRA",
        "colab_type": "code",
        "outputId": "60d46e8b-e5f1-4c3d-f19c-726d093401e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        }
      },
      "source": [
        "pred_test, model, evals_result = run_lgb(X_train, X_test, y_train, y_test, test_df)\n",
        "print(\"LightGBM Training Completed...\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 50 rounds.\n",
            "[50]\ttraining's auc: 0.994399\tvalid_1's auc: 0.994245\n",
            "[100]\ttraining's auc: 0.995106\tvalid_1's auc: 0.994934\n",
            "[150]\ttraining's auc: 0.996243\tvalid_1's auc: 0.995222\n",
            "[200]\ttraining's auc: 0.996484\tvalid_1's auc: 0.99547\n",
            "[250]\ttraining's auc: 0.996724\tvalid_1's auc: 0.995766\n",
            "[300]\ttraining's auc: 0.997028\tvalid_1's auc: 0.996007\n",
            "[350]\ttraining's auc: 0.997262\tvalid_1's auc: 0.996277\n",
            "[400]\ttraining's auc: 0.997457\tvalid_1's auc: 0.996518\n",
            "[450]\ttraining's auc: 0.997624\tvalid_1's auc: 0.996718\n",
            "[500]\ttraining's auc: 0.997725\tvalid_1's auc: 0.996769\n",
            "[550]\ttraining's auc: 0.99784\tvalid_1's auc: 0.996906\n",
            "[600]\ttraining's auc: 0.997937\tvalid_1's auc: 0.997026\n",
            "[650]\ttraining's auc: 0.998077\tvalid_1's auc: 0.997142\n",
            "[700]\ttraining's auc: 0.998199\tvalid_1's auc: 0.997266\n",
            "[750]\ttraining's auc: 0.998309\tvalid_1's auc: 0.997399\n",
            "[800]\ttraining's auc: 0.998391\tvalid_1's auc: 0.997498\n",
            "[850]\ttraining's auc: 0.998468\tvalid_1's auc: 0.997524\n",
            "[900]\ttraining's auc: 0.998548\tvalid_1's auc: 0.997567\n",
            "[950]\ttraining's auc: 0.998611\tvalid_1's auc: 0.997621\n",
            "[1000]\ttraining's auc: 0.998657\tvalid_1's auc: 0.997676\n",
            "[1050]\ttraining's auc: 0.998708\tvalid_1's auc: 0.997708\n",
            "[1100]\ttraining's auc: 0.998774\tvalid_1's auc: 0.997777\n",
            "[1150]\ttraining's auc: 0.998839\tvalid_1's auc: 0.997828\n",
            "[1200]\ttraining's auc: 0.998898\tvalid_1's auc: 0.997851\n",
            "[1250]\ttraining's auc: 0.998954\tvalid_1's auc: 0.997891\n",
            "[1300]\ttraining's auc: 0.998995\tvalid_1's auc: 0.997918\n",
            "[1350]\ttraining's auc: 0.999035\tvalid_1's auc: 0.997932\n",
            "[1400]\ttraining's auc: 0.999061\tvalid_1's auc: 0.997969\n",
            "[1450]\ttraining's auc: 0.9991\tvalid_1's auc: 0.998015\n",
            "[1500]\ttraining's auc: 0.999129\tvalid_1's auc: 0.998046\n",
            "[1550]\ttraining's auc: 0.99917\tvalid_1's auc: 0.998065\n",
            "[1600]\ttraining's auc: 0.999198\tvalid_1's auc: 0.998071\n",
            "[1650]\ttraining's auc: 0.99923\tvalid_1's auc: 0.99809\n",
            "[1700]\ttraining's auc: 0.999251\tvalid_1's auc: 0.998095\n",
            "[1750]\ttraining's auc: 0.999274\tvalid_1's auc: 0.998131\n",
            "[1800]\ttraining's auc: 0.999298\tvalid_1's auc: 0.998137\n",
            "[1850]\ttraining's auc: 0.999315\tvalid_1's auc: 0.99815\n",
            "[1900]\ttraining's auc: 0.999347\tvalid_1's auc: 0.998177\n",
            "[1950]\ttraining's auc: 0.999371\tvalid_1's auc: 0.99818\n",
            "[2000]\ttraining's auc: 0.999394\tvalid_1's auc: 0.99818\n",
            "Early stopping, best iteration is:\n",
            "[1953]\ttraining's auc: 0.999372\tvalid_1's auc: 0.998182\n",
            "LightGBM Training Completed...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMm694-qLiPf",
        "colab_type": "code",
        "outputId": "3014a362-478b-4f14-9f8a-5e2fce3105b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "source": [
        "feature_imp = pd.DataFrame(sorted(zip(model.feature_importance(),X_train.columns)), columns=['Value','Feature'])\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "# sorted(zip(clf.feature_importances_, X.columns), reverse=True)\n",
        "feature_imp = pd.DataFrame(sorted(zip(model.feature_importance(importance_type='gain'),X_train.columns)), columns=['Value','Feature'])\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False).head(50))\n",
        "plt.title('LightGBM Features (avg over folds)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8XY6WGA7sBWwEHChpM0nrsfL4Bqh2Odc+H9Vk7JuA4yQNAqg7umIxMLJcj651lrQd8KDt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\nJEmSJsQCsyRJkiRJkiRpQv5/IBsMvgYniPMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiMQiCRkLn3P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub = pd.read_csv('../content/Submission.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iu7p5gEgMQBH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub['redemption_status'] = pred_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDL06DsOMV0V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub.to_csv('LGB_Mode_Sub7.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NqDK4qNCO58w",
        "colab_type": "text"
      },
      "source": [
        "## Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zG_n5kGNO8Ic",
        "colab_type": "code",
        "outputId": "8a6b693c-25cc-478b-f8d7-490b5c6f141e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "y_pred = model.predict(X_test)\n",
        "confusion_matrix(y_test,y_pred.round())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19397,    13],\n",
              "       [   60,   122]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5sf7AkUkO8Fa",
        "colab_type": "code",
        "outputId": "768f06dd-86a4-4f11-9fd9-544ac958d704",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,y_pred.round()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     19410\n",
            "           1       0.90      0.67      0.77       182\n",
            "\n",
            "    accuracy                           1.00     19592\n",
            "   macro avg       0.95      0.83      0.88     19592\n",
            "weighted avg       1.00      1.00      1.00     19592\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ysuk2jdIPFg8",
        "colab_type": "text"
      },
      "source": [
        "## Kfold"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oecOGnfuMezS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {\n",
        "        \"objective\" : \"binary\",\n",
        "       \"n_estimators\":10000,\n",
        "       \"reg_alpha\" : 2.0,\n",
        "       \"reg_lambda\":2.1,\n",
        "       \"n_jobs\":-1,\n",
        "       \"colsample_bytree\":.8,\n",
        "       \"min_child_weight\":0.9,\n",
        "       \"subsample\":0.8715623,\n",
        "       \"min_data_in_leaf\":5,\n",
        "       \"nthread\":4,\n",
        "       \"metric\" : \"auc\",\n",
        "       \"num_leaves\" : 50,\n",
        "       \"learning_rate\" : 0.01,\n",
        "       \"verbosity\" : -1,\n",
        "       \"seed\": 120,\n",
        "       \"max_bin\":150,\n",
        "       'max_depth':3,\n",
        "       #'min_gain_to_split':.0222415,\n",
        "      # 'scale_pos_weight':1\n",
        "    }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Z7qXFw-RU1b",
        "colab_type": "code",
        "outputId": "c5eaab60-cbd0-4cc1-d7fb-69027de5f3bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix,roc_auc_score\n",
        "errcb=[]\n",
        "y_pred_totcb=[]\n",
        "from sklearn.model_selection import KFold,StratifiedKFold\n",
        "fold=StratifiedKFold(n_splits=4,shuffle=True,random_state=1994)\n",
        "i=1\n",
        "for train_index, test_index in fold.split(X,y):\n",
        "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
        "    y_train, y_test = y[train_index], y[test_index]\n",
        "    lgtrain = lgb.Dataset(X_train, label=y_train)\n",
        "    lgval = lgb.Dataset(X_test, label=y_test)\n",
        "    model = lgb.train(params, lgtrain, 10000, \n",
        "                      valid_sets=[lgtrain, lgval], \n",
        "                      early_stopping_rounds=100, \n",
        "                      verbose_eval=100)\n",
        "    preds = model.predict(X_test, num_iteration=model.best_iteration)\n",
        "    print(\"err: \",roc_auc_score(y_test,preds))\n",
        "    errcb.append(roc_auc_score(y_test,preds))\n",
        "    p = model.predict(test_df, num_iteration=model.best_iteration)\n",
        "    y_pred_totcb.append(p)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\ttraining's auc: 0.996357\tvalid_1's auc: 0.995476\n",
            "[200]\ttraining's auc: 0.997127\tvalid_1's auc: 0.996122\n",
            "[300]\ttraining's auc: 0.997761\tvalid_1's auc: 0.99673\n",
            "[400]\ttraining's auc: 0.998193\tvalid_1's auc: 0.997028\n",
            "[500]\ttraining's auc: 0.998492\tvalid_1's auc: 0.997201\n",
            "[600]\ttraining's auc: 0.998729\tvalid_1's auc: 0.997293\n",
            "[700]\ttraining's auc: 0.998919\tvalid_1's auc: 0.997343\n",
            "[800]\ttraining's auc: 0.99908\tvalid_1's auc: 0.997384\n",
            "[900]\ttraining's auc: 0.999213\tvalid_1's auc: 0.997423\n",
            "[1000]\ttraining's auc: 0.999329\tvalid_1's auc: 0.997426\n",
            "Early stopping, best iteration is:\n",
            "[975]\ttraining's auc: 0.999302\tvalid_1's auc: 0.997431\n",
            "err:  0.9974310464720174\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\ttraining's auc: 0.996376\tvalid_1's auc: 0.995257\n",
            "[200]\ttraining's auc: 0.997127\tvalid_1's auc: 0.995841\n",
            "[300]\ttraining's auc: 0.997769\tvalid_1's auc: 0.996356\n",
            "[400]\ttraining's auc: 0.998165\tvalid_1's auc: 0.996673\n",
            "[500]\ttraining's auc: 0.998467\tvalid_1's auc: 0.996884\n",
            "[600]\ttraining's auc: 0.998691\tvalid_1's auc: 0.99705\n",
            "[700]\ttraining's auc: 0.99888\tvalid_1's auc: 0.997151\n",
            "[800]\ttraining's auc: 0.999049\tvalid_1's auc: 0.997263\n",
            "[900]\ttraining's auc: 0.999181\tvalid_1's auc: 0.997348\n",
            "[1000]\ttraining's auc: 0.999291\tvalid_1's auc: 0.997426\n",
            "[1100]\ttraining's auc: 0.999384\tvalid_1's auc: 0.997474\n",
            "[1200]\ttraining's auc: 0.999464\tvalid_1's auc: 0.997519\n",
            "[1300]\ttraining's auc: 0.999538\tvalid_1's auc: 0.997542\n",
            "[1400]\ttraining's auc: 0.999603\tvalid_1's auc: 0.997558\n",
            "[1500]\ttraining's auc: 0.999658\tvalid_1's auc: 0.997566\n",
            "[1600]\ttraining's auc: 0.999707\tvalid_1's auc: 0.997576\n",
            "[1700]\ttraining's auc: 0.999749\tvalid_1's auc: 0.997582\n",
            "Early stopping, best iteration is:\n",
            "[1682]\ttraining's auc: 0.999743\tvalid_1's auc: 0.997585\n",
            "err:  0.9975847954209623\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\ttraining's auc: 0.996186\tvalid_1's auc: 0.99613\n",
            "[200]\ttraining's auc: 0.997092\tvalid_1's auc: 0.996705\n",
            "[300]\ttraining's auc: 0.997724\tvalid_1's auc: 0.997041\n",
            "[400]\ttraining's auc: 0.998149\tvalid_1's auc: 0.997208\n",
            "[500]\ttraining's auc: 0.998446\tvalid_1's auc: 0.997319\n",
            "[600]\ttraining's auc: 0.998674\tvalid_1's auc: 0.997412\n",
            "[700]\ttraining's auc: 0.99887\tvalid_1's auc: 0.99753\n",
            "[800]\ttraining's auc: 0.999034\tvalid_1's auc: 0.997613\n",
            "[900]\ttraining's auc: 0.999168\tvalid_1's auc: 0.997666\n",
            "[1000]\ttraining's auc: 0.999277\tvalid_1's auc: 0.997719\n",
            "[1100]\ttraining's auc: 0.999371\tvalid_1's auc: 0.997745\n",
            "[1200]\ttraining's auc: 0.999452\tvalid_1's auc: 0.997787\n",
            "[1300]\ttraining's auc: 0.999527\tvalid_1's auc: 0.997809\n",
            "[1400]\ttraining's auc: 0.999591\tvalid_1's auc: 0.997832\n",
            "[1500]\ttraining's auc: 0.999639\tvalid_1's auc: 0.99787\n",
            "[1600]\ttraining's auc: 0.999685\tvalid_1's auc: 0.997884\n",
            "[1700]\ttraining's auc: 0.999727\tvalid_1's auc: 0.997912\n",
            "[1800]\ttraining's auc: 0.999764\tvalid_1's auc: 0.997925\n",
            "[1900]\ttraining's auc: 0.999791\tvalid_1's auc: 0.99793\n",
            "[2000]\ttraining's auc: 0.999817\tvalid_1's auc: 0.99793\n",
            "Early stopping, best iteration is:\n",
            "[1950]\ttraining's auc: 0.999804\tvalid_1's auc: 0.997939\n",
            "err:  0.9979394896705561\n",
            "Training until validation scores don't improve for 100 rounds.\n",
            "[100]\ttraining's auc: 0.99643\tvalid_1's auc: 0.995314\n",
            "[200]\ttraining's auc: 0.997152\tvalid_1's auc: 0.995969\n",
            "[300]\ttraining's auc: 0.997661\tvalid_1's auc: 0.996621\n",
            "[400]\ttraining's auc: 0.998085\tvalid_1's auc: 0.997119\n",
            "[500]\ttraining's auc: 0.998381\tvalid_1's auc: 0.997383\n",
            "[600]\ttraining's auc: 0.998613\tvalid_1's auc: 0.997562\n",
            "[700]\ttraining's auc: 0.998798\tvalid_1's auc: 0.997697\n",
            "[800]\ttraining's auc: 0.998965\tvalid_1's auc: 0.997775\n",
            "[900]\ttraining's auc: 0.999111\tvalid_1's auc: 0.997873\n",
            "[1000]\ttraining's auc: 0.999229\tvalid_1's auc: 0.997919\n",
            "[1100]\ttraining's auc: 0.999321\tvalid_1's auc: 0.997971\n",
            "[1200]\ttraining's auc: 0.999396\tvalid_1's auc: 0.998\n",
            "[1300]\ttraining's auc: 0.999466\tvalid_1's auc: 0.99803\n",
            "[1400]\ttraining's auc: 0.999527\tvalid_1's auc: 0.998057\n",
            "[1500]\ttraining's auc: 0.99958\tvalid_1's auc: 0.998077\n",
            "[1600]\ttraining's auc: 0.999625\tvalid_1's auc: 0.998092\n",
            "[1700]\ttraining's auc: 0.999664\tvalid_1's auc: 0.99811\n",
            "[1800]\ttraining's auc: 0.999701\tvalid_1's auc: 0.998116\n",
            "[1900]\ttraining's auc: 0.999733\tvalid_1's auc: 0.998126\n",
            "[2000]\ttraining's auc: 0.999762\tvalid_1's auc: 0.998139\n",
            "[2100]\ttraining's auc: 0.999786\tvalid_1's auc: 0.998138\n",
            "Early stopping, best iteration is:\n",
            "[2002]\ttraining's auc: 0.999762\tvalid_1's auc: 0.998142\n",
            "err:  0.9981416059468609\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ef_MwPjZRX69",
        "colab_type": "code",
        "outputId": "b1e1e2dd-9419-471e-d002-4c6269d39eb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        }
      },
      "source": [
        "feature_imp = pd.DataFrame(sorted(zip(model.feature_importance(),X_train.columns)), columns=['Value','Feature'])\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "# sorted(zip(clf.feature_importances_, X.columns), reverse=True)\n",
        "feature_imp = pd.DataFrame(sorted(zip(model.feature_importance(importance_type='gain'),X_train.columns)), columns=['Value','Feature'])\n",
        "\n",
        "plt.figure(figsize=(20, 10))\n",
        "sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False).head(50))\n",
        "plt.title('LightGBM Features (avg over folds)')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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27SnnD3QKEREREb2m1R3MKTBHRI+kwBwRERHxaikwR0RExLqk1QLzev2RTERE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Tbzdj9cscWhj3PcC3OhU/ZPtD/Tluh1Gvf7Ov+cLpryjf6LPvHIjhIyIiIiIiIiKiH7W6gzkJ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\nEb2SBHNERERERERERERE9Mr/B2mvQEVMRb+vAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T11U-0jqTIDS",
        "colab_type": "code",
        "outputId": "ebb8774b-7554-4c1d-d841-b3f1a63a5325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "y_pred_totcb"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([1.52934598e-01, 2.08471872e-02, 2.72206833e-05, ...,\n",
              "        4.59152474e-02, 4.76528554e-02, 1.84901818e-05]),\n",
              " array([1.29535227e-01, 7.80923266e-03, 8.19167532e-06, ...,\n",
              "        1.82690339e-02, 1.05686701e-02, 5.83375482e-06]),\n",
              " array([5.47200901e-02, 6.16006247e-03, 3.14403845e-06, ...,\n",
              "        1.19049884e-02, 2.21174590e-02, 1.86537195e-06]),\n",
              " array([1.16461252e-01, 6.89779350e-03, 5.02105588e-06, ...,\n",
              "        8.60839950e-03, 5.06512839e-02, 4.67432324e-06])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73WhUdxMTOde",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = np.mean(y_pred_totcb,0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbOX55U8TQqs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub['redemption_status']= y_pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SENr1oCyTSmO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub.to_csv(\"LGB_Mode_Kfold_Sub4.csv\",index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loyx5L6FUY47",
        "colab_type": "text"
      },
      "source": [
        "## XGB"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21OZTJmlTb5t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CswU8JBBUaLN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def run_xgb(X_train, X_test, y_train, y_test, test_df_lgb):\n",
        "    params = {'objective': 'binary:logistic', \n",
        "              'booster' : \"gbtree\",\n",
        "              'eval_metric':'auc',\n",
        "              'eta': 0.04,\n",
        "              'max_depth': 4, \n",
        "              'subsample': 0.9500000000000001, #changed from 0.6\n",
        "              'colsample_bytree': 0.65,\n",
        "              'lambda':0.3,\n",
        "              'gamma':0.6,\n",
        "              'max_delta_step':1,\n",
        "              'min_child_weight':1.0,\n",
        "              #'alpha':0.001,\n",
        "              'random_state': 42,\n",
        "              #'scale_pos_weight':106,\n",
        "              'max_bin':300, #changed from 450\n",
        "              'silent': True}\n",
        "    \n",
        "    tr_data = xgb.DMatrix(X_train, y_train)\n",
        "    va_data = xgb.DMatrix(X_test, y_test)\n",
        "    \n",
        "    watchlist = [(tr_data, 'train'), (va_data, 'valid')]\n",
        "    \n",
        "    model_xgb = xgb.train(params, tr_data, 1000, watchlist, maximize=False, early_stopping_rounds = 10, verbose_eval=10)\n",
        "    \n",
        "    #cv = xgb.cv(params, tr_data, 271, nfold=5, early_stopping_rounds=10, verbose_eval=10)\n",
        "    \n",
        "    dtest = xgb.DMatrix(test_df_lgb)\n",
        "    xgb_pred_y = model_xgb.predict(dtest, ntree_limit=model_xgb.best_ntree_limit)\n",
        "    \n",
        "    return xgb_pred_y, model_xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qxMBcRtUcwN",
        "colab_type": "code",
        "outputId": "e66f6c64-7228-43df-b524-83dc3707cee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "source": [
        "# Training XGB\n",
        "pred_test_xgb, model_xgb = run_xgb(X_train, X_test, y_train, y_test, test_df)\n",
        "print(\"XGB Training Completed...\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\ttrain-auc:0.657651\tvalid-auc:0.612099\n",
            "Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.\n",
            "\n",
            "Will train until valid-auc hasn't improved in 10 rounds.\n",
            "[10]\ttrain-auc:0.704425\tvalid-auc:0.674675\n",
            "[20]\ttrain-auc:0.941264\tvalid-auc:0.91409\n",
            "[30]\ttrain-auc:0.983786\tvalid-auc:0.968966\n",
            "[40]\ttrain-auc:0.996539\tvalid-auc:0.987757\n",
            "[50]\ttrain-auc:0.996624\tvalid-auc:0.992916\n",
            "[60]\ttrain-auc:0.996865\tvalid-auc:0.996216\n",
            "[70]\ttrain-auc:0.997075\tvalid-auc:0.99655\n",
            "[80]\ttrain-auc:0.997194\tvalid-auc:0.996768\n",
            "[90]\ttrain-auc:0.998131\tvalid-auc:0.996968\n",
            "[100]\ttrain-auc:0.998279\tvalid-auc:0.997145\n",
            "[110]\ttrain-auc:0.998376\tvalid-auc:0.997263\n",
            "[120]\ttrain-auc:0.998528\tvalid-auc:0.997365\n",
            "[130]\ttrain-auc:0.998646\tvalid-auc:0.99744\n",
            "[140]\ttrain-auc:0.998759\tvalid-auc:0.997492\n",
            "[150]\ttrain-auc:0.99887\tvalid-auc:0.99753\n",
            "[160]\ttrain-auc:0.998972\tvalid-auc:0.997575\n",
            "[170]\ttrain-auc:0.999061\tvalid-auc:0.997649\n",
            "[180]\ttrain-auc:0.999138\tvalid-auc:0.997688\n",
            "[190]\ttrain-auc:0.999209\tvalid-auc:0.997759\n",
            "[200]\ttrain-auc:0.999276\tvalid-auc:0.99778\n",
            "[210]\ttrain-auc:0.999346\tvalid-auc:0.997838\n",
            "[220]\ttrain-auc:0.999398\tvalid-auc:0.997884\n",
            "[230]\ttrain-auc:0.999452\tvalid-auc:0.997904\n",
            "[240]\ttrain-auc:0.999515\tvalid-auc:0.997936\n",
            "[250]\ttrain-auc:0.999564\tvalid-auc:0.997962\n",
            "[260]\ttrain-auc:0.999623\tvalid-auc:0.997986\n",
            "Stopping. Best iteration:\n",
            "[258]\ttrain-auc:0.999612\tvalid-auc:0.997995\n",
            "\n",
            "XGB Training Completed...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhqDBOguUeK7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub['redemption_status'] = pred_test_xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6yFROQYUlP8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub.to_csv('XGB_Mode_Sub4.csv',index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wNcxhoYV2Oi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}