Data Imputation & Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modules :\n",
    "\n",
    "from pyforest import *\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "from IPython.display import Image\n",
    "import re\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Merchant_Banking.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AmExpert 2019 – Machine Learning Hackathon\n",
    "\n",
    "\n",
    "## Introduction\n",
    "American Express and Analytics Vidhya presents “AmExpert 2019 – Machine Learning Hackathon”. An amazing opportunity to showcase your analytical abilities and talent.\n",
    "\n",
    "Get a taste of the kind of challenges we face here at American Express on day to day basis.\n",
    "\n",
    "Exciting prizes up for grabs! Best performers also get a chance to get interviewed by American Express for analytics roles!\n",
    "\n",
    "Don’t hold on to the buzzing ideas in your mind. Just wear your thinking hats and display the spectrum of your creativity!\n",
    "\n",
    "\n",
    "# Problem Statement\n",
    "\n",
    "## Predicting Coupon Redemption\n",
    "\n",
    "XYZ Credit Card company regularly helps it’s merchants understand their data better and take key business decisions accurately by providing machine learning and analytics consulting. ABC is an established Brick & Mortar retailer that frequently conducts marketing campaigns for its diverse product range. As a merchant of XYZ, they have sought XYZ to assist them in their discount marketing process using the power of machine learning. Can you wear the AmExpert hat and help out ABC?\n",
    "\n",
    " \n",
    "Discount marketing and coupon usage are very widely used promotional techniques to attract new customers and to retain & reinforce loyalty of existing customers. The measurement of a consumer’s propensity towards coupon usage and the prediction of the redemption behaviour are crucial parameters in assessing the effectiveness of a marketing campaign.\n",
    "\n",
    " \n",
    "ABC’s promotions are shared across various channels including email, notifications, etc. A number of these campaigns include coupon discounts that are offered for a specific product/range of products. The retailer would like the ability to predict whether customers redeem the coupons received across channels, which will enable the retailer’s marketing team to accurately design coupon construct, and develop more precise and targeted marketing strategies.\n",
    "\n",
    " \n",
    "The data available in this problem contains the following information, including the details of a sample of campaigns and coupons used in previous campaigns -\n",
    "\n",
    "**User Demographic Details**\n",
    "\n",
    "**Campaign and coupon Details**\n",
    "\n",
    "**Product details**\n",
    "\n",
    "**Previous transactions**\n",
    "\n",
    "\n",
    "Based on previous transaction & performance data from the last 18 campaigns, predict the probability for the next 10 campaigns in the test set for each coupon and customer combination, whether the customer will redeem the coupon or not?\n",
    "\n",
    " \n",
    "\n",
    "### Dataset Description\n",
    "\n",
    "Here is the schema for the different data tables available. The detailed data dictionary is provided next.\n",
    "\n",
    "![title](amex19.png)\n",
    "\n",
    "You are provided with the following files in train.zip:\n",
    "\n",
    "**train.csv:** Train data containing the coupons offered to the given customers under the 18 campaigns\n",
    "\n",
    "|Variable |Definition |\n",
    "|----|----|\n",
    "|id\t|Unique id for coupon customer impression|\n",
    "|campaign_id\t|Unique id for a discount campaign|\n",
    "|coupon_id\t|Unique id for a discount coupon|\n",
    "|customer_id\t|Unique id for a customer|\n",
    "|redemption_status|\t(target) (0 - Coupon not redeemed, 1 - Coupon redeemed) |\n",
    "\n",
    "\n",
    "**campaign_data.csv:** Campaign information for each of the 28 campaigns\n",
    "\n",
    "|Variable|\tDefinition|\n",
    "|----|----|\n",
    "|campaign_id\t|Unique id for a discount campaign|\n",
    "|campaign_type\t|Anonymised Campaign Type (X/Y)|\n",
    "|start_date\t|Campaign Start Date|\n",
    "|end_date\t|Campaign End Date |\n",
    "\n",
    "\n",
    "**coupon_item_mapping.csv:** Mapping of coupon and items valid for discount under that coupon\n",
    "\n",
    "|Variable|\tDefinition|\n",
    "|----|----|\n",
    "|coupon_id\t|Unique id for a discount coupon (no order)|\n",
    "|item_id\t|Unique id for items for which given coupon is valid (no order) |\n",
    "\n",
    "\n",
    "**customer_demographics.csv:** Customer demographic information for some customers\n",
    "\n",
    "|Variable\t|Definition|\n",
    "|----|----|\n",
    "|customer_id\t|Unique id for a customer|\n",
    "|age_range\t|Age range of customer family in years|\n",
    "|marital_status|\tMarried/Single|\n",
    "|rented|\t0 - not rented accommodation, 1 - rented accommodation|\n",
    "|family_size|\tNumber of family members|\n",
    "|no_of_children\t|Number of children in the family|\n",
    "|income_bracket|\tLabel Encoded Income Bracket (Higher income corresponds to higher number) |\n",
    "\n",
    "\n",
    "**customer_transaction_data.csv:** Transaction data for all customers for duration of campaigns in the train data\n",
    "\n",
    "|Variable\t|Definition|\n",
    "|----|----|\n",
    "|date\t|Date of Transaction|\n",
    "|customer_id|Unique id for a customer|\n",
    "|item_id|\tUnique id for item|\n",
    "|quantity\t|quantity of item bought|\n",
    "|selling_price\t|Sales value of the transaction|\n",
    "|other_discount|\tDiscount from other sources such as manufacturer coupon/loyalty card|\n",
    "|coupon_discount|\tDiscount availed from retailer coupon |\n",
    "\n",
    "\n",
    "**item_data.csv:** Item information for each item sold by the retailer\n",
    "\n",
    "|Variable|\tDefinition|\n",
    "|----|----|\n",
    "|item_id|\tUnique id for itemv\n",
    "|brand\t|Unique id for item brand|\n",
    "|brand_type\t|Brand Type (local/Established)|\n",
    "|category\t|Item Category |\n",
    "\n",
    "\n",
    "**test.csv:** Contains the coupon customer combination for which redemption status is to be predicted\n",
    "\n",
    "|Variable|\tDefinition|\n",
    "|----|----|\n",
    "|id\t|Unique id for coupon customer impression|\n",
    "|campaign_id\t|Unique id for a discount campaign|\n",
    "|coupon_id\t|Unique id for a discount coupon|\n",
    "|customer_id\t|Unique id for a customer |\n",
    "\n",
    "*Campaign, coupon and customer data for test set is also contained in train.zip *\n",
    "\n",
    "**sample_submission.csv:** This file contains the format in which you have to submit your predictions.\n",
    "\n",
    "To summarise the entire process:\n",
    "\n",
    "* Customers receive coupons under various campaigns and may choose to redeem it.\n",
    "* They can redeem the given coupon for any valid product for that coupon as per coupon item mapping within the duration between campaign start date and end date\n",
    "* Next, the customer will redeem the coupon for an item at the retailer store and that will reflect in the transaction table in the column coupon_discount.\n",
    " \n",
    "### Evaluation Metric\n",
    "Submissions are evaluated on area under the ROC curve between the predicted probability and the observed target.\n",
    " \n",
    "\n",
    "### Public and Private Split\n",
    "* Test data is further randomly divided into Public (40%) and Private data (60%)\n",
    "* Your initial responses will be checked and scored on the Public data.\n",
    "* The final rankings would be based on your private score which will be published once the competition is over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Datasets :\n",
    "\n",
    "campaign_data = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\campaign_data.csv')\n",
    "coupon_item_mapping = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\coupon_item_mapping.csv')\n",
    "customer_demographs = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\customer_demographics.csv')\n",
    "customer_transaction_data = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\customer_transaction_data.csv')\n",
    "item_data = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\item_data.csv')\n",
    "train = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\train.csv')\n",
    "test = pd.read_csv(r'D:\\MOOCs & Material\\Data Science & ML\\Data Modelling - Datasets\\American Express (AmExpert) - Analytics vidhya\\Datasets\\test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>OBJECTIVE :</h2>\n",
    "\n",
    "Based on previous transaction & performance data from the last **18** campaigns, predict the probability for the next **10** campaigns in the test set for each coupon and customer combination, whether the customer will redeem the coupon or not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Performing EDA for </h2>\n",
    "\n",
    "+ Campaign_Data\n",
    "+ Coupon_Item_Mapping\n",
    "+ Item_data\n",
    "+ Customer_Transaction_Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Campaign Data :* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\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>campaign_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Y</td>\n",
       "      <td>21/10/13</td>\n",
       "      <td>20/12/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>Y</td>\n",
       "      <td>21/10/13</td>\n",
       "      <td>22/11/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Y</td>\n",
       "      <td>07/09/13</td>\n",
       "      <td>16/11/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>Y</td>\n",
       "      <td>08/10/13</td>\n",
       "      <td>15/11/13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>Y</td>\n",
       "      <td>16/09/13</td>\n",
       "      <td>18/10/13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   campaign_id campaign_type start_date  end_date\n",
       "0           24             Y   21/10/13  20/12/13\n",
       "1           25             Y   21/10/13  22/11/13\n",
       "2           20             Y   07/09/13  16/11/13\n",
       "3           23             Y   08/10/13  15/11/13\n",
       "4           21             Y   16/09/13  18/10/13"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "campaign_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 4 columns):\n",
      "campaign_id      28 non-null int64\n",
      "campaign_type    28 non-null object\n",
      "start_date       28 non-null object\n",
      "end_date         28 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 1.0+ KB\n"
     ]
    }
   ],
   "source": [
    "campaign_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(campaign_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total no of unique campaign ids :\n",
    "\n",
    "campaign_data.campaign_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count_by_train :  18\n",
      "count_by_test :  10\n"
     ]
    }
   ],
   "source": [
    "# For train set, there are 18 campaign_ids and therefore we need to predict for 10 campigns of the test set :\n",
    "\n",
    "count_by_train = 0\n",
    "\n",
    "for each in train.campaign_id.unique():\n",
    "    if each in campaign_data.campaign_id.unique():\n",
    "        count_by_train+=1\n",
    "        \n",
    "print('count_by_train : ', count_by_train)\n",
    "\n",
    "count_by_test = 0\n",
    "\n",
    "for each in test.campaign_id.unique():\n",
    "    if each in campaign_data.campaign_id.unique():\n",
    "        count_by_test+=1\n",
    "        \n",
    "print('count_by_test : ', count_by_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique No of campaign_type : 2\n",
      "Types No of campaign_type : ['Y' 'X']\n"
     ]
    }
   ],
   "source": [
    "# FEATURE - Campaign_Type :\n",
    "\n",
    "print('Unique No of campaign_type :', campaign_data.campaign_type.nunique())\n",
    "print('Types No of campaign_type :', campaign_data.campaign_type.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting to datetime for the 'start_date' and the 'end_date' of the campaigns :\n",
    "\n",
    "campaign_data.start_date = pd.to_datetime(campaign_data.start_date)\n",
    "campaign_data.end_date = pd.to_datetime(campaign_data.end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>campaign_id</th>\n",
       "      <th>campaign_type</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>Y</td>\n",
       "      <td>2013-10-21</td>\n",
       "      <td>2013-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>Y</td>\n",
       "      <td>2013-10-21</td>\n",
       "      <td>2013-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>Y</td>\n",
       "      <td>2013-07-09</td>\n",
       "      <td>2013-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>Y</td>\n",
       "      <td>2013-08-10</td>\n",
       "      <td>2013-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "      <td>Y</td>\n",
       "      <td>2013-09-16</td>\n",
       "      <td>2013-10-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   campaign_id campaign_type start_date   end_date\n",
       "0           24             Y 2013-10-21 2013-12-20\n",
       "1           25             Y 2013-10-21 2013-11-22\n",
       "2           20             Y 2013-07-09 2013-11-16\n",
       "3           23             Y 2013-08-10 2013-11-15\n",
       "4           21             Y 2013-09-16 2013-10-18"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "campaign_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Train Data:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 78369 entries, 0 to 78368\n",
      "Data columns (total 5 columns):\n",
      "id                   78369 non-null int64\n",
      "campaign_id          78369 non-null int64\n",
      "coupon_id            78369 non-null int64\n",
      "customer_id          78369 non-null int64\n",
      "redemption_status    78369 non-null int64\n",
      "dtypes: int64(5)\n",
      "memory usage: 3.0 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>campaign_id</th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>redemption_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>116</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>635</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>644</td>\n",
       "      <td>1050</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1017</td>\n",
       "      <td>1489</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  campaign_id  coupon_id  customer_id  redemption_status\n",
       "0   1           13         27         1053                  0\n",
       "1   2           13        116           48                  0\n",
       "2   6            9        635          205                  0\n",
       "3   7           13        644         1050                  0\n",
       "4   9            8       1017         1489                  0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78369"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             customer_id\n",
       "customer_id             \n",
       "1                     94\n",
       "3                     42\n",
       "4                     23\n",
       "5                     52\n",
       "6                     32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More than one data points of each customer(customer_id) :\n",
    "\n",
    "train.groupby('customer_id').agg({'customer_id':'count'}).head() # COUNT "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">   In train data, more than 1 data point is present for the same customer_id\n",
    "\n",
    ">   More than one type of coupon is applied by each customer over the campaigns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Customer Transaction Data:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>other_discount</th>\n",
       "      <th>coupon_discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>26830</td>\n",
       "      <td>1</td>\n",
       "      <td>35.26</td>\n",
       "      <td>-10.69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>54253</td>\n",
       "      <td>1</td>\n",
       "      <td>53.43</td>\n",
       "      <td>-13.89</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>31962</td>\n",
       "      <td>1</td>\n",
       "      <td>106.50</td>\n",
       "      <td>-14.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>33647</td>\n",
       "      <td>1</td>\n",
       "      <td>67.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>48199</td>\n",
       "      <td>1</td>\n",
       "      <td>71.24</td>\n",
       "      <td>-28.14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  customer_id  item_id  quantity  selling_price  other_discount  \\\n",
       "0  2012-01-02         1501    26830         1          35.26          -10.69   \n",
       "1  2012-01-02         1501    54253         1          53.43          -13.89   \n",
       "2  2012-01-02         1501    31962         1         106.50          -14.25   \n",
       "3  2012-01-02         1501    33647         1          67.32            0.00   \n",
       "4  2012-01-02         1501    48199         1          71.24          -28.14   \n",
       "\n",
       "   coupon_discount  \n",
       "0              0.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              0.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             customer_id\n",
       "customer_id             \n",
       "1                   1048\n",
       "2                    419\n",
       "3                    705\n",
       "4                    220\n",
       "5                    792"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More than one data points of each customer(customer_id) :\n",
    "\n",
    "customer_transaction_data.groupby('customer_id').agg({'customer_id':'count'}).head() # COUNT "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">   In customer_transaction_data, more than 1 data point is present for the same customer_id.\n",
    "\n",
    ">   More than one transaction has been performed by each customer over the campaigns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aggregrating transaction featurs for each customer_id(customer) :\n",
    "\n",
    "# More than one data points of each customer(customer_id) :\n",
    "\n",
    "customer_transaction_data_groupBy = customer_transaction_data.groupby('customer_id').agg({'selling_price':['mean', 'max', 'min', 'sum'],\n",
    "                                                     'other_discount':['mean', 'max', 'min', 'sum'],\n",
    "                                                      'coupon_discount':['mean', 'max', 'min', 'sum']})  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">selling_price</th>\n",
       "      <th colspan=\"4\" halign=\"left\">other_discount</th>\n",
       "      <th colspan=\"4\" halign=\"left\">coupon_discount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>94.001842</td>\n",
       "      <td>445.25</td>\n",
       "      <td>12.11</td>\n",
       "      <td>98513.93</td>\n",
       "      <td>-16.250382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-372.23</td>\n",
       "      <td>-17030.40</td>\n",
       "      <td>-2.019876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-106.86</td>\n",
       "      <td>-2116.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>102.864033</td>\n",
       "      <td>640.80</td>\n",
       "      <td>14.25</td>\n",
       "      <td>43100.03</td>\n",
       "      <td>-16.830430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-312.03</td>\n",
       "      <td>-7051.95</td>\n",
       "      <td>-0.595084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-249.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>103.617404</td>\n",
       "      <td>862.00</td>\n",
       "      <td>8.90</td>\n",
       "      <td>73050.27</td>\n",
       "      <td>-22.714227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-463.06</td>\n",
       "      <td>-16013.53</td>\n",
       "      <td>-3.091546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-142.48</td>\n",
       "      <td>-2179.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>154.423727</td>\n",
       "      <td>1330.05</td>\n",
       "      <td>14.25</td>\n",
       "      <td>33973.22</td>\n",
       "      <td>-13.305409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-390.04</td>\n",
       "      <td>-2927.19</td>\n",
       "      <td>-0.404773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-89.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>130.827146</td>\n",
       "      <td>1485.35</td>\n",
       "      <td>12.11</td>\n",
       "      <td>103615.10</td>\n",
       "      <td>-13.657917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-216.21</td>\n",
       "      <td>-10817.07</td>\n",
       "      <td>-0.114684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-71.24</td>\n",
       "      <td>-90.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            selling_price                            other_discount       \\\n",
       "                     mean      max    min        sum           mean  max   \n",
       "customer_id                                                                \n",
       "1               94.001842   445.25  12.11   98513.93     -16.250382  0.0   \n",
       "2              102.864033   640.80  14.25   43100.03     -16.830430  0.0   \n",
       "3              103.617404   862.00   8.90   73050.27     -22.714227  0.0   \n",
       "4              154.423727  1330.05  14.25   33973.22     -13.305409  0.0   \n",
       "5              130.827146  1485.35  12.11  103615.10     -13.657917  0.0   \n",
       "\n",
       "                              coupon_discount                        \n",
       "                min       sum            mean  max     min      sum  \n",
       "customer_id                                                          \n",
       "1           -372.23 -17030.40       -2.019876  0.0 -106.86 -2116.83  \n",
       "2           -312.03  -7051.95       -0.595084  0.0  -89.05  -249.34  \n",
       "3           -463.06 -16013.53       -3.091546  0.0 -142.48 -2179.54  \n",
       "4           -390.04  -2927.19       -0.404773  0.0  -89.05   -89.05  \n",
       "5           -216.21 -10817.07       -0.114684  0.0  -71.24   -90.83  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction_data_groupBy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Customer Demographs:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70+</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>46-55</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>46-55</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id age_range marital_status  rented family_size no_of_children  \\\n",
       "0            1       70+        Married       0           2            NaN   \n",
       "1            6     46-55        Married       0           2            NaN   \n",
       "2            7     26-35            NaN       0           3              1   \n",
       "3            8     26-35            NaN       0           4              2   \n",
       "4           10     46-55         Single       0           1            NaN   \n",
       "\n",
       "   income_bracket  \n",
       "0               4  \n",
       "1               5  \n",
       "2               3  \n",
       "3               6  \n",
       "4               5  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_demographs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 760 entries, 0 to 759\n",
      "Data columns (total 7 columns):\n",
      "customer_id       760 non-null int64\n",
      "age_range         760 non-null object\n",
      "marital_status    431 non-null object\n",
      "rented            760 non-null int64\n",
      "family_size       760 non-null object\n",
      "no_of_children    222 non-null object\n",
      "income_bracket    760 non-null int64\n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "customer_demographs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6c066608>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation for nulls in 'customer_demographs' :\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "sns.heatmap(customer_demographs.isnull(), cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">   **Maritial_status** & **No_of_Children** are found to be containing Nulls :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Feature 'no_of_children' imputation  :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>70+</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>46-55</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>46-55</td>\n",
       "      <td>Single</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id age_range marital_status  rented family_size no_of_children  \\\n",
       "0            1       70+        Married       0           2            NaN   \n",
       "1            6     46-55        Married       0           2            NaN   \n",
       "2            7     26-35            NaN       0           3              1   \n",
       "3            8     26-35            NaN       0           4              2   \n",
       "4           10     46-55         Single       0           1            NaN   \n",
       "\n",
       "   income_bracket  \n",
       "0               4  \n",
       "1               5  \n",
       "2               3  \n",
       "3               6  \n",
       "4               5  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_demographs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the 'NaN' with string 'NaN' for analysing distributions :\n",
    "\n",
    "customer_demographs['no_of_children'] = customer_demographs['no_of_children'].apply(lambda x:'NaN' if str(x)=='nan' else x)\n",
    "customer_demographs['marital_status'] = customer_demographs['marital_status'].apply(lambda x:'NaN' if str(x)=='nan' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN    538\n",
       "1      107\n",
       "3+      60\n",
       "2       55\n",
       "Name: no_of_children, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 538 null values in 'no_of_childern' feature which we need to impute : \n",
    "\n",
    "customer_demographs['no_of_children'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6c22c788>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# No of nulls in 'no_of_children', 'age_range' wise :\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.title('No of childred feature distribution Age_range wise')\n",
    "sns.countplot(data=customer_demographs, x='no_of_children', hue='age_range')\n",
    "\n",
    "# No of nulls in 'no_of_children', 'marital_status' wise :\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title('No_of_childred feature distribution Marital_status wise')\n",
    "sns.countplot(data=customer_demographs, x='no_of_children', hue='marital_status')\n",
    "\n",
    "\n",
    "\n",
    "# if marital_status='single':   set no_of_childern='0'\n",
    "\n",
    "for i in range(len(customer_demographs)):\n",
    "    \n",
    "    if (customer_demographs.loc[i, 'marital_status']=='Single') and (customer_demographs.loc[i, 'no_of_children']=='NaN'):\n",
    "        customer_demographs.loc[i, 'no_of_children']='0'  # '0' as in string\n",
    "        \n",
    "        \n",
    "# No of nulls in 'no_of_children', 'marital_status' wise :\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.title('No_of_childred feature distribution Marital_status wise After imputation')\n",
    "sns.countplot(data=customer_demographs, x='no_of_children', hue='marital_status')      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1.) For the **'no_of_children'** as Null, we imputed the same to be '0' where the ***martial status*** is 'Single'.\n",
    "\n",
    "> 2.) For the ***'no_of_children'*** as Null, and ***marital_status***='Married', we shall impute according to the feature \"age_range\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN    447\n",
       "1      107\n",
       "0       91\n",
       "3+      60\n",
       "2       55\n",
       "Name: no_of_children, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_demographs.no_of_children.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 760 entries, 0 to 759\n",
      "Data columns (total 7 columns):\n",
      "customer_id       760 non-null int64\n",
      "age_range         760 non-null object\n",
      "marital_status    760 non-null object\n",
      "rented            760 non-null int64\n",
      "family_size       760 non-null object\n",
      "no_of_children    760 non-null object\n",
      "income_bracket    760 non-null int64\n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "customer_demographs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>57</td>\n",
       "      <td>110</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>186</td>\n",
       "      <td>377</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>380</td>\n",
       "      <td>775</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>475</td>\n",
       "      <td>965</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>539</td>\n",
       "      <td>1121</td>\n",
       "      <td>18-25</td>\n",
       "      <td>Married</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id age_range marital_status  rented family_size no_of_children  \\\n",
       "57           110     18-25        Married       0           3              1   \n",
       "186          377     18-25        Married       1           3              1   \n",
       "380          775     18-25        Married       0           3              1   \n",
       "475          965     18-25        Married       1           3              1   \n",
       "539         1121     18-25        Married       0           3              1   \n",
       "\n",
       "     income_bracket  \n",
       "57                6  \n",
       "186               4  \n",
       "380               2  \n",
       "475               4  \n",
       "539               5  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='18-25')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Where no_of_children= \"not null('NaN')\" and marital_status='Married' and with different age_range :\n",
    "\n",
    "\n",
    "impute_children_age_married_PLUS_46_55 = customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='46-55')]\n",
    "\n",
    "impute_children_age_married_PLUS_70plus = customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='70+')]\n",
    "\n",
    "impute_children_age_married_PLUS_26_35 = customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='26-35')]\n",
    "\n",
    "impute_children_age_married_PLUS_36_45 = customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='36-45')]\n",
    "\n",
    "impute_children_age_married_PLUS_56_70 = customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='56-70')]\n",
    "\n",
    "impute_children_age_married_PLUS_18_25 = customer_demographs[(customer_demographs['no_of_children']!='NaN') & \n",
    "                                            (customer_demographs['marital_status']=='Married')& \n",
    "                                            (customer_demographs['age_range']=='18-25')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6d680a48>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ('No_of_Children' is not 'NaN', null) AND (marital_status='married') AND (age_range=###) \n",
    "\n",
    "impute_children_age = customer_demographs[(customer_demographs['no_of_children']=='NaN') & \n",
    "                                          (customer_demographs['marital_status']=='Married')]\n",
    "\n",
    "plt.figure(figsize=(20, 40))\n",
    "\n",
    "plt.subplot(7,2,1)\n",
    "plt.title('age_range of married with no_of_children as Null')\n",
    "sns.countplot(data=impute_children_age, x='no_of_children', hue='age_range')\n",
    "\n",
    "plt.subplot(7,2,2)\n",
    "plt.title('age_range 46-55 of married with no_of_children as not Null')\n",
    "sns.countplot(data=impute_children_age_married_PLUS_46_55, x='no_of_children')\n",
    "\n",
    "plt.subplot(7,2,3)\n",
    "plt.title('age_range 70+ of married with no_of_children as not Null')\n",
    "sns.countplot(data=impute_children_age_married_PLUS_70plus, x='no_of_children')\n",
    "\n",
    "plt.subplot(7,2,4)\n",
    "plt.title('age_range 26-35 of married with no_of_children as not Null')\n",
    "sns.countplot(data=impute_children_age_married_PLUS_26_35, x='no_of_children')\n",
    "\n",
    "plt.subplot(7,2,5)\n",
    "plt.title('age_range 36-45 of married with no_of_children as not Null')\n",
    "sns.countplot(data=impute_children_age_married_PLUS_36_45, x='no_of_children')\n",
    "\n",
    "plt.subplot(7,2,6)\n",
    "plt.title('age_range 56-70 of married with no_of_children as not Null')\n",
    "sns.countplot(data=impute_children_age_married_PLUS_56_70, x='no_of_children')\n",
    "\n",
    "plt.subplot(7,2,7)\n",
    "plt.title('age_range 18-25 of married with no_of_children as not Null')\n",
    "sns.countplot(data=impute_children_age_married_PLUS_18_25, x='no_of_children')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature imputation according to the maximum occurences of the 'age_range' class values in 'married' and 'no_of_children' is null:\n",
    "\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='Married')& \n",
    "                        (customer_demographs['age_range']=='70+'), 'no_of_children'] = '1'  \n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='Married')& \n",
    "                        (customer_demographs['age_range']=='46-55'), 'no_of_children'] = '1'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='Married')& \n",
    "                        (customer_demographs['age_range']=='26-35'), 'no_of_children'] = '1'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='Married')& \n",
    "                        (customer_demographs['age_range']=='36-45'), 'no_of_children'] = '1'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='Married')& \n",
    "                        (customer_demographs['age_range']=='56-70'), 'no_of_children'] = '2'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='Married')& \n",
    "                        (customer_demographs['age_range']=='18-25'), 'no_of_children'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN    272\n",
       "1      253\n",
       "0       91\n",
       "2       84\n",
       "3+      60\n",
       "Name: no_of_children, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only 272 'NaN' remains in the 'no_of_children' feature : \n",
    "\n",
    "customer_demographs.no_of_children.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  CASE - I : Now the no of points having only no_of_children=='NaN' and marital_status=='NaN' only :\n",
    "\n",
    "len(customer_demographs[(customer_demographs['no_of_children']=='NaN') & (customer_demographs['marital_status']=='NaN')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CASE - II : Now the no of points having only no_of_children=='NaN' only :\n",
    "\n",
    "len(customer_demographs[(customer_demographs['no_of_children']=='NaN')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CASE - III : Now the no of points having only marital_status=='NaN' only :\n",
    "\n",
    "len(customer_demographs[(customer_demographs['marital_status']=='NaN') & (customer_demographs['no_of_children']!='NaN')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>36-45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>46-55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>46-55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>70+</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>18-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>744</td>\n",
       "      <td>1545</td>\n",
       "      <td>36-45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>1566</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>755</td>\n",
       "      <td>1577</td>\n",
       "      <td>36-45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>757</td>\n",
       "      <td>1579</td>\n",
       "      <td>46-55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>758</td>\n",
       "      <td>1580</td>\n",
       "      <td>26-35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>272 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     customer_id age_range marital_status  rented family_size no_of_children  \\\n",
       "10            17     36-45            NaN       0           1            NaN   \n",
       "11            19     46-55            NaN       0           1            NaN   \n",
       "14            28     46-55            NaN       0           2            NaN   \n",
       "15            30       70+            NaN       0           1            NaN   \n",
       "18            35     18-25            NaN       0           2            NaN   \n",
       "..           ...       ...            ...     ...         ...            ...   \n",
       "744         1545     36-45            NaN       0           2            NaN   \n",
       "750         1566     26-35            NaN       0           2            NaN   \n",
       "755         1577     36-45            NaN       0           2            NaN   \n",
       "757         1579     46-55            NaN       0           1            NaN   \n",
       "758         1580     26-35            NaN       0           2            NaN   \n",
       "\n",
       "     income_bracket  \n",
       "10                5  \n",
       "11                3  \n",
       "14                1  \n",
       "15                5  \n",
       "18                4  \n",
       "..              ...  \n",
       "744               8  \n",
       "750               9  \n",
       "755               5  \n",
       "757               4  \n",
       "758               5  \n",
       "\n",
       "[272 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imputing where 'marital_status' is NaN and 'no_of_children' is NaN :\n",
    "\n",
    "marital_status_AND_no_of_children_NaN = customer_demographs[(customer_demographs['marital_status']=='NaN') & (customer_demographs['no_of_children']=='NaN')]\n",
    "marital_status_AND_no_of_children_NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6d7d92c8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(data=marital_status_AND_no_of_children_NaN, x='age_range', hue='family_size')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(data=customer_demographs, x='age_range', hue='no_of_children')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to impute where marital_status & no_of_children are NaN (Nulls) :\n",
    "\n",
    "+ Now we first attempt to impute 'no_of_children' feature :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1.) For the ***age_range*** & ***family_size status*** no defininte inference can be found for imputation.\n",
    "\n",
    "> 2.) For ***age_range*** vs ***no_of_children***, we will use the 2nd plot to impute 'no_of_children' feature according to maximum present values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature imputation according to the maximum occurences of the 'age_range' class values in 'marital_status'=Null and 'no_of_children'= null:\n",
    "\n",
    "# Where 'marital_status'=NaN & 'no_of_children'=NaN, we look by 'age_range' & 'no_of_children' feature (2nd plot): \n",
    "\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='NaN')& \n",
    "                        (customer_demographs['age_range']=='70+'), 'no_of_children'] = '1'  \n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='NaN')& \n",
    "                        (customer_demographs['age_range']=='46-55'), 'no_of_children'] = '1'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='NaN')& \n",
    "                        (customer_demographs['age_range']=='26-35'), 'no_of_children'] = '1'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='NaN')& \n",
    "                        (customer_demographs['age_range']=='36-45'), 'no_of_children'] = '1'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='NaN')& \n",
    "                        (customer_demographs['age_range']=='56-70'), 'no_of_children'] = '2'\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['no_of_children']=='NaN') & \n",
    "                        (customer_demographs['marital_status']=='NaN')& \n",
    "                        (customer_demographs['age_range']=='18-25'), 'no_of_children'] = '1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 1.) Where 'marital_status'=NotNull & 'no_of_children'=Null, imputation has been performed. \n",
    "\n",
    "> 2.) Where 'marital_status'=Null & 'no_of_children'=Null, imputation has been performed. \n",
    "\n",
    "> 3.) Where 'marital_status'=Null & 'no_of_children'=NotNull, imputation is now left. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final imputation to perform where 'marital_status'=Null and 'no_of_children'=Not Null :\n",
    "\n",
    "len(customer_demographs[(customer_demographs['marital_status']=='NaN') & (customer_demographs['no_of_children']!='NaN')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xd6d8f8488>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 7))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.countplot(data=customer_demographs, x='marital_status', hue='age_range')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.countplot(data=customer_demographs, x='marital_status', hue='family_size')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  Since 1st plot does not reveal much for imputing the NaNs in the marital_status, hence we use the 2nd plot to impute the 'NaN' marital_status according to the presence of maximum occurence of values in marital_status : \n",
    "\n",
    "> + Where 'family_size'=1 and 'marital_status' is NaN, impute setting marital_status='Single'.\n",
    "\n",
    "> + Where 'family_size'=5 and 'marital_status' is NaN, impute setting marital_status='Married'.\n",
    "\n",
    "> + Where 'family_size'=4 and 'marital_status' is NaN, impute setting marital_status='Married'.\n",
    "\n",
    "> + Where 'family_size'=3 and 'marital_status' is NaN, impute setting marital_status='Married'.\n",
    "\n",
    "> + Where 'family_size'=2 and 'marital_status' is NaN, impute setting marital_status='Married'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature imputation according to the rules mentioned above for 'marital_status' only :\n",
    "\n",
    "customer_demographs.ix[(customer_demographs['family_size']=='1') & \n",
    "                        (customer_demographs['marital_status']=='NaN'), 'marital_status'] = 'Single'  \n",
    "\n",
    "customer_demographs.ix[(customer_demographs['family_size']=='5+') & \n",
    "                        (customer_demographs['marital_status']=='NaN'), 'marital_status'] = 'Married'  \n",
    "\n",
    "customer_demographs.ix[(customer_demographs['family_size']=='4') & \n",
    "                        (customer_demographs['marital_status']=='NaN'), 'marital_status'] = 'Married'  \n",
    "\n",
    "customer_demographs.ix[(customer_demographs['family_size']=='3') & \n",
    "                        (customer_demographs['marital_status']=='NaN'), 'marital_status'] = 'Married'  \n",
    "\n",
    "customer_demographs.ix[(customer_demographs['family_size']=='2') & \n",
    "                        (customer_demographs['marital_status']=='NaN'), 'marital_status'] = 'Married'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     512\n",
       "2      97\n",
       "0      91\n",
       "3+     60\n",
       "Name: no_of_children, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value counts of customer_demographs 'no_of_children':\n",
    "\n",
    "customer_demographs.no_of_children.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married    463\n",
       "Single     297\n",
       "Name: marital_status, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value counts of customer_demographs 'marital_status':\n",
    "\n",
    "customer_demographs.marital_status.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Imputation of the 'no_of_children' & 'marital_status' has been accomplished*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Coupon_item_mapping & Item_Data :*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>494</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>522</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>518</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coupon_id  item_id\n",
       "0        105       37\n",
       "1        107       75\n",
       "2        494       76\n",
       "3        522       77\n",
       "4        518       77"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coupon_item_mapping.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>brand_type</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Established</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Established</td>\n",
       "      <td>Miscellaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  brand   brand_type       category\n",
       "0        1      1  Established        Grocery\n",
       "1        2      1  Established  Miscellaneous\n",
       "2        3     56        Local         Bakery\n",
       "3        4     56        Local        Grocery\n",
       "4        5     56        Local        Grocery"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "coup_MERGED_item = coupon_item_mapping.merge(item_data, how='inner', on='item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>brand_type</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>105</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>107</td>\n",
       "      <td>75</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coupon_id  item_id  brand brand_type category\n",
       "0        105       37     56      Local  Grocery\n",
       "1          6       37     56      Local  Grocery\n",
       "2         22       37     56      Local  Grocery\n",
       "3         31       37     56      Local  Grocery\n",
       "4        107       75     56      Local  Grocery"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coup_MERGED_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Established    78759\n",
       "Local          13904\n",
       "Name: brand_type, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coup_MERGED_item.brand_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grocery                   36466\n",
       "Pharmaceutical            25061\n",
       "Natural Products           6819\n",
       "Meat                       6218\n",
       "Packaged Meat              6144\n",
       "Skin & Hair Care           4924\n",
       "Seafood                    2227\n",
       "Flowers & Plants           1963\n",
       "Dairy, Juices & Snacks     1867\n",
       "Garden                      286\n",
       "Prepared Food               240\n",
       "Miscellaneous               184\n",
       "Bakery                      100\n",
       "Salads                      100\n",
       "Travel                       44\n",
       "Vegetables (cut)             19\n",
       "Restauarant                   1\n",
       "Name: category, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coup_MERGED_item.category.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coupon_id  category\n",
       "0          1        39\n",
       "1          2         2\n",
       "2          3        17\n",
       "3          4        24\n",
       "4          5         7"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coup_MERGED_item_groupBy = coup_MERGED_item.groupby('coupon_id').agg({'category':'count'})\n",
    "coup_MERGED_item_groupBy.reset_index(inplace=True)\n",
    "coup_MERGED_item_groupBy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">   One coupon (coupon_id) gets applied on more than one type of item_id and each item_id can have different **(brand_type+category)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Item_Data & Customer_Transaction_Data :*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>selling_price</th>\n",
       "      <th>other_discount</th>\n",
       "      <th>coupon_discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>26830</td>\n",
       "      <td>1</td>\n",
       "      <td>35.26</td>\n",
       "      <td>-10.69</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>54253</td>\n",
       "      <td>1</td>\n",
       "      <td>53.43</td>\n",
       "      <td>-13.89</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>31962</td>\n",
       "      <td>1</td>\n",
       "      <td>106.50</td>\n",
       "      <td>-14.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>33647</td>\n",
       "      <td>1</td>\n",
       "      <td>67.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1501</td>\n",
       "      <td>48199</td>\n",
       "      <td>1</td>\n",
       "      <td>71.24</td>\n",
       "      <td>-28.14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  customer_id  item_id  quantity  selling_price  other_discount  \\\n",
       "0  2012-01-02         1501    26830         1          35.26          -10.69   \n",
       "1  2012-01-02         1501    54253         1          53.43          -13.89   \n",
       "2  2012-01-02         1501    31962         1         106.50          -14.25   \n",
       "3  2012-01-02         1501    33647         1          67.32            0.00   \n",
       "4  2012-01-02         1501    48199         1          71.24          -28.14   \n",
       "\n",
       "   coupon_discount  \n",
       "0              0.0  \n",
       "1              0.0  \n",
       "2              0.0  \n",
       "3              0.0  \n",
       "4              0.0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1324566, 7)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>brand</th>\n",
       "      <th>brand_type</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Established</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Established</td>\n",
       "      <td>Miscellaneous</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>56</td>\n",
       "      <td>Local</td>\n",
       "      <td>Grocery</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id  brand   brand_type       category\n",
       "0        1      1  Established        Grocery\n",
       "1        2      1  Established  Miscellaneous\n",
       "2        3     56        Local         Bakery\n",
       "3        4     56        Local        Grocery\n",
       "4        5     56        Local        Grocery"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74066, 4)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_transaction_data_groupBy.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">selling_price</th>\n",
       "      <th colspan=\"4\" halign=\"left\">other_discount</th>\n",
       "      <th colspan=\"4\" halign=\"left\">coupon_discount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>94.001842</td>\n",
       "      <td>445.25</td>\n",
       "      <td>12.11</td>\n",
       "      <td>98513.93</td>\n",
       "      <td>-16.250382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-372.23</td>\n",
       "      <td>-17030.40</td>\n",
       "      <td>-2.019876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-106.86</td>\n",
       "      <td>-2116.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>102.864033</td>\n",
       "      <td>640.80</td>\n",
       "      <td>14.25</td>\n",
       "      <td>43100.03</td>\n",
       "      <td>-16.830430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-312.03</td>\n",
       "      <td>-7051.95</td>\n",
       "      <td>-0.595084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-249.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>103.617404</td>\n",
       "      <td>862.00</td>\n",
       "      <td>8.90</td>\n",
       "      <td>73050.27</td>\n",
       "      <td>-22.714227</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-463.06</td>\n",
       "      <td>-16013.53</td>\n",
       "      <td>-3.091546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-142.48</td>\n",
       "      <td>-2179.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>154.423727</td>\n",
       "      <td>1330.05</td>\n",
       "      <td>14.25</td>\n",
       "      <td>33973.22</td>\n",
       "      <td>-13.305409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-390.04</td>\n",
       "      <td>-2927.19</td>\n",
       "      <td>-0.404773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-89.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>130.827146</td>\n",
       "      <td>1485.35</td>\n",
       "      <td>12.11</td>\n",
       "      <td>103615.10</td>\n",
       "      <td>-13.657917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-216.21</td>\n",
       "      <td>-10817.07</td>\n",
       "      <td>-0.114684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-71.24</td>\n",
       "      <td>-90.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  customer_id selling_price                            other_discount       \\\n",
       "                       mean      max    min        sum           mean  max   \n",
       "0           1     94.001842   445.25  12.11   98513.93     -16.250382  0.0   \n",
       "1           2    102.864033   640.80  14.25   43100.03     -16.830430  0.0   \n",
       "2           3    103.617404   862.00   8.90   73050.27     -22.714227  0.0   \n",
       "3           4    154.423727  1330.05  14.25   33973.22     -13.305409  0.0   \n",
       "4           5    130.827146  1485.35  12.11  103615.10     -13.657917  0.0   \n",
       "\n",
       "                    coupon_discount                        \n",
       "      min       sum            mean  max     min      sum  \n",
       "0 -372.23 -17030.40       -2.019876  0.0 -106.86 -2116.83  \n",
       "1 -312.03  -7051.95       -0.595084  0.0  -89.05  -249.34  \n",
       "2 -463.06 -16013.53       -3.091546  0.0 -142.48 -2179.54  \n",
       "3 -390.04  -2927.19       -0.404773  0.0  -89.05   -89.05  \n",
       "4 -216.21 -10817.07       -0.114684  0.0  -71.24   -90.83  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction_data_groupBy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1582, 13)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_transaction_data_groupBy.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Preparing the train set :* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78369, 8)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_latest = train + campaign_data :\n",
    "\n",
    "train_MERGED_campaign_data = train.merge(campaign_data, how='left', on='campaign_id')\n",
    "train_MERGED_campaign_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "campaign_id          0\n",
       "coupon_id            0\n",
       "customer_id          0\n",
       "redemption_status    0\n",
       "campaign_type        0\n",
       "start_date           0\n",
       "end_date             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_MERGED_campaign_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78369, 9)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_latest = train_latest + coup_merged_item\n",
    "train_MERGED_campaign_data_MERGED_coupon = train_MERGED_campaign_data.merge(coup_MERGED_item_groupBy, how='left', on='coupon_id') \n",
    "train_MERGED_campaign_data_MERGED_coupon.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                   0\n",
       "campaign_id          0\n",
       "coupon_id            0\n",
       "customer_id          0\n",
       "redemption_status    0\n",
       "campaign_type        0\n",
       "start_date           0\n",
       "end_date             0\n",
       "category             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_MERGED_campaign_data_MERGED_coupon.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78369, 15)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_latest = train_latest + customer_dempgraphs\n",
    "train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics = train_MERGED_campaign_data_MERGED_coupon.merge(customer_demographs, \n",
    "                                                                                                                      how='left', on='customer_id')\n",
    "train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics.shape                                                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0\n",
       "campaign_id              0\n",
       "coupon_id                0\n",
       "customer_id              0\n",
       "redemption_status        0\n",
       "campaign_type            0\n",
       "start_date               0\n",
       "end_date                 0\n",
       "category                 0\n",
       "age_range            34708\n",
       "marital_status       34708\n",
       "rented               34708\n",
       "family_size          34708\n",
       "no_of_children       34708\n",
       "income_bracket       34708\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78369, 27)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_latest = train_latest + customer_transaction_data_merged_item_data\n",
    "train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics_MERGED_customer_transaction_data = train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics.merge(customer_transaction_data_groupBy, \n",
    "                                                                                                                      how='left', \n",
    "                                                                                                                      on='customer_id')\n",
    "train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics_MERGED_customer_transaction_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>campaign_id</th>\n",
       "      <th>coupon_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>redemption_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>116</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>635</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>644</td>\n",
       "      <td>1050</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1017</td>\n",
       "      <td>1489</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  campaign_id  coupon_id  customer_id  redemption_status\n",
       "0   1           13         27         1053                  0\n",
       "1   2           13        116           48                  0\n",
       "2   6            9        635          205                  0\n",
       "3   7           13        644         1050                  0\n",
       "4   9            8       1017         1489                  0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(78369, 27)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# final train_set :\n",
    "\n",
    "train_df = train_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics_MERGED_customer_transaction_data\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                          0\n",
       "campaign_id                 0\n",
       "coupon_id                   0\n",
       "customer_id                 0\n",
       "redemption_status           0\n",
       "campaign_type               0\n",
       "start_date                  0\n",
       "end_date                    0\n",
       "category                    0\n",
       "age_range               34708\n",
       "marital_status          34708\n",
       "rented                  34708\n",
       "family_size             34708\n",
       "no_of_children          34708\n",
       "income_bracket          34708\n",
       "selling_price_mean          0\n",
       "selling_price_max           0\n",
       "selling_price_min           0\n",
       "selling_price_sum           0\n",
       "other_discount_mean         0\n",
       "other_discount_max          0\n",
       "other_discount_min          0\n",
       "other_discount_sum          0\n",
       "coupon_discount_mean        0\n",
       "coupon_discount_max         0\n",
       "coupon_discount_min         0\n",
       "coupon_discount_sum         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 78369 entries, 0 to 78368\n",
      "Data columns (total 27 columns):\n",
      "id                      78369 non-null int64\n",
      "campaign_id             78369 non-null int64\n",
      "coupon_id               78369 non-null int64\n",
      "customer_id             78369 non-null int64\n",
      "redemption_status       78369 non-null int64\n",
      "campaign_type           78369 non-null object\n",
      "start_date              78369 non-null datetime64[ns]\n",
      "end_date                78369 non-null datetime64[ns]\n",
      "category                78369 non-null int64\n",
      "age_range               43661 non-null object\n",
      "marital_status          43661 non-null object\n",
      "rented                  43661 non-null float64\n",
      "family_size             43661 non-null object\n",
      "no_of_children          43661 non-null object\n",
      "income_bracket          43661 non-null float64\n",
      "selling_price_mean      78369 non-null float64\n",
      "selling_price_max       78369 non-null float64\n",
      "selling_price_min       78369 non-null float64\n",
      "selling_price_sum       78369 non-null float64\n",
      "other_discount_mean     78369 non-null float64\n",
      "other_discount_max      78369 non-null float64\n",
      "other_discount_min      78369 non-null float64\n",
      "other_discount_sum      78369 non-null float64\n",
      "coupon_discount_mean    78369 non-null float64\n",
      "coupon_discount_max     78369 non-null float64\n",
      "coupon_discount_min     78369 non-null float64\n",
      "coupon_discount_sum     78369 non-null float64\n",
      "dtypes: datetime64[ns](2), float64(14), int64(6), object(5)\n",
      "memory usage: 16.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rectifying columns in train :\n",
    "\n",
    "feature_list_train = [str(each_train_col[0]+\"_\"+each_train_col[1]) if str(type(each_train_col))==\"<class 'tuple'>\" else each_train_col  for each_train_col in train_df.columns ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rectifying column names :\n",
    "\n",
    "train_df.columns = feature_list_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Train set still contains Nulls  :*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>category</th>\n",
       "      <th>age_range</th>\n",
       "      <th>...</th>\n",
       "      <th>selling_price_min</th>\n",
       "      <th>selling_price_sum</th>\n",
       "      <th>other_discount_mean</th>\n",
       "      <th>other_discount_max</th>\n",
       "      <th>other_discount_min</th>\n",
       "      <th>other_discount_sum</th>\n",
       "      <th>coupon_discount_mean</th>\n",
       "      <th>coupon_discount_max</th>\n",
       "      <th>coupon_discount_min</th>\n",
       "      <th>coupon_discount_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>2013-05-19</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>125</td>\n",
       "      <td>46-55</td>\n",
       "      <td>...</td>\n",
       "      <td>17.45</td>\n",
       "      <td>57120.75</td>\n",
       "      <td>-33.168935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-890.50</td>\n",
       "      <td>-10282.37</td>\n",
       "      <td>-0.287258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-89.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>2013-05-19</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>3</td>\n",
       "      <td>36-45</td>\n",
       "      <td>...</td>\n",
       "      <td>7.12</td>\n",
       "      <td>90185.10</td>\n",
       "      <td>-27.699169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-372.23</td>\n",
       "      <td>-10664.18</td>\n",
       "      <td>-3.215039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-213.72</td>\n",
       "      <td>-1237.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>2013-11-03</td>\n",
       "      <td>2013-12-04</td>\n",
       "      <td>67</td>\n",
       "      <td>46-55</td>\n",
       "      <td>...</td>\n",
       "      <td>17.45</td>\n",
       "      <td>117461.66</td>\n",
       "      <td>-17.795660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-468.05</td>\n",
       "      <td>-17261.79</td>\n",
       "      <td>-2.212082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-2145.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>2013-05-19</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13.89</td>\n",
       "      <td>23291.42</td>\n",
       "      <td>-16.655570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-312.03</td>\n",
       "      <td>-3947.37</td>\n",
       "      <td>-0.751477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-178.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>2013-02-16</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>32</td>\n",
       "      <td>46-55</td>\n",
       "      <td>...</td>\n",
       "      <td>8.90</td>\n",
       "      <td>67797.49</td>\n",
       "      <td>-20.524733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-320.22</td>\n",
       "      <td>-11534.90</td>\n",
       "      <td>-0.471548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-53.43</td>\n",
       "      <td>-265.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  campaign_id  coupon_id  customer_id  redemption_status campaign_type  \\\n",
       "0   1           13         27         1053                  0             X   \n",
       "1   2           13        116           48                  0             X   \n",
       "2   6            9        635          205                  0             Y   \n",
       "3   7           13        644         1050                  0             X   \n",
       "4   9            8       1017         1489                  0             X   \n",
       "\n",
       "  start_date   end_date  category age_range  ... selling_price_min  \\\n",
       "0 2013-05-19 2013-05-07       125     46-55  ...             17.45   \n",
       "1 2013-05-19 2013-05-07         3     36-45  ...              7.12   \n",
       "2 2013-11-03 2013-12-04        67     46-55  ...             17.45   \n",
       "3 2013-05-19 2013-05-07         4       NaN  ...             13.89   \n",
       "4 2013-02-16 2013-05-04        32     46-55  ...              8.90   \n",
       "\n",
       "   selling_price_sum other_discount_mean other_discount_max  \\\n",
       "0           57120.75          -33.168935                0.0   \n",
       "1           90185.10          -27.699169                0.0   \n",
       "2          117461.66          -17.795660                0.0   \n",
       "3           23291.42          -16.655570                0.0   \n",
       "4           67797.49          -20.524733                0.0   \n",
       "\n",
       "   other_discount_min  other_discount_sum  coupon_discount_mean  \\\n",
       "0             -890.50           -10282.37             -0.287258   \n",
       "1             -372.23           -10664.18             -3.215039   \n",
       "2             -468.05           -17261.79             -2.212082   \n",
       "3             -312.03            -3947.37             -0.751477   \n",
       "4             -320.22           -11534.90             -0.471548   \n",
       "\n",
       "   coupon_discount_max  coupon_discount_min  coupon_discount_sum  \n",
       "0                  0.0               -89.05               -89.05  \n",
       "1                  0.0              -213.72             -1237.79  \n",
       "2                  0.0               -89.05             -2145.72  \n",
       "3                  0.0               -89.05              -178.10  \n",
       "4                  0.0               -53.43              -265.01  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature engineering timedelta(days) from campaign_start and end_dates for train and test sets :\n",
    "\n",
    "train_df['Gap_days_campaign'] = train['end_date'] - train['start_date'] \n",
    "test_df['Gap_days_campaign'] = test['end_date'] - test['start_date'] \n",
    "\n",
    "# train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *Preparing the test set :* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_latest = test + campaign_data \n",
    "\n",
    "test_MERGED_campaign_data = test.merge(campaign_data, how='left', on='campaign_id')\n",
    "test_MERGED_campaign_data.shape\n",
    "\n",
    "# test_latest = test_latest + coup_merged_item\n",
    "test_MERGED_campaign_data_MERGED_coupon = test_MERGED_campaign_data.merge(coup_MERGED_item_groupBy, how='left', on='coupon_id') \n",
    "test_MERGED_campaign_data_MERGED_coupon.shape\n",
    "\n",
    "# test_latest = test_latest + customer_dempgraphs\n",
    "test_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics = test_MERGED_campaign_data_MERGED_coupon.merge(customer_demographs, \n",
    "                                                                                                                      how='left', on='customer_id')\n",
    "test_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics.shape                                                                        \n",
    "\n",
    "# test_latest = test_latest + customer_transaction_data_merged_item_data\n",
    "test_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics_MERGED_customer_transaction_data = test_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics.merge(customer_transaction_data_groupBy, \n",
    "                                                                                                                      how='left', \n",
    "                                                                                                                      on='customer_id')\n",
    "test_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics_MERGED_customer_transaction_data.shape\n",
    "\n",
    "# final test_set :\n",
    "\n",
    "test_df = test_MERGED_campaign_data_MERGED_coupon_MERGED_customer_demographics_MERGED_customer_transaction_data\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rectifying columns in test :\n",
    "\n",
    "feature_list_test = [str(each_test_col[0]+\"_\"+each_test_col[1]) if str(type(each_test_col))==\"<class 'tuple'>\" else each_test_col  for each_test_col in test.columns ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train set :\n",
    "\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train set features :\n",
    "\n",
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test set :\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1428"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "760"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_demographs.customer_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum = 0\n",
    "for each in train.customer_id.unique():\n",
    "    \n",
    "    if each in customer_demographs.customer_id.unique():\n",
    "        sum+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "703"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "725"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1428-703"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>category</th>\n",
       "      <th>age_range</th>\n",
       "      <th>...</th>\n",
       "      <th>selling_price_min</th>\n",
       "      <th>selling_price_sum</th>\n",
       "      <th>other_discount_mean</th>\n",
       "      <th>other_discount_max</th>\n",
       "      <th>other_discount_min</th>\n",
       "      <th>other_discount_sum</th>\n",
       "      <th>coupon_discount_mean</th>\n",
       "      <th>coupon_discount_max</th>\n",
       "      <th>coupon_discount_min</th>\n",
       "      <th>coupon_discount_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>2013-05-19</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>125</td>\n",
       "      <td>46-55</td>\n",
       "      <td>...</td>\n",
       "      <td>17.45</td>\n",
       "      <td>57120.75</td>\n",
       "      <td>-33.168935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-890.50</td>\n",
       "      <td>-10282.37</td>\n",
       "      <td>-0.287258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-89.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>2013-05-19</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>3</td>\n",
       "      <td>36-45</td>\n",
       "      <td>...</td>\n",
       "      <td>7.12</td>\n",
       "      <td>90185.10</td>\n",
       "      <td>-27.699169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-372.23</td>\n",
       "      <td>-10664.18</td>\n",
       "      <td>-3.215039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-213.72</td>\n",
       "      <td>-1237.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>2013-11-03</td>\n",
       "      <td>2013-12-04</td>\n",
       "      <td>67</td>\n",
       "      <td>46-55</td>\n",
       "      <td>...</td>\n",
       "      <td>17.45</td>\n",
       "      <td>117461.66</td>\n",
       "      <td>-17.795660</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-468.05</td>\n",
       "      <td>-17261.79</td>\n",
       "      <td>-2.212082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-2145.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>2013-05-19</td>\n",
       "      <td>2013-05-07</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>13.89</td>\n",
       "      <td>23291.42</td>\n",
       "      <td>-16.655570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-312.03</td>\n",
       "      <td>-3947.37</td>\n",
       "      <td>-0.751477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-89.05</td>\n",
       "      <td>-178.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>2013-02-16</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>32</td>\n",
       "      <td>46-55</td>\n",
       "      <td>...</td>\n",
       "      <td>8.90</td>\n",
       "      <td>67797.49</td>\n",
       "      <td>-20.524733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-320.22</td>\n",
       "      <td>-11534.90</td>\n",
       "      <td>-0.471548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-53.43</td>\n",
       "      <td>-265.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  campaign_id  coupon_id  customer_id  redemption_status campaign_type  \\\n",
       "0   1           13         27         1053                  0             X   \n",
       "1   2           13        116           48                  0             X   \n",
       "2   6            9        635          205                  0             Y   \n",
       "3   7           13        644         1050                  0             X   \n",
       "4   9            8       1017         1489                  0             X   \n",
       "\n",
       "  start_date   end_date  category age_range  ... selling_price_min  \\\n",
       "0 2013-05-19 2013-05-07       125     46-55  ...             17.45   \n",
       "1 2013-05-19 2013-05-07         3     36-45  ...              7.12   \n",
       "2 2013-11-03 2013-12-04        67     46-55  ...             17.45   \n",
       "3 2013-05-19 2013-05-07         4       NaN  ...             13.89   \n",
       "4 2013-02-16 2013-05-04        32     46-55  ...              8.90   \n",
       "\n",
       "   selling_price_sum other_discount_mean other_discount_max  \\\n",
       "0           57120.75          -33.168935                0.0   \n",
       "1           90185.10          -27.699169                0.0   \n",
       "2          117461.66          -17.795660                0.0   \n",
       "3           23291.42          -16.655570                0.0   \n",
       "4           67797.49          -20.524733                0.0   \n",
       "\n",
       "   other_discount_min  other_discount_sum  coupon_discount_mean  \\\n",
       "0             -890.50           -10282.37             -0.287258   \n",
       "1             -372.23           -10664.18             -3.215039   \n",
       "2             -468.05           -17261.79             -2.212082   \n",
       "3             -312.03            -3947.37             -0.751477   \n",
       "4             -320.22           -11534.90             -0.471548   \n",
       "\n",
       "   coupon_discount_max  coupon_discount_min  coupon_discount_sum  \n",
       "0                  0.0               -89.05               -89.05  \n",
       "1                  0.0              -213.72             -1237.79  \n",
       "2                  0.0               -89.05             -2145.72  \n",
       "3                  0.0               -89.05              -178.10  \n",
       "4                  0.0               -53.43              -265.01  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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