Starbucks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starbucks Challenge\n",
    "\n",
    "### Introduction\n",
    "\n",
    "This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BOGO (buy one get one free). Some users might not receive any offer during certain weeks. \n",
    "\n",
    "Not all users receive the same offer, and that is the challenge to solve with this data set.\n",
    "\n",
    "Every offer has a validity period before the offer expires. As an example, a BOGO offer might be valid for only 5 days. You'll see in the data set that informational offers have a validity period even though these ads are merely providing information about a product; for example, if an informational offer has 7 days of validity, you can assume the customer is feeling the influence of the offer for 7 days after receiving the advertisement.\n",
    "\n",
    "\n",
    "### Example\n",
    "\n",
    "To give an example, a user could receive a discount offer buy 10 dollars get 2 off on Monday. The offer is valid for 10 days from receipt. If the customer accumulates at least 10 dollars in purchases during the validity period, the customer completes the offer.\n",
    "\n",
    "However, there are a few things to watch out for in this data set. Customers do not opt into the offers that they receive; in other words, a user can receive an offer, never actually view the offer, and still complete the offer. For example, a user might receive the \"buy 10 dollars get 2 dollars off offer\", but the user never opens the offer during the 10 day validity period. The customer spends 15 dollars during those ten days. There will be an offer completion record in the data set; however, the customer was not influenced by the offer because the customer never viewed the offer.\n",
    "\n",
    "### Cleaning\n",
    "\n",
    "This makes data cleaning especially important and tricky.\n",
    "\n",
    "You'll also want to take into account that some demographic groups will make purchases even if they don't receive an offer. From a business perspective, if a customer is going to make a 10 dollar purchase without an offer anyway, you wouldn't want to send a buy 10 dollars get 2 dollars off offer. You'll want to try to assess what a certain demographic group will buy when not receiving any offers.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Sets\n",
    "\n",
    "The data is contained in three files:\n",
    "\n",
    "* portfolio.json - containing offer ids and meta data about each offer (duration, type, etc.)\n",
    "* profile.json - demographic data for each customer\n",
    "* transcript.json - records for transactions, offers received, offers viewed, and offers completed\n",
    "\n",
    "Here is the schema and explanation of each variable in the files:\n",
    "\n",
    "**portfolio.json**\n",
    "* id (string) - offer id\n",
    "* offer_type (string) - type of offer ie BOGO, discount, informational\n",
    "* difficulty (int) - minimum required spend to complete an offer\n",
    "* reward (int) - reward given for completing an offer\n",
    "* duration (int) - time for offer to be open, in days\n",
    "* channels (list of strings)\n",
    "\n",
    "**profile.json**\n",
    "* age (int) - age of the customer \n",
    "* became_member_on (int) - date when customer created an app account\n",
    "* gender (str) - gender of the customer (note some entries contain 'O' for other rather than M or F)\n",
    "* id (str) - customer id\n",
    "* income (float) - customer's income\n",
    "\n",
    "**transcript.json**\n",
    "* event (str) - record description (ie transaction, offer received, offer viewed, etc.)\n",
    "* person (str) - customer id\n",
    "* time (int) - time in hours since start of test. The data begins at time t=0\n",
    "* value - (dict of strings) - either an offer id or transaction amount depending on the record\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import json\n",
    "% matplotlib inline\n",
    "\n",
    "# read in the json files\n",
    "portfolio = pd.read_json('data/portfolio.json', orient='records', lines=True)\n",
    "profile = pd.read_json('data/profile.json', orient='records', lines=True)\n",
    "transcript = pd.read_json('data/transcript.json', orient='records', lines=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA on Portfolio**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10         7   \n",
       "1  [web, email, mobile, social]          10         5   \n",
       "2          [web, email, mobile]           0         4   \n",
       "3          [web, email, mobile]           5         7   \n",
       "4                  [web, email]          20        10   \n",
       "\n",
       "                                 id     offer_type  reward  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      "channels      10 non-null object\n",
      "difficulty    10 non-null int64\n",
      "duration      10 non-null int64\n",
      "id            10 non-null object\n",
      "offer_type    10 non-null object\n",
      "reward        10 non-null int64\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 560.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "portfolio.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "channels      0\n",
       "difficulty    0\n",
       "duration      0\n",
       "id            0\n",
       "offer_type    0\n",
       "reward        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bogo', 'informational', 'discount'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio['offer_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "offer_type\n",
       "bogo             4\n",
       "discount         4\n",
       "informational    2\n",
       "Name: id, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.groupby(by=\"offer_type\")['id'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  0,  5, 20,  7])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio['difficulty'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA on profile**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17000 entries, 0 to 16999\n",
      "Data columns (total 5 columns):\n",
      "age                 17000 non-null int64\n",
      "became_member_on    17000 non-null int64\n",
      "gender              14825 non-null object\n",
      "id                  17000 non-null object\n",
      "income              14825 non-null float64\n",
      "dtypes: float64(1), int64(2), object(2)\n",
      "memory usage: 664.1+ KB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17000, 5)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>id</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>118</td>\n",
       "      <td>20170212</td>\n",
       "      <td>None</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>20180712</td>\n",
       "      <td>None</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118</td>\n",
       "      <td>20170804</td>\n",
       "      <td>None</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  became_member_on gender                                id    income\n",
       "0  118          20170212   None  68be06ca386d4c31939f3a4f0e3dd783       NaN\n",
       "1   55          20170715      F  0610b486422d4921ae7d2bf64640c50b  112000.0\n",
       "2  118          20180712   None  38fe809add3b4fcf9315a9694bb96ff5       NaN\n",
       "3   75          20170509      F  78afa995795e4d85b5d9ceeca43f5fef  100000.0\n",
       "4  118          20170804   None  a03223e636434f42ac4c3df47e8bac43       NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([118,  55,  75,  68,  65,  58,  61,  26,  62,  49,  57,  40,  64,\n",
       "        78,  42,  56,  33,  46,  59,  67,  53,  22,  96,  69,  20,  45,\n",
       "        54,  39,  41,  79,  66,  29,  44,  63,  36,  76,  77,  30,  51,\n",
       "        27,  73,  74,  70,  89,  50,  90,  60,  19,  72,  52,  18,  71,\n",
       "        83,  43,  47,  32,  38,  34,  85,  48,  35,  82,  21,  24,  81,\n",
       "        25,  37,  23, 100,  28,  84,  80,  87,  86,  94,  31,  88,  95,\n",
       "        93,  91,  92,  98, 101,  97,  99])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([None, 'F', 'M', 'O'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    0\n",
       "became_member_on       0\n",
       "gender              2175\n",
       "id                     0\n",
       "income              2175\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    8484\n",
       "F    6129\n",
       "O     212\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     14825.000000\n",
       "mean      65404.991568\n",
       "std       21598.299410\n",
       "min       30000.000000\n",
       "25%       49000.000000\n",
       "50%       64000.000000\n",
       "75%       80000.000000\n",
       "max      120000.000000\n",
       "Name: income, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['income'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118    2175\n",
       "58      408\n",
       "53      372\n",
       "51      363\n",
       "54      359\n",
       "59      359\n",
       "57      353\n",
       "52      351\n",
       "55      350\n",
       "56      342\n",
       "63      338\n",
       "60      333\n",
       "49      321\n",
       "62      318\n",
       "67      317\n",
       "64      311\n",
       "61      309\n",
       "48      288\n",
       "50      284\n",
       "66      284\n",
       "65      280\n",
       "68      259\n",
       "70      252\n",
       "69      242\n",
       "47      232\n",
       "45      231\n",
       "71      230\n",
       "42      227\n",
       "43      215\n",
       "46      212\n",
       "       ... \n",
       "22      131\n",
       "30      131\n",
       "29      129\n",
       "23      126\n",
       "81      118\n",
       "35      118\n",
       "79      117\n",
       "80      116\n",
       "31      113\n",
       "82      105\n",
       "83      102\n",
       "84       83\n",
       "18       70\n",
       "85       70\n",
       "87       66\n",
       "88       64\n",
       "86       54\n",
       "89       53\n",
       "90       52\n",
       "91       47\n",
       "92       40\n",
       "94       33\n",
       "93       29\n",
       "95       22\n",
       "97       13\n",
       "100      12\n",
       "96        8\n",
       "98        5\n",
       "99        5\n",
       "101       5\n",
       "Name: age, Length: 85, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 2175\n",
       "became_member_on    2175\n",
       "gender                 0\n",
       "id                  2175\n",
       "income                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile[profile['age']==118].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EDA on transcript**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 4 columns):\n",
      "event     306534 non-null object\n",
      "person    306534 non-null object\n",
      "time      306534 non-null int64\n",
      "value     306534 non-null object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>event</th>\n",
       "      <th>person</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>offer received</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>offer received</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>offer received</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>offer received</td>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>offer received</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            event                            person  time  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef     0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43     0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932     0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586     0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598     0   \n",
       "\n",
       "                                              value  \n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(306534, 4)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "event     0\n",
       "person    0\n",
       "time      0\n",
       "value     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['offer received', 'offer viewed', 'transaction', 'offer completed'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['event'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.columns.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transaction        138953\n",
       "offer received      76277\n",
       "offer viewed        57725\n",
       "offer completed     33579\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['event'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   6,  12,  18,  24,  30,  36,  42,  48,  54,  60,  66,  72,\n",
       "        78,  84,  90,  96, 102, 108, 114, 120, 126, 132, 138, 144, 150,\n",
       "       156, 162, 168, 174, 180, 186, 192, 198, 204, 210, 216, 222, 228,\n",
       "       234, 240, 246, 252, 258, 264, 270, 276, 282, 288, 294, 300, 306,\n",
       "       312, 318, 324, 330, 336, 342, 348, 354, 360, 366, 372, 378, 384,\n",
       "       390, 396, 402, 408, 414, 420, 426, 432, 438, 444, 450, 456, 462,\n",
       "       468, 474, 480, 486, 492, 498, 504, 510, 516, 522, 528, 534, 540,\n",
       "       546, 552, 558, 564, 570, 576, 582, 588, 594, 600, 606, 612, 618,\n",
       "       624, 630, 636, 642, 648, 654, 660, 666, 672, 678, 684, 690, 696,\n",
       "       702, 708, 714])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['time'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transaction=transcript[transcript['event']==\"transaction\"]"
   ]
  },
  {
   "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>event</th>\n",
       "      <th>person</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12654</th>\n",
       "      <td>transaction</td>\n",
       "      <td>02c083884c7d45b39cc68e1314fec56c</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 0.8300000000000001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12657</th>\n",
       "      <td>transaction</td>\n",
       "      <td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 34.56}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12659</th>\n",
       "      <td>transaction</td>\n",
       "      <td>54890f68699049c2a04d415abc25e717</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 13.23}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12670</th>\n",
       "      <td>transaction</td>\n",
       "      <td>b2f1cd155b864803ad8334cdf13c4bd2</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 19.51}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12671</th>\n",
       "      <td>transaction</td>\n",
       "      <td>fe97aa22dd3e48c8b143116a8403dd52</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 18.97}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12678</th>\n",
       "      <td>transaction</td>\n",
       "      <td>629fc02d56414d91bca360decdfa9288</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 33.9}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12686</th>\n",
       "      <td>transaction</td>\n",
       "      <td>bbeb54e861614fc7b22a8844f72dca6c</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 0.22}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12687</th>\n",
       "      <td>transaction</td>\n",
       "      <td>a97e6f33219c432db82acfa0d19c602d</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 18.59}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12691</th>\n",
       "      <td>transaction</td>\n",
       "      <td>676506bad68e4161b9bbaffeb039626b</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 18.01}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12696</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8f7dd3b2afe14c078eb4f6e6fe4ba97d</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 19.11}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12706</th>\n",
       "      <td>transaction</td>\n",
       "      <td>4cbe33c601a5407f8202086565c55111</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 36.19}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12709</th>\n",
       "      <td>transaction</td>\n",
       "      <td>b432b74402bb4981a4651c8df1670365</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 6.46}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12711</th>\n",
       "      <td>transaction</td>\n",
       "      <td>a04fcfd571034456aaa6d56c0a3fd9b6</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 5.02}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12716</th>\n",
       "      <td>transaction</td>\n",
       "      <td>227f2d69e46a4899b70d48182822cff6</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 28.39}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12720</th>\n",
       "      <td>transaction</td>\n",
       "      <td>bb0f25e23a4c4de6a645527c275cd594</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 28.08}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12724</th>\n",
       "      <td>transaction</td>\n",
       "      <td>c2c72ce6038644c797208046d1e3498a</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 0.75}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12738</th>\n",
       "      <td>transaction</td>\n",
       "      <td>7ca349e55ff544c7a13addfdea2e2c06</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 1.02}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12743</th>\n",
       "      <td>transaction</td>\n",
       "      <td>d72d201be5794279aa716d8ad82b8d90</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 13.57}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12756</th>\n",
       "      <td>transaction</td>\n",
       "      <td>ad80753fc9e0485c9e6b1cc9478d827f</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 10.22}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12763</th>\n",
       "      <td>transaction</td>\n",
       "      <td>73ffefd41e9a4ca3ab26b2b3697c6eb7</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 31.42}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12766</th>\n",
       "      <td>transaction</td>\n",
       "      <td>3e621194f72e40d7a0b695ee9b7c38b7</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 27.89}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12768</th>\n",
       "      <td>transaction</td>\n",
       "      <td>1f5c961416e64c5d88098b02b1bdf246</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 4.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12775</th>\n",
       "      <td>transaction</td>\n",
       "      <td>3bcc51fdde354eb1949c813dbc905182</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 13.05}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12777</th>\n",
       "      <td>transaction</td>\n",
       "      <td>ed46fca6de7042478b411690878dc069</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 1.16}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12779</th>\n",
       "      <td>transaction</td>\n",
       "      <td>b860d355ef6e4c66b5d5a837c56ef32d</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 38.38}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12781</th>\n",
       "      <td>transaction</td>\n",
       "      <td>4ad3748475204cf99571183f05b5e2f7</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 4.08}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12783</th>\n",
       "      <td>transaction</td>\n",
       "      <td>99297ea01107436fa8c2e2bc86f55d89</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 5.78}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12785</th>\n",
       "      <td>transaction</td>\n",
       "      <td>24115a61df25473e84a8a03f3c98de1a</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 14.27}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12794</th>\n",
       "      <td>transaction</td>\n",
       "      <td>7195944f0cc34115b0a5e7b4a62055f2</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 11.52}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12797</th>\n",
       "      <td>transaction</td>\n",
       "      <td>afce4cf8194f4e90a3e92da941a23601</td>\n",
       "      <td>0</td>\n",
       "      <td>{'amount': 13.93}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306500</th>\n",
       "      <td>transaction</td>\n",
       "      <td>f0ffaed9279946f1bc29614a234f1ee1</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.43}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306501</th>\n",
       "      <td>transaction</td>\n",
       "      <td>93907d06a946424ba630487fe7aeafd1</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 28.03}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306502</th>\n",
       "      <td>transaction</td>\n",
       "      <td>588ceea482344c3ab6845c83aaed4ac0</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.08}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306503</th>\n",
       "      <td>transaction</td>\n",
       "      <td>a97208c5be42445d9949e82e0f70f622</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 5.8}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306504</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8524d450673b4c24869b6c94380006de</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.89}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306505</th>\n",
       "      <td>transaction</td>\n",
       "      <td>b895c57e8cd047a8872ce02aa54759d6</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.48}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306508</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8431c16f8e1d440880db371a68f82dd0</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.19}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306510</th>\n",
       "      <td>transaction</td>\n",
       "      <td>ba620885e51c4b0ea64a4f61daad494f</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 14.31}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306511</th>\n",
       "      <td>transaction</td>\n",
       "      <td>a1a8f40407c444cc848468275308958a</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.37}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306512</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8d80970192fa496f99d6b45c470a4b60</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 6.92}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306513</th>\n",
       "      <td>transaction</td>\n",
       "      <td>bde275066f3c4fa0bff3093e3b866a2c</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 12.73}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306514</th>\n",
       "      <td>transaction</td>\n",
       "      <td>f1e4fd36e5a0446f83861308bddf6945</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 8.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306515</th>\n",
       "      <td>transaction</td>\n",
       "      <td>0b64be3b241c4407a5c9a71781173829</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306516</th>\n",
       "      <td>transaction</td>\n",
       "      <td>86d03d35d7e0434b935e7743e83be3a0</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 9.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306517</th>\n",
       "      <td>transaction</td>\n",
       "      <td>3408fd05c781401f8442fb6dbaaea9c7</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 11.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306518</th>\n",
       "      <td>transaction</td>\n",
       "      <td>1593d617fac246ef8e50dbb0ffd77f5f</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 40.67}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306519</th>\n",
       "      <td>transaction</td>\n",
       "      <td>f1b31d07b5d84f69a2d5f1d07843989e</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 31.13}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306520</th>\n",
       "      <td>transaction</td>\n",
       "      <td>2ce987015ec0404a97ba333e8e814090</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.6400000000000001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306521</th>\n",
       "      <td>transaction</td>\n",
       "      <td>2e33545f0a764d27b2ccff95fc8d72c4</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 17.35}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306522</th>\n",
       "      <td>transaction</td>\n",
       "      <td>d1c4500ace2e45e9a45d3cd2fccac8d8</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.42}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306523</th>\n",
       "      <td>transaction</td>\n",
       "      <td>b65affd9e07346a1906364a396950e3d</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 18.35}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306524</th>\n",
       "      <td>transaction</td>\n",
       "      <td>d613ca9c59dd42f497bdbf6178da54a7</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 25.14}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306525</th>\n",
       "      <td>transaction</td>\n",
       "      <td>eec70ab28af74a22a4aeb889c0317944</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 43.58}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306526</th>\n",
       "      <td>transaction</td>\n",
       "      <td>24f56b5e1849462093931b164eb803b5</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 22.64}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306528</th>\n",
       "      <td>transaction</td>\n",
       "      <td>5ca2620962114246ab218fc648eb3934</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>transaction</td>\n",
       "      <td>b3a1272bc9904337b331bf348c3e8c17</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.5899999999999999}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>transaction</td>\n",
       "      <td>68213b08d99a4ae1b0dcb72aebd9aa35</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 9.53}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>transaction</td>\n",
       "      <td>a00058cf10334a308c68e7631c529907</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 3.61}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>transaction</td>\n",
       "      <td>76ddbd6576844afe811f1a3c0fbb5bec</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 3.5300000000000002}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>transaction</td>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.05}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138953 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              event                            person  time  \\\n",
       "12654   transaction  02c083884c7d45b39cc68e1314fec56c     0   \n",
       "12657   transaction  9fa9ae8f57894cc9a3b8a9bbe0fc1b2f     0   \n",
       "12659   transaction  54890f68699049c2a04d415abc25e717     0   \n",
       "12670   transaction  b2f1cd155b864803ad8334cdf13c4bd2     0   \n",
       "12671   transaction  fe97aa22dd3e48c8b143116a8403dd52     0   \n",
       "12678   transaction  629fc02d56414d91bca360decdfa9288     0   \n",
       "12686   transaction  bbeb54e861614fc7b22a8844f72dca6c     0   \n",
       "12687   transaction  a97e6f33219c432db82acfa0d19c602d     0   \n",
       "12691   transaction  676506bad68e4161b9bbaffeb039626b     0   \n",
       "12696   transaction  8f7dd3b2afe14c078eb4f6e6fe4ba97d     0   \n",
       "12706   transaction  4cbe33c601a5407f8202086565c55111     0   \n",
       "12709   transaction  b432b74402bb4981a4651c8df1670365     0   \n",
       "12711   transaction  a04fcfd571034456aaa6d56c0a3fd9b6     0   \n",
       "12716   transaction  227f2d69e46a4899b70d48182822cff6     0   \n",
       "12720   transaction  bb0f25e23a4c4de6a645527c275cd594     0   \n",
       "12724   transaction  c2c72ce6038644c797208046d1e3498a     0   \n",
       "12738   transaction  7ca349e55ff544c7a13addfdea2e2c06     0   \n",
       "12743   transaction  d72d201be5794279aa716d8ad82b8d90     0   \n",
       "12756   transaction  ad80753fc9e0485c9e6b1cc9478d827f     0   \n",
       "12763   transaction  73ffefd41e9a4ca3ab26b2b3697c6eb7     0   \n",
       "12766   transaction  3e621194f72e40d7a0b695ee9b7c38b7     0   \n",
       "12768   transaction  1f5c961416e64c5d88098b02b1bdf246     0   \n",
       "12775   transaction  3bcc51fdde354eb1949c813dbc905182     0   \n",
       "12777   transaction  ed46fca6de7042478b411690878dc069     0   \n",
       "12779   transaction  b860d355ef6e4c66b5d5a837c56ef32d     0   \n",
       "12781   transaction  4ad3748475204cf99571183f05b5e2f7     0   \n",
       "12783   transaction  99297ea01107436fa8c2e2bc86f55d89     0   \n",
       "12785   transaction  24115a61df25473e84a8a03f3c98de1a     0   \n",
       "12794   transaction  7195944f0cc34115b0a5e7b4a62055f2     0   \n",
       "12797   transaction  afce4cf8194f4e90a3e92da941a23601     0   \n",
       "...             ...                               ...   ...   \n",
       "306500  transaction  f0ffaed9279946f1bc29614a234f1ee1   714   \n",
       "306501  transaction  93907d06a946424ba630487fe7aeafd1   714   \n",
       "306502  transaction  588ceea482344c3ab6845c83aaed4ac0   714   \n",
       "306503  transaction  a97208c5be42445d9949e82e0f70f622   714   \n",
       "306504  transaction  8524d450673b4c24869b6c94380006de   714   \n",
       "306505  transaction  b895c57e8cd047a8872ce02aa54759d6   714   \n",
       "306508  transaction  8431c16f8e1d440880db371a68f82dd0   714   \n",
       "306510  transaction  ba620885e51c4b0ea64a4f61daad494f   714   \n",
       "306511  transaction  a1a8f40407c444cc848468275308958a   714   \n",
       "306512  transaction  8d80970192fa496f99d6b45c470a4b60   714   \n",
       "306513  transaction  bde275066f3c4fa0bff3093e3b866a2c   714   \n",
       "306514  transaction  f1e4fd36e5a0446f83861308bddf6945   714   \n",
       "306515  transaction  0b64be3b241c4407a5c9a71781173829   714   \n",
       "306516  transaction  86d03d35d7e0434b935e7743e83be3a0   714   \n",
       "306517  transaction  3408fd05c781401f8442fb6dbaaea9c7   714   \n",
       "306518  transaction  1593d617fac246ef8e50dbb0ffd77f5f   714   \n",
       "306519  transaction  f1b31d07b5d84f69a2d5f1d07843989e   714   \n",
       "306520  transaction  2ce987015ec0404a97ba333e8e814090   714   \n",
       "306521  transaction  2e33545f0a764d27b2ccff95fc8d72c4   714   \n",
       "306522  transaction  d1c4500ace2e45e9a45d3cd2fccac8d8   714   \n",
       "306523  transaction  b65affd9e07346a1906364a396950e3d   714   \n",
       "306524  transaction  d613ca9c59dd42f497bdbf6178da54a7   714   \n",
       "306525  transaction  eec70ab28af74a22a4aeb889c0317944   714   \n",
       "306526  transaction  24f56b5e1849462093931b164eb803b5   714   \n",
       "306528  transaction  5ca2620962114246ab218fc648eb3934   714   \n",
       "306529  transaction  b3a1272bc9904337b331bf348c3e8c17   714   \n",
       "306530  transaction  68213b08d99a4ae1b0dcb72aebd9aa35   714   \n",
       "306531  transaction  a00058cf10334a308c68e7631c529907   714   \n",
       "306532  transaction  76ddbd6576844afe811f1a3c0fbb5bec   714   \n",
       "306533  transaction  c02b10e8752c4d8e9b73f918558531f7   714   \n",
       "\n",
       "                                 value  \n",
       "12654   {'amount': 0.8300000000000001}  \n",
       "12657                {'amount': 34.56}  \n",
       "12659                {'amount': 13.23}  \n",
       "12670                {'amount': 19.51}  \n",
       "12671                {'amount': 18.97}  \n",
       "12678                 {'amount': 33.9}  \n",
       "12686                 {'amount': 0.22}  \n",
       "12687                {'amount': 18.59}  \n",
       "12691                {'amount': 18.01}  \n",
       "12696                {'amount': 19.11}  \n",
       "12706                {'amount': 36.19}  \n",
       "12709                 {'amount': 6.46}  \n",
       "12711                 {'amount': 5.02}  \n",
       "12716                {'amount': 28.39}  \n",
       "12720                {'amount': 28.08}  \n",
       "12724                 {'amount': 0.75}  \n",
       "12738                 {'amount': 1.02}  \n",
       "12743                {'amount': 13.57}  \n",
       "12756                {'amount': 10.22}  \n",
       "12763                {'amount': 31.42}  \n",
       "12766                {'amount': 27.89}  \n",
       "12768                  {'amount': 4.2}  \n",
       "12775                {'amount': 13.05}  \n",
       "12777                 {'amount': 1.16}  \n",
       "12779                {'amount': 38.38}  \n",
       "12781                 {'amount': 4.08}  \n",
       "12783                 {'amount': 5.78}  \n",
       "12785                {'amount': 14.27}  \n",
       "12794                {'amount': 11.52}  \n",
       "12797                {'amount': 13.93}  \n",
       "...                                ...  \n",
       "306500                {'amount': 1.43}  \n",
       "306501               {'amount': 28.03}  \n",
       "306502                {'amount': 1.08}  \n",
       "306503                 {'amount': 5.8}  \n",
       "306504                {'amount': 4.89}  \n",
       "306505                {'amount': 4.48}  \n",
       "306508                {'amount': 1.19}  \n",
       "306510               {'amount': 14.31}  \n",
       "306511                {'amount': 2.37}  \n",
       "306512                {'amount': 6.92}  \n",
       "306513               {'amount': 12.73}  \n",
       "306514                 {'amount': 8.2}  \n",
       "306515                 {'amount': 2.6}  \n",
       "306516                 {'amount': 9.2}  \n",
       "306517                {'amount': 11.7}  \n",
       "306518               {'amount': 40.67}  \n",
       "306519               {'amount': 31.13}  \n",
       "306520  {'amount': 1.6400000000000001}  \n",
       "306521               {'amount': 17.35}  \n",
       "306522                {'amount': 4.42}  \n",
       "306523               {'amount': 18.35}  \n",
       "306524               {'amount': 25.14}  \n",
       "306525               {'amount': 43.58}  \n",
       "306526               {'amount': 22.64}  \n",
       "306528                 {'amount': 2.2}  \n",
       "306529  {'amount': 1.5899999999999999}  \n",
       "306530                {'amount': 9.53}  \n",
       "306531                {'amount': 3.61}  \n",
       "306532  {'amount': 3.5300000000000002}  \n",
       "306533                {'amount': 4.05}  \n",
       "\n",
       "[138953 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transaction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Preprocessing** for portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10         7   \n",
       "1  [web, email, mobile, social]          10         5   \n",
       "2          [web, email, mobile]           0         4   \n",
       "3          [web, email, mobile]           5         7   \n",
       "4                  [web, email]          20        10   \n",
       "\n",
       "                                 id     offer_type  reward  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10         7   \n",
       "1  [web, email, mobile, social]          10         5   \n",
       "2          [web, email, mobile]           0         4   \n",
       "3          [web, email, mobile]           5         7   \n",
       "4                  [web, email]          20        10   \n",
       "\n",
       "                           offer_id     offer_type  reward  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename id to offer_id\n",
    "portfolio.rename(columns={'id':'offer_id'},inplace=True)\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>120</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>bogo</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>informational</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>168</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>bogo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>240</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>discount</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       channels  difficulty  duration  \\\n",
       "0       [email, mobile, social]          10       168   \n",
       "1  [web, email, mobile, social]          10       120   \n",
       "2          [web, email, mobile]           0        96   \n",
       "3          [web, email, mobile]           5       168   \n",
       "4                  [web, email]          20       240   \n",
       "\n",
       "                           offer_id     offer_type  reward  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd           bogo      10  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0           bogo      10  \n",
       "2  3f207df678b143eea3cee63160fa8bed  informational       0  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9           bogo       5  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7       discount       5  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert duration from days to hours\n",
    "portfolio['duration']=portfolio['duration'].apply(lambda x: x*24)\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename duration to duration_h\n",
    "portfolio.rename(columns={'duration':'duration_h'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#one hot encoding for channels\n",
    "portfolio['channel_email']=portfolio['channels'].apply(lambda x: 1 if 'email' in x else 0)\n",
    "portfolio['channel_mobile']=portfolio['channels'].apply(lambda x: 1 if 'mobile' in x else 0)\n",
    "portfolio['channel_social']=portfolio['channels'].apply(lambda x: 1 if 'social' in x else 0)\n",
    "portfolio['channel_web']=portfolio['channels'].apply(lambda x: 1 if 'web' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio.loc[portfolio['offer_type'] == 'bogo', 'offer_type'] = 1\n",
    "portfolio.loc[portfolio['offer_type'] == 'discount', 'offer_type'] = 2\n",
    "portfolio.loc[portfolio['offer_type'] == 'informational', 'offer_type'] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio.drop(labels='channels',axis=1,inplace=True)"
   ]
  },
  {
   "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>difficulty</th>\n",
       "      <th>duration_h</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>channel_email</th>\n",
       "      <th>channel_mobile</th>\n",
       "      <th>channel_social</th>\n",
       "      <th>channel_web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>120</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>168</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>240</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>168</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>240</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>120</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>168</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   difficulty  duration_h                          offer_id  offer_type  \\\n",
       "0          10         168  ae264e3637204a6fb9bb56bc8210ddfd           1   \n",
       "1          10         120  4d5c57ea9a6940dd891ad53e9dbe8da0           1   \n",
       "2           0          96  3f207df678b143eea3cee63160fa8bed           3   \n",
       "3           5         168  9b98b8c7a33c4b65b9aebfe6a799e6d9           1   \n",
       "4          20         240  0b1e1539f2cc45b7b9fa7c272da2e1d7           2   \n",
       "5           7         168  2298d6c36e964ae4a3e7e9706d1fb8c2           2   \n",
       "6          10         240  fafdcd668e3743c1bb461111dcafc2a4           2   \n",
       "7           0          72  5a8bc65990b245e5a138643cd4eb9837           3   \n",
       "8           5         120  f19421c1d4aa40978ebb69ca19b0e20d           1   \n",
       "9          10         168  2906b810c7d4411798c6938adc9daaa5           2   \n",
       "\n",
       "   reward  channel_email  channel_mobile  channel_social  channel_web  \n",
       "0      10              1               1               1            0  \n",
       "1      10              1               1               1            1  \n",
       "2       0              1               1               0            1  \n",
       "3       5              1               1               0            1  \n",
       "4       5              1               0               0            1  \n",
       "5       3              1               1               1            1  \n",
       "6       2              1               1               1            1  \n",
       "7       0              1               1               1            0  \n",
       "8       5              1               1               1            1  \n",
       "9       2              1               1               0            1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize difficulty and reward columns with minmax scalar\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler() \n",
    "x=['difficulty','reward']\n",
    "portfolio[x]  = scaler.fit_transform(portfolio[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>difficulty</th>\n",
       "      <th>duration_h</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>channel_email</th>\n",
       "      <th>channel_mobile</th>\n",
       "      <th>channel_social</th>\n",
       "      <th>channel_web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.50</td>\n",
       "      <td>168</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>120</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>96</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.25</td>\n",
       "      <td>168</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>240</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.35</td>\n",
       "      <td>168</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.50</td>\n",
       "      <td>240</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>72</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.25</td>\n",
       "      <td>120</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.50</td>\n",
       "      <td>168</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   difficulty  duration_h                          offer_id  offer_type  \\\n",
       "0        0.50         168  ae264e3637204a6fb9bb56bc8210ddfd           1   \n",
       "1        0.50         120  4d5c57ea9a6940dd891ad53e9dbe8da0           1   \n",
       "2        0.00          96  3f207df678b143eea3cee63160fa8bed           3   \n",
       "3        0.25         168  9b98b8c7a33c4b65b9aebfe6a799e6d9           1   \n",
       "4        1.00         240  0b1e1539f2cc45b7b9fa7c272da2e1d7           2   \n",
       "5        0.35         168  2298d6c36e964ae4a3e7e9706d1fb8c2           2   \n",
       "6        0.50         240  fafdcd668e3743c1bb461111dcafc2a4           2   \n",
       "7        0.00          72  5a8bc65990b245e5a138643cd4eb9837           3   \n",
       "8        0.25         120  f19421c1d4aa40978ebb69ca19b0e20d           1   \n",
       "9        0.50         168  2906b810c7d4411798c6938adc9daaa5           2   \n",
       "\n",
       "   reward  channel_email  channel_mobile  channel_social  channel_web  \n",
       "0     1.0              1               1               1            0  \n",
       "1     1.0              1               1               1            1  \n",
       "2     0.0              1               1               0            1  \n",
       "3     0.5              1               1               0            1  \n",
       "4     0.5              1               0               0            1  \n",
       "5     0.3              1               1               1            1  \n",
       "6     0.2              1               1               1            1  \n",
       "7     0.0              1               1               1            0  \n",
       "8     0.5              1               1               1            1  \n",
       "9     0.2              1               1               0            1  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transformation for offer id\n",
    "labels_offer_id = portfolio['offer_id'].astype('category').cat.categories.tolist()\n",
    "replace_offer_id = {'offer_id' : {k: v for k,v in zip(labels_offer_id,list(range(1,len(labels_offer_id)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'offer_id': {'0b1e1539f2cc45b7b9fa7c272da2e1d7': 1,\n",
       "  '2298d6c36e964ae4a3e7e9706d1fb8c2': 2,\n",
       "  '2906b810c7d4411798c6938adc9daaa5': 3,\n",
       "  '3f207df678b143eea3cee63160fa8bed': 4,\n",
       "  '4d5c57ea9a6940dd891ad53e9dbe8da0': 5,\n",
       "  '5a8bc65990b245e5a138643cd4eb9837': 6,\n",
       "  '9b98b8c7a33c4b65b9aebfe6a799e6d9': 7,\n",
       "  'ae264e3637204a6fb9bb56bc8210ddfd': 8,\n",
       "  'f19421c1d4aa40978ebb69ca19b0e20d': 9,\n",
       "  'fafdcd668e3743c1bb461111dcafc2a4': 10}}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replace_offer_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio.replace(replace_offer_id, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>difficulty</th>\n",
       "      <th>duration_h</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>channel_email</th>\n",
       "      <th>channel_mobile</th>\n",
       "      <th>channel_social</th>\n",
       "      <th>channel_web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.50</td>\n",
       "      <td>168</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>120</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.25</td>\n",
       "      <td>168</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>240</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.35</td>\n",
       "      <td>168</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.50</td>\n",
       "      <td>240</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.25</td>\n",
       "      <td>120</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.50</td>\n",
       "      <td>168</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   difficulty  duration_h  offer_id  offer_type  reward  channel_email  \\\n",
       "0        0.50         168         8           1     1.0              1   \n",
       "1        0.50         120         5           1     1.0              1   \n",
       "2        0.00          96         4           3     0.0              1   \n",
       "3        0.25         168         7           1     0.5              1   \n",
       "4        1.00         240         1           2     0.5              1   \n",
       "5        0.35         168         2           2     0.3              1   \n",
       "6        0.50         240        10           2     0.2              1   \n",
       "7        0.00          72         6           3     0.0              1   \n",
       "8        0.25         120         9           1     0.5              1   \n",
       "9        0.50         168         3           2     0.2              1   \n",
       "\n",
       "   channel_mobile  channel_social  channel_web  \n",
       "0               1               1            0  \n",
       "1               1               1            1  \n",
       "2               1               0            1  \n",
       "3               1               0            1  \n",
       "4               0               0            1  \n",
       "5               1               1            1  \n",
       "6               1               1            1  \n",
       "7               1               1            0  \n",
       "8               1               1            1  \n",
       "9               1               0            1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Processing on Profile**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>id</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>118</td>\n",
       "      <td>20170212</td>\n",
       "      <td>None</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>20180712</td>\n",
       "      <td>None</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118</td>\n",
       "      <td>20170804</td>\n",
       "      <td>None</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  became_member_on gender                                id    income\n",
       "0  118          20170212   None  68be06ca386d4c31939f3a4f0e3dd783       NaN\n",
       "1   55          20170715      F  0610b486422d4921ae7d2bf64640c50b  112000.0\n",
       "2  118          20180712   None  38fe809add3b4fcf9315a9694bb96ff5       NaN\n",
       "3   75          20170509      F  78afa995795e4d85b5d9ceeca43f5fef  100000.0\n",
       "4  118          20170804   None  a03223e636434f42ac4c3df47e8bac43       NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename id to customer_id\n",
    "profile.rename(columns={'id':'customer_id'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rearrange columns\n",
    "profile=profile.reindex(columns=['customer_id', 'age','became_member_on','gender','income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#transform cust id similar as offer id\n",
    "labels_cus_id = profile['customer_id'].astype('category').cat.categories.tolist()\n",
    "replace_cus_id = {'customer_id' : {k: v for k,v in zip(labels_cus_id,list(range(1,len(labels_cus_id)+1)))}}\n",
    "profile.replace(replace_cus_id, inplace=True)"
   ]
  },
  {
   "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>customer_id</th>\n",
       "      <th>age</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6962</td>\n",
       "      <td>118</td>\n",
       "      <td>20170212</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399</td>\n",
       "      <td>55</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>112000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3747</td>\n",
       "      <td>118</td>\n",
       "      <td>20180712</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7997</td>\n",
       "      <td>75</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10736</td>\n",
       "      <td>118</td>\n",
       "      <td>20170804</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15044</td>\n",
       "      <td>68</td>\n",
       "      <td>20180426</td>\n",
       "      <td>M</td>\n",
       "      <td>70000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9525</td>\n",
       "      <td>118</td>\n",
       "      <td>20170925</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6940</td>\n",
       "      <td>118</td>\n",
       "      <td>20171002</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3729</td>\n",
       "      <td>65</td>\n",
       "      <td>20180209</td>\n",
       "      <td>M</td>\n",
       "      <td>53000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9147</td>\n",
       "      <td>118</td>\n",
       "      <td>20161122</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13109</td>\n",
       "      <td>118</td>\n",
       "      <td>20170824</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1341</td>\n",
       "      <td>118</td>\n",
       "      <td>20150919</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3060</td>\n",
       "      <td>58</td>\n",
       "      <td>20171111</td>\n",
       "      <td>M</td>\n",
       "      <td>51000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11411</td>\n",
       "      <td>61</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>57000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14985</td>\n",
       "      <td>26</td>\n",
       "      <td>20140213</td>\n",
       "      <td>M</td>\n",
       "      <td>46000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3275</td>\n",
       "      <td>62</td>\n",
       "      <td>20160211</td>\n",
       "      <td>F</td>\n",
       "      <td>71000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6547</td>\n",
       "      <td>49</td>\n",
       "      <td>20141113</td>\n",
       "      <td>M</td>\n",
       "      <td>52000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7719</td>\n",
       "      <td>118</td>\n",
       "      <td>20170801</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6657</td>\n",
       "      <td>57</td>\n",
       "      <td>20171231</td>\n",
       "      <td>M</td>\n",
       "      <td>42000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11023</td>\n",
       "      <td>61</td>\n",
       "      <td>20180501</td>\n",
       "      <td>F</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4542</td>\n",
       "      <td>40</td>\n",
       "      <td>20160504</td>\n",
       "      <td>F</td>\n",
       "      <td>71000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4997</td>\n",
       "      <td>64</td>\n",
       "      <td>20170909</td>\n",
       "      <td>M</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12985</td>\n",
       "      <td>78</td>\n",
       "      <td>20170616</td>\n",
       "      <td>F</td>\n",
       "      <td>71000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2780</td>\n",
       "      <td>118</td>\n",
       "      <td>20170907</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16459</td>\n",
       "      <td>42</td>\n",
       "      <td>20130811</td>\n",
       "      <td>M</td>\n",
       "      <td>69000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13881</td>\n",
       "      <td>56</td>\n",
       "      <td>20180428</td>\n",
       "      <td>F</td>\n",
       "      <td>88000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6733</td>\n",
       "      <td>118</td>\n",
       "      <td>20170330</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13237</td>\n",
       "      <td>33</td>\n",
       "      <td>20170926</td>\n",
       "      <td>F</td>\n",
       "      <td>52000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7702</td>\n",
       "      <td>46</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>59000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15695</td>\n",
       "      <td>59</td>\n",
       "      <td>20150121</td>\n",
       "      <td>M</td>\n",
       "      <td>41000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>10930</td>\n",
       "      <td>67</td>\n",
       "      <td>20151107</td>\n",
       "      <td>M</td>\n",
       "      <td>94000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>7338</td>\n",
       "      <td>52</td>\n",
       "      <td>20180522</td>\n",
       "      <td>F</td>\n",
       "      <td>75000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>15111</td>\n",
       "      <td>44</td>\n",
       "      <td>20170119</td>\n",
       "      <td>F</td>\n",
       "      <td>51000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>564</td>\n",
       "      <td>30</td>\n",
       "      <td>20151012</td>\n",
       "      <td>M</td>\n",
       "      <td>57000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>16493</td>\n",
       "      <td>59</td>\n",
       "      <td>20160113</td>\n",
       "      <td>F</td>\n",
       "      <td>71000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>1762</td>\n",
       "      <td>61</td>\n",
       "      <td>20171231</td>\n",
       "      <td>F</td>\n",
       "      <td>81000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>1183</td>\n",
       "      <td>39</td>\n",
       "      <td>20160921</td>\n",
       "      <td>F</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16977</th>\n",
       "      <td>15663</td>\n",
       "      <td>118</td>\n",
       "      <td>20160609</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>5647</td>\n",
       "      <td>29</td>\n",
       "      <td>20170220</td>\n",
       "      <td>F</td>\n",
       "      <td>58000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>10723</td>\n",
       "      <td>63</td>\n",
       "      <td>20130922</td>\n",
       "      <td>F</td>\n",
       "      <td>52000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16980</th>\n",
       "      <td>6129</td>\n",
       "      <td>118</td>\n",
       "      <td>20160901</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>1640</td>\n",
       "      <td>84</td>\n",
       "      <td>20160629</td>\n",
       "      <td>M</td>\n",
       "      <td>93000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16982</th>\n",
       "      <td>14484</td>\n",
       "      <td>118</td>\n",
       "      <td>20160415</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>16960</td>\n",
       "      <td>72</td>\n",
       "      <td>20150404</td>\n",
       "      <td>F</td>\n",
       "      <td>65000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>3763</td>\n",
       "      <td>75</td>\n",
       "      <td>20160716</td>\n",
       "      <td>F</td>\n",
       "      <td>78000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>10140</td>\n",
       "      <td>26</td>\n",
       "      <td>20180619</td>\n",
       "      <td>M</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>12780</td>\n",
       "      <td>59</td>\n",
       "      <td>20170731</td>\n",
       "      <td>F</td>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>7882</td>\n",
       "      <td>57</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>16357</td>\n",
       "      <td>64</td>\n",
       "      <td>20180104</td>\n",
       "      <td>M</td>\n",
       "      <td>51000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16989</th>\n",
       "      <td>13481</td>\n",
       "      <td>118</td>\n",
       "      <td>20180305</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>8087</td>\n",
       "      <td>70</td>\n",
       "      <td>20160310</td>\n",
       "      <td>F</td>\n",
       "      <td>39000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16991</th>\n",
       "      <td>11375</td>\n",
       "      <td>118</td>\n",
       "      <td>20160116</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>4269</td>\n",
       "      <td>21</td>\n",
       "      <td>20170917</td>\n",
       "      <td>F</td>\n",
       "      <td>72000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>13524</td>\n",
       "      <td>60</td>\n",
       "      <td>20180505</td>\n",
       "      <td>M</td>\n",
       "      <td>113000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16994</th>\n",
       "      <td>12848</td>\n",
       "      <td>118</td>\n",
       "      <td>20151211</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>7265</td>\n",
       "      <td>45</td>\n",
       "      <td>20180604</td>\n",
       "      <td>F</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>2888</td>\n",
       "      <td>61</td>\n",
       "      <td>20180713</td>\n",
       "      <td>M</td>\n",
       "      <td>72000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>106</td>\n",
       "      <td>49</td>\n",
       "      <td>20170126</td>\n",
       "      <td>M</td>\n",
       "      <td>73000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>10568</td>\n",
       "      <td>83</td>\n",
       "      <td>20160307</td>\n",
       "      <td>F</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>15178</td>\n",
       "      <td>62</td>\n",
       "      <td>20170722</td>\n",
       "      <td>F</td>\n",
       "      <td>82000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  age  became_member_on gender    income\n",
       "0             6962  118          20170212   None       NaN\n",
       "1              399   55          20170715      F  112000.0\n",
       "2             3747  118          20180712   None       NaN\n",
       "3             7997   75          20170509      F  100000.0\n",
       "4            10736  118          20170804   None       NaN\n",
       "5            15044   68          20180426      M   70000.0\n",
       "6             9525  118          20170925   None       NaN\n",
       "7             6940  118          20171002   None       NaN\n",
       "8             3729   65          20180209      M   53000.0\n",
       "9             9147  118          20161122   None       NaN\n",
       "10           13109  118          20170824   None       NaN\n",
       "11            1341  118          20150919   None       NaN\n",
       "12            3060   58          20171111      M   51000.0\n",
       "13           11411   61          20170911      F   57000.0\n",
       "14           14985   26          20140213      M   46000.0\n",
       "15            3275   62          20160211      F   71000.0\n",
       "16            6547   49          20141113      M   52000.0\n",
       "17            7719  118          20170801   None       NaN\n",
       "18            6657   57          20171231      M   42000.0\n",
       "19           11023   61          20180501      F   40000.0\n",
       "20            4542   40          20160504      F   71000.0\n",
       "21            4997   64          20170909      M  100000.0\n",
       "22           12985   78          20170616      F   71000.0\n",
       "23            2780  118          20170907   None       NaN\n",
       "24           16459   42          20130811      M   69000.0\n",
       "25           13881   56          20180428      F   88000.0\n",
       "26            6733  118          20170330   None       NaN\n",
       "27           13237   33          20170926      F   52000.0\n",
       "28            7702   46          20170911      F   59000.0\n",
       "29           15695   59          20150121      M   41000.0\n",
       "...            ...  ...               ...    ...       ...\n",
       "16970        10930   67          20151107      M   94000.0\n",
       "16971         7338   52          20180522      F   75000.0\n",
       "16972        15111   44          20170119      F   51000.0\n",
       "16973          564   30          20151012      M   57000.0\n",
       "16974        16493   59          20160113      F   71000.0\n",
       "16975         1762   61          20171231      F   81000.0\n",
       "16976         1183   39          20160921      F   54000.0\n",
       "16977        15663  118          20160609   None       NaN\n",
       "16978         5647   29          20170220      F   58000.0\n",
       "16979        10723   63          20130922      F   52000.0\n",
       "16980         6129  118          20160901   None       NaN\n",
       "16981         1640   84          20160629      M   93000.0\n",
       "16982        14484  118          20160415   None       NaN\n",
       "16983        16960   72          20150404      F   65000.0\n",
       "16984         3763   75          20160716      F   78000.0\n",
       "16985        10140   26          20180619      M   55000.0\n",
       "16986        12780   59          20170731      F   63000.0\n",
       "16987         7882   57          20160709      M   40000.0\n",
       "16988        16357   64          20180104      M   51000.0\n",
       "16989        13481  118          20180305   None       NaN\n",
       "16990         8087   70          20160310      F   39000.0\n",
       "16991        11375  118          20160116   None       NaN\n",
       "16992         4269   21          20170917      F   72000.0\n",
       "16993        13524   60          20180505      M  113000.0\n",
       "16994        12848  118          20151211   None       NaN\n",
       "16995         7265   45          20180604      F   54000.0\n",
       "16996         2888   61          20180713      M   72000.0\n",
       "16997          106   49          20170126      M   73000.0\n",
       "16998        10568   83          20160307      F   50000.0\n",
       "16999        15178   62          20170722      F   82000.0\n",
       "\n",
       "[17000 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace age 118 with nan\n",
    "profile['age']=profile['age'].apply(lambda x: np.nan if x==118 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop values with nan in age income gender\n",
    "profile=profile.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id         0\n",
       "age                 0\n",
       "became_member_on    0\n",
       "gender              0\n",
       "income              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['age'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change float to int for age and income\n",
    "profile = profile.astype({\"age\": int, \"income\": int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14825 entries, 1 to 16999\n",
      "Data columns (total 5 columns):\n",
      "customer_id         14825 non-null int64\n",
      "age                 14825 non-null int64\n",
      "became_member_on    14825 non-null int64\n",
      "gender              14825 non-null object\n",
      "income              14825 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 694.9+ KB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=profile['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new column grouped wiith age group\n",
    "profile['age_group']=pd.cut(profile['age'],bins=[17,22,35,60,103],labels=['teenager','young-adult','adult','elderly'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=profile['age_group'].astype('category').cat.categories.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "y={\"age_group\": {k:v for k,v in zip(x,list(range(1,len(x)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.replace(y,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.drop(columns=\"age\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>income</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>112000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7997</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>100000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15044</td>\n",
       "      <td>20180426</td>\n",
       "      <td>M</td>\n",
       "      <td>70000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3729</td>\n",
       "      <td>20180209</td>\n",
       "      <td>M</td>\n",
       "      <td>53000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3060</td>\n",
       "      <td>20171111</td>\n",
       "      <td>M</td>\n",
       "      <td>51000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11411</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>57000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14985</td>\n",
       "      <td>20140213</td>\n",
       "      <td>M</td>\n",
       "      <td>46000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3275</td>\n",
       "      <td>20160211</td>\n",
       "      <td>F</td>\n",
       "      <td>71000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6547</td>\n",
       "      <td>20141113</td>\n",
       "      <td>M</td>\n",
       "      <td>52000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6657</td>\n",
       "      <td>20171231</td>\n",
       "      <td>M</td>\n",
       "      <td>42000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11023</td>\n",
       "      <td>20180501</td>\n",
       "      <td>F</td>\n",
       "      <td>40000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4542</td>\n",
       "      <td>20160504</td>\n",
       "      <td>F</td>\n",
       "      <td>71000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4997</td>\n",
       "      <td>20170909</td>\n",
       "      <td>M</td>\n",
       "      <td>100000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12985</td>\n",
       "      <td>20170616</td>\n",
       "      <td>F</td>\n",
       "      <td>71000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16459</td>\n",
       "      <td>20130811</td>\n",
       "      <td>M</td>\n",
       "      <td>69000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13881</td>\n",
       "      <td>20180428</td>\n",
       "      <td>F</td>\n",
       "      <td>88000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13237</td>\n",
       "      <td>20170926</td>\n",
       "      <td>F</td>\n",
       "      <td>52000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7702</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>59000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15695</td>\n",
       "      <td>20150121</td>\n",
       "      <td>M</td>\n",
       "      <td>41000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5915</td>\n",
       "      <td>20140823</td>\n",
       "      <td>M</td>\n",
       "      <td>96000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13995</td>\n",
       "      <td>20170916</td>\n",
       "      <td>O</td>\n",
       "      <td>52000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1962</td>\n",
       "      <td>20170925</td>\n",
       "      <td>M</td>\n",
       "      <td>70000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8941</td>\n",
       "      <td>20171117</td>\n",
       "      <td>F</td>\n",
       "      <td>89000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>15981</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>33000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1052</td>\n",
       "      <td>20160814</td>\n",
       "      <td>F</td>\n",
       "      <td>57000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9458</td>\n",
       "      <td>20140511</td>\n",
       "      <td>M</td>\n",
       "      <td>68000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10669</td>\n",
       "      <td>20170812</td>\n",
       "      <td>F</td>\n",
       "      <td>63000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7308</td>\n",
       "      <td>20151112</td>\n",
       "      <td>M</td>\n",
       "      <td>40000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>170</td>\n",
       "      <td>20160711</td>\n",
       "      <td>F</td>\n",
       "      <td>30000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12891</td>\n",
       "      <td>20180707</td>\n",
       "      <td>M</td>\n",
       "      <td>33000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16963</th>\n",
       "      <td>9354</td>\n",
       "      <td>20170112</td>\n",
       "      <td>F</td>\n",
       "      <td>80000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>6914</td>\n",
       "      <td>20180408</td>\n",
       "      <td>M</td>\n",
       "      <td>62000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>15132</td>\n",
       "      <td>20180409</td>\n",
       "      <td>M</td>\n",
       "      <td>34000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>1054</td>\n",
       "      <td>20150126</td>\n",
       "      <td>M</td>\n",
       "      <td>51000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>13778</td>\n",
       "      <td>20150626</td>\n",
       "      <td>M</td>\n",
       "      <td>53000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>2864</td>\n",
       "      <td>20170725</td>\n",
       "      <td>M</td>\n",
       "      <td>87000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>10930</td>\n",
       "      <td>20151107</td>\n",
       "      <td>M</td>\n",
       "      <td>94000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>7338</td>\n",
       "      <td>20180522</td>\n",
       "      <td>F</td>\n",
       "      <td>75000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>15111</td>\n",
       "      <td>20170119</td>\n",
       "      <td>F</td>\n",
       "      <td>51000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>564</td>\n",
       "      <td>20151012</td>\n",
       "      <td>M</td>\n",
       "      <td>57000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>16493</td>\n",
       "      <td>20160113</td>\n",
       "      <td>F</td>\n",
       "      <td>71000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>1762</td>\n",
       "      <td>20171231</td>\n",
       "      <td>F</td>\n",
       "      <td>81000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>1183</td>\n",
       "      <td>20160921</td>\n",
       "      <td>F</td>\n",
       "      <td>54000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>5647</td>\n",
       "      <td>20170220</td>\n",
       "      <td>F</td>\n",
       "      <td>58000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>10723</td>\n",
       "      <td>20130922</td>\n",
       "      <td>F</td>\n",
       "      <td>52000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>1640</td>\n",
       "      <td>20160629</td>\n",
       "      <td>M</td>\n",
       "      <td>93000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>16960</td>\n",
       "      <td>20150404</td>\n",
       "      <td>F</td>\n",
       "      <td>65000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>3763</td>\n",
       "      <td>20160716</td>\n",
       "      <td>F</td>\n",
       "      <td>78000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>10140</td>\n",
       "      <td>20180619</td>\n",
       "      <td>M</td>\n",
       "      <td>55000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>12780</td>\n",
       "      <td>20170731</td>\n",
       "      <td>F</td>\n",
       "      <td>63000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>7882</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>40000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>16357</td>\n",
       "      <td>20180104</td>\n",
       "      <td>M</td>\n",
       "      <td>51000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>8087</td>\n",
       "      <td>20160310</td>\n",
       "      <td>F</td>\n",
       "      <td>39000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>4269</td>\n",
       "      <td>20170917</td>\n",
       "      <td>F</td>\n",
       "      <td>72000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>13524</td>\n",
       "      <td>20180505</td>\n",
       "      <td>M</td>\n",
       "      <td>113000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>7265</td>\n",
       "      <td>20180604</td>\n",
       "      <td>F</td>\n",
       "      <td>54000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>2888</td>\n",
       "      <td>20180713</td>\n",
       "      <td>M</td>\n",
       "      <td>72000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>106</td>\n",
       "      <td>20170126</td>\n",
       "      <td>M</td>\n",
       "      <td>73000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>10568</td>\n",
       "      <td>20160307</td>\n",
       "      <td>F</td>\n",
       "      <td>50000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>15178</td>\n",
       "      <td>20170722</td>\n",
       "      <td>F</td>\n",
       "      <td>82000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  became_member_on gender  income  age_group\n",
       "1              399          20170715      F  112000          3\n",
       "3             7997          20170509      F  100000          4\n",
       "5            15044          20180426      M   70000          4\n",
       "8             3729          20180209      M   53000          4\n",
       "12            3060          20171111      M   51000          3\n",
       "13           11411          20170911      F   57000          4\n",
       "14           14985          20140213      M   46000          2\n",
       "15            3275          20160211      F   71000          4\n",
       "16            6547          20141113      M   52000          3\n",
       "18            6657          20171231      M   42000          3\n",
       "19           11023          20180501      F   40000          4\n",
       "20            4542          20160504      F   71000          3\n",
       "21            4997          20170909      M  100000          4\n",
       "22           12985          20170616      F   71000          4\n",
       "24           16459          20130811      M   69000          3\n",
       "25           13881          20180428      F   88000          3\n",
       "27           13237          20170926      F   52000          2\n",
       "28            7702          20170911      F   59000          3\n",
       "29           15695          20150121      M   41000          3\n",
       "30            5915          20140823      M   96000          4\n",
       "31           13995          20170916      O   52000          3\n",
       "32            1962          20170925      M   70000          1\n",
       "33            8941          20171117      F   89000          4\n",
       "34           15981          20160709      M   33000          3\n",
       "35            1052          20160814      F   57000          4\n",
       "37            9458          20140511      M   68000          3\n",
       "38           10669          20170812      F   63000          2\n",
       "40            7308          20151112      M   40000          4\n",
       "41             170          20160711      F   30000          1\n",
       "42           12891          20180707      M   33000          3\n",
       "...            ...               ...    ...     ...        ...\n",
       "16963         9354          20170112      F   80000          4\n",
       "16964         6914          20180408      M   62000          3\n",
       "16965        15132          20180409      M   34000          3\n",
       "16966         1054          20150126      M   51000          3\n",
       "16967        13778          20150626      M   53000          3\n",
       "16968         2864          20170725      M   87000          4\n",
       "16970        10930          20151107      M   94000          4\n",
       "16971         7338          20180522      F   75000          3\n",
       "16972        15111          20170119      F   51000          3\n",
       "16973          564          20151012      M   57000          2\n",
       "16974        16493          20160113      F   71000          3\n",
       "16975         1762          20171231      F   81000          4\n",
       "16976         1183          20160921      F   54000          3\n",
       "16978         5647          20170220      F   58000          2\n",
       "16979        10723          20130922      F   52000          4\n",
       "16981         1640          20160629      M   93000          4\n",
       "16983        16960          20150404      F   65000          4\n",
       "16984         3763          20160716      F   78000          4\n",
       "16985        10140          20180619      M   55000          2\n",
       "16986        12780          20170731      F   63000          3\n",
       "16987         7882          20160709      M   40000          3\n",
       "16988        16357          20180104      M   51000          4\n",
       "16990         8087          20160310      F   39000          4\n",
       "16992         4269          20170917      F   72000          1\n",
       "16993        13524          20180505      M  113000          3\n",
       "16995         7265          20180604      F   54000          3\n",
       "16996         2888          20180713      M   72000          4\n",
       "16997          106          20170126      M   73000          3\n",
       "16998        10568          20160307      F   50000          4\n",
       "16999        15178          20170722      F   82000          4\n",
       "\n",
       "[14825 rows x 5 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new column income range\n",
    "profile['income_range']=pd.cut(profile['income'],bins=[29999,60000,90000,120001],labels=['average','above_average','high'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1              high\n",
       "3              high\n",
       "5     above_average\n",
       "8           average\n",
       "12          average\n",
       "Name: income_range, dtype: category\n",
       "Categories (3, object): [average < above_average < high]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['income_range'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile=profile.drop(columns=\"income\")"
   ]
  },
  {
   "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>customer_id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>age_group</th>\n",
       "      <th>income_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7997</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15044</td>\n",
       "      <td>20180426</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3729</td>\n",
       "      <td>20180209</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3060</td>\n",
       "      <td>20171111</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11411</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14985</td>\n",
       "      <td>20140213</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3275</td>\n",
       "      <td>20160211</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6547</td>\n",
       "      <td>20141113</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6657</td>\n",
       "      <td>20171231</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11023</td>\n",
       "      <td>20180501</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4542</td>\n",
       "      <td>20160504</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4997</td>\n",
       "      <td>20170909</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12985</td>\n",
       "      <td>20170616</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16459</td>\n",
       "      <td>20130811</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13881</td>\n",
       "      <td>20180428</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13237</td>\n",
       "      <td>20170926</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7702</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15695</td>\n",
       "      <td>20150121</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5915</td>\n",
       "      <td>20140823</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13995</td>\n",
       "      <td>20170916</td>\n",
       "      <td>O</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1962</td>\n",
       "      <td>20170925</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8941</td>\n",
       "      <td>20171117</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>15981</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1052</td>\n",
       "      <td>20160814</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9458</td>\n",
       "      <td>20140511</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10669</td>\n",
       "      <td>20170812</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7308</td>\n",
       "      <td>20151112</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>170</td>\n",
       "      <td>20160711</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12891</td>\n",
       "      <td>20180707</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16963</th>\n",
       "      <td>9354</td>\n",
       "      <td>20170112</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>6914</td>\n",
       "      <td>20180408</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>15132</td>\n",
       "      <td>20180409</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>1054</td>\n",
       "      <td>20150126</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>13778</td>\n",
       "      <td>20150626</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>2864</td>\n",
       "      <td>20170725</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>10930</td>\n",
       "      <td>20151107</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>7338</td>\n",
       "      <td>20180522</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>15111</td>\n",
       "      <td>20170119</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>564</td>\n",
       "      <td>20151012</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>16493</td>\n",
       "      <td>20160113</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>1762</td>\n",
       "      <td>20171231</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>1183</td>\n",
       "      <td>20160921</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>5647</td>\n",
       "      <td>20170220</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>10723</td>\n",
       "      <td>20130922</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>1640</td>\n",
       "      <td>20160629</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>16960</td>\n",
       "      <td>20150404</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>3763</td>\n",
       "      <td>20160716</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>10140</td>\n",
       "      <td>20180619</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>12780</td>\n",
       "      <td>20170731</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>7882</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>16357</td>\n",
       "      <td>20180104</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>8087</td>\n",
       "      <td>20160310</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>4269</td>\n",
       "      <td>20170917</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>13524</td>\n",
       "      <td>20180505</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>7265</td>\n",
       "      <td>20180604</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>2888</td>\n",
       "      <td>20180713</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>106</td>\n",
       "      <td>20170126</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>10568</td>\n",
       "      <td>20160307</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>average</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>15178</td>\n",
       "      <td>20170722</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>above_average</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  became_member_on gender  age_group   income_range\n",
       "1              399          20170715      F          3           high\n",
       "3             7997          20170509      F          4           high\n",
       "5            15044          20180426      M          4  above_average\n",
       "8             3729          20180209      M          4        average\n",
       "12            3060          20171111      M          3        average\n",
       "13           11411          20170911      F          4        average\n",
       "14           14985          20140213      M          2        average\n",
       "15            3275          20160211      F          4  above_average\n",
       "16            6547          20141113      M          3        average\n",
       "18            6657          20171231      M          3        average\n",
       "19           11023          20180501      F          4        average\n",
       "20            4542          20160504      F          3  above_average\n",
       "21            4997          20170909      M          4           high\n",
       "22           12985          20170616      F          4  above_average\n",
       "24           16459          20130811      M          3  above_average\n",
       "25           13881          20180428      F          3  above_average\n",
       "27           13237          20170926      F          2        average\n",
       "28            7702          20170911      F          3        average\n",
       "29           15695          20150121      M          3        average\n",
       "30            5915          20140823      M          4           high\n",
       "31           13995          20170916      O          3        average\n",
       "32            1962          20170925      M          1  above_average\n",
       "33            8941          20171117      F          4  above_average\n",
       "34           15981          20160709      M          3        average\n",
       "35            1052          20160814      F          4        average\n",
       "37            9458          20140511      M          3  above_average\n",
       "38           10669          20170812      F          2  above_average\n",
       "40            7308          20151112      M          4        average\n",
       "41             170          20160711      F          1        average\n",
       "42           12891          20180707      M          3        average\n",
       "...            ...               ...    ...        ...            ...\n",
       "16963         9354          20170112      F          4  above_average\n",
       "16964         6914          20180408      M          3  above_average\n",
       "16965        15132          20180409      M          3        average\n",
       "16966         1054          20150126      M          3        average\n",
       "16967        13778          20150626      M          3        average\n",
       "16968         2864          20170725      M          4  above_average\n",
       "16970        10930          20151107      M          4           high\n",
       "16971         7338          20180522      F          3  above_average\n",
       "16972        15111          20170119      F          3        average\n",
       "16973          564          20151012      M          2        average\n",
       "16974        16493          20160113      F          3  above_average\n",
       "16975         1762          20171231      F          4  above_average\n",
       "16976         1183          20160921      F          3        average\n",
       "16978         5647          20170220      F          2        average\n",
       "16979        10723          20130922      F          4        average\n",
       "16981         1640          20160629      M          4           high\n",
       "16983        16960          20150404      F          4  above_average\n",
       "16984         3763          20160716      F          4  above_average\n",
       "16985        10140          20180619      M          2        average\n",
       "16986        12780          20170731      F          3  above_average\n",
       "16987         7882          20160709      M          3        average\n",
       "16988        16357          20180104      M          4        average\n",
       "16990         8087          20160310      F          4        average\n",
       "16992         4269          20170917      F          1  above_average\n",
       "16993        13524          20180505      M          3           high\n",
       "16995         7265          20180604      F          3        average\n",
       "16996         2888          20180713      M          4  above_average\n",
       "16997          106          20170126      M          3  above_average\n",
       "16998        10568          20160307      F          4        average\n",
       "16999        15178          20170722      F          4  above_average\n",
       "\n",
       "[14825 rows x 5 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=profile['income_range'].astype('category').cat.categories.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "x={'income_range':{k:v for k,v in zip(labels,list(range(1,len(labels)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.replace(x,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>age_group</th>\n",
       "      <th>income_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399</td>\n",
       "      <td>20170715</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7997</td>\n",
       "      <td>20170509</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15044</td>\n",
       "      <td>20180426</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3729</td>\n",
       "      <td>20180209</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3060</td>\n",
       "      <td>20171111</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11411</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14985</td>\n",
       "      <td>20140213</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3275</td>\n",
       "      <td>20160211</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6547</td>\n",
       "      <td>20141113</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6657</td>\n",
       "      <td>20171231</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11023</td>\n",
       "      <td>20180501</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4542</td>\n",
       "      <td>20160504</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4997</td>\n",
       "      <td>20170909</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12985</td>\n",
       "      <td>20170616</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16459</td>\n",
       "      <td>20130811</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13881</td>\n",
       "      <td>20180428</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13237</td>\n",
       "      <td>20170926</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7702</td>\n",
       "      <td>20170911</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15695</td>\n",
       "      <td>20150121</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5915</td>\n",
       "      <td>20140823</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13995</td>\n",
       "      <td>20170916</td>\n",
       "      <td>O</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1962</td>\n",
       "      <td>20170925</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8941</td>\n",
       "      <td>20171117</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>15981</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1052</td>\n",
       "      <td>20160814</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9458</td>\n",
       "      <td>20140511</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10669</td>\n",
       "      <td>20170812</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7308</td>\n",
       "      <td>20151112</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>170</td>\n",
       "      <td>20160711</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12891</td>\n",
       "      <td>20180707</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16963</th>\n",
       "      <td>9354</td>\n",
       "      <td>20170112</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>6914</td>\n",
       "      <td>20180408</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>15132</td>\n",
       "      <td>20180409</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>1054</td>\n",
       "      <td>20150126</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>13778</td>\n",
       "      <td>20150626</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>2864</td>\n",
       "      <td>20170725</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>10930</td>\n",
       "      <td>20151107</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>7338</td>\n",
       "      <td>20180522</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>15111</td>\n",
       "      <td>20170119</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>564</td>\n",
       "      <td>20151012</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>16493</td>\n",
       "      <td>20160113</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>1762</td>\n",
       "      <td>20171231</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>1183</td>\n",
       "      <td>20160921</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>5647</td>\n",
       "      <td>20170220</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>10723</td>\n",
       "      <td>20130922</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>1640</td>\n",
       "      <td>20160629</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>16960</td>\n",
       "      <td>20150404</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>3763</td>\n",
       "      <td>20160716</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>10140</td>\n",
       "      <td>20180619</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>12780</td>\n",
       "      <td>20170731</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>7882</td>\n",
       "      <td>20160709</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>16357</td>\n",
       "      <td>20180104</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>8087</td>\n",
       "      <td>20160310</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>4269</td>\n",
       "      <td>20170917</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>13524</td>\n",
       "      <td>20180505</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>7265</td>\n",
       "      <td>20180604</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>2888</td>\n",
       "      <td>20180713</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>106</td>\n",
       "      <td>20170126</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>10568</td>\n",
       "      <td>20160307</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>15178</td>\n",
       "      <td>20170722</td>\n",
       "      <td>F</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  became_member_on gender  age_group  income_range\n",
       "1              399          20170715      F          3             3\n",
       "3             7997          20170509      F          4             3\n",
       "5            15044          20180426      M          4             2\n",
       "8             3729          20180209      M          4             1\n",
       "12            3060          20171111      M          3             1\n",
       "13           11411          20170911      F          4             1\n",
       "14           14985          20140213      M          2             1\n",
       "15            3275          20160211      F          4             2\n",
       "16            6547          20141113      M          3             1\n",
       "18            6657          20171231      M          3             1\n",
       "19           11023          20180501      F          4             1\n",
       "20            4542          20160504      F          3             2\n",
       "21            4997          20170909      M          4             3\n",
       "22           12985          20170616      F          4             2\n",
       "24           16459          20130811      M          3             2\n",
       "25           13881          20180428      F          3             2\n",
       "27           13237          20170926      F          2             1\n",
       "28            7702          20170911      F          3             1\n",
       "29           15695          20150121      M          3             1\n",
       "30            5915          20140823      M          4             3\n",
       "31           13995          20170916      O          3             1\n",
       "32            1962          20170925      M          1             2\n",
       "33            8941          20171117      F          4             2\n",
       "34           15981          20160709      M          3             1\n",
       "35            1052          20160814      F          4             1\n",
       "37            9458          20140511      M          3             2\n",
       "38           10669          20170812      F          2             2\n",
       "40            7308          20151112      M          4             1\n",
       "41             170          20160711      F          1             1\n",
       "42           12891          20180707      M          3             1\n",
       "...            ...               ...    ...        ...           ...\n",
       "16963         9354          20170112      F          4             2\n",
       "16964         6914          20180408      M          3             2\n",
       "16965        15132          20180409      M          3             1\n",
       "16966         1054          20150126      M          3             1\n",
       "16967        13778          20150626      M          3             1\n",
       "16968         2864          20170725      M          4             2\n",
       "16970        10930          20151107      M          4             3\n",
       "16971         7338          20180522      F          3             2\n",
       "16972        15111          20170119      F          3             1\n",
       "16973          564          20151012      M          2             1\n",
       "16974        16493          20160113      F          3             2\n",
       "16975         1762          20171231      F          4             2\n",
       "16976         1183          20160921      F          3             1\n",
       "16978         5647          20170220      F          2             1\n",
       "16979        10723          20130922      F          4             1\n",
       "16981         1640          20160629      M          4             3\n",
       "16983        16960          20150404      F          4             2\n",
       "16984         3763          20160716      F          4             2\n",
       "16985        10140          20180619      M          2             1\n",
       "16986        12780          20170731      F          3             2\n",
       "16987         7882          20160709      M          3             1\n",
       "16988        16357          20180104      M          4             1\n",
       "16990         8087          20160310      F          4             1\n",
       "16992         4269          20170917      F          1             2\n",
       "16993        13524          20180505      M          3             3\n",
       "16995         7265          20180604      F          3             1\n",
       "16996         2888          20180713      M          4             2\n",
       "16997          106          20170126      M          3             2\n",
       "16998        10568          20160307      F          4             1\n",
       "16999        15178          20170722      F          4             2\n",
       "\n",
       "[14825 rows x 5 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gender to 1 2 3\n",
    "labels=profile['gender'].astype('category').cat.categories.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "x={'gender':{k:v for k,v in zip(labels,list(range(1,len(labels)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.replace(x,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile['became_member_on']=pd.to_datetime(profile['became_member_on'],format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       2017-07-15\n",
       "3       2017-05-09\n",
       "5       2018-04-26\n",
       "8       2018-02-09\n",
       "12      2017-11-11\n",
       "13      2017-09-11\n",
       "14      2014-02-13\n",
       "15      2016-02-11\n",
       "16      2014-11-13\n",
       "18      2017-12-31\n",
       "19      2018-05-01\n",
       "20      2016-05-04\n",
       "21      2017-09-09\n",
       "22      2017-06-16\n",
       "24      2013-08-11\n",
       "25      2018-04-28\n",
       "27      2017-09-26\n",
       "28      2017-09-11\n",
       "29      2015-01-21\n",
       "30      2014-08-23\n",
       "31      2017-09-16\n",
       "32      2017-09-25\n",
       "33      2017-11-17\n",
       "34      2016-07-09\n",
       "35      2016-08-14\n",
       "37      2014-05-11\n",
       "38      2017-08-12\n",
       "40      2015-11-12\n",
       "41      2016-07-11\n",
       "42      2018-07-07\n",
       "           ...    \n",
       "16963   2017-01-12\n",
       "16964   2018-04-08\n",
       "16965   2018-04-09\n",
       "16966   2015-01-26\n",
       "16967   2015-06-26\n",
       "16968   2017-07-25\n",
       "16970   2015-11-07\n",
       "16971   2018-05-22\n",
       "16972   2017-01-19\n",
       "16973   2015-10-12\n",
       "16974   2016-01-13\n",
       "16975   2017-12-31\n",
       "16976   2016-09-21\n",
       "16978   2017-02-20\n",
       "16979   2013-09-22\n",
       "16981   2016-06-29\n",
       "16983   2015-04-04\n",
       "16984   2016-07-16\n",
       "16985   2018-06-19\n",
       "16986   2017-07-31\n",
       "16987   2016-07-09\n",
       "16988   2018-01-04\n",
       "16990   2016-03-10\n",
       "16992   2017-09-17\n",
       "16993   2018-05-05\n",
       "16995   2018-06-04\n",
       "16996   2018-07-13\n",
       "16997   2017-01-26\n",
       "16998   2016-03-07\n",
       "16999   2017-07-22\n",
       "Name: became_member_on, Length: 14825, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['became_member_on']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile['membership_year']=pd.to_datetime(profile['became_member_on']).dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        2017\n",
       "3        2017\n",
       "5        2018\n",
       "8        2018\n",
       "12       2017\n",
       "13       2017\n",
       "14       2014\n",
       "15       2016\n",
       "16       2014\n",
       "18       2017\n",
       "19       2018\n",
       "20       2016\n",
       "21       2017\n",
       "22       2017\n",
       "24       2013\n",
       "25       2018\n",
       "27       2017\n",
       "28       2017\n",
       "29       2015\n",
       "30       2014\n",
       "31       2017\n",
       "32       2017\n",
       "33       2017\n",
       "34       2016\n",
       "35       2016\n",
       "37       2014\n",
       "38       2017\n",
       "40       2015\n",
       "41       2016\n",
       "42       2018\n",
       "         ... \n",
       "16963    2017\n",
       "16964    2018\n",
       "16965    2018\n",
       "16966    2015\n",
       "16967    2015\n",
       "16968    2017\n",
       "16970    2015\n",
       "16971    2018\n",
       "16972    2017\n",
       "16973    2015\n",
       "16974    2016\n",
       "16975    2017\n",
       "16976    2016\n",
       "16978    2017\n",
       "16979    2013\n",
       "16981    2016\n",
       "16983    2015\n",
       "16984    2016\n",
       "16985    2018\n",
       "16986    2017\n",
       "16987    2016\n",
       "16988    2018\n",
       "16990    2016\n",
       "16992    2017\n",
       "16993    2018\n",
       "16995    2018\n",
       "16996    2018\n",
       "16997    2017\n",
       "16998    2016\n",
       "16999    2017\n",
       "Name: membership_year, Length: 14825, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['membership_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "profile['membership_days']=datetime.datetime.today().date() - profile['became_member_on'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile['membership_days']=profile['membership_days'].dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>became_member_on</th>\n",
       "      <th>gender</th>\n",
       "      <th>age_group</th>\n",
       "      <th>income_range</th>\n",
       "      <th>membership_year</th>\n",
       "      <th>membership_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>399</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7997</td>\n",
       "      <td>2017-05-09</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>1109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15044</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3729</td>\n",
       "      <td>2018-02-09</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3060</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11411</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14985</td>\n",
       "      <td>2014-02-13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>2290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3275</td>\n",
       "      <td>2016-02-11</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6547</td>\n",
       "      <td>2014-11-13</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6657</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11023</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4542</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4997</td>\n",
       "      <td>2017-09-09</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>12985</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16459</td>\n",
       "      <td>2013-08-11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>2476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13881</td>\n",
       "      <td>2018-04-28</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13237</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7702</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>15695</td>\n",
       "      <td>2015-01-21</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5915</td>\n",
       "      <td>2014-08-23</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13995</td>\n",
       "      <td>2017-09-16</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1962</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8941</td>\n",
       "      <td>2017-11-17</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>15981</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1052</td>\n",
       "      <td>2016-08-14</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>9458</td>\n",
       "      <td>2014-05-11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>2203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10669</td>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>7308</td>\n",
       "      <td>2015-11-12</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>170</td>\n",
       "      <td>2016-07-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>12891</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>16963</th>\n",
       "      <td>9354</td>\n",
       "      <td>2017-01-12</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16964</th>\n",
       "      <td>6914</td>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16965</th>\n",
       "      <td>15132</td>\n",
       "      <td>2018-04-09</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16966</th>\n",
       "      <td>1054</td>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16967</th>\n",
       "      <td>13778</td>\n",
       "      <td>2015-06-26</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16968</th>\n",
       "      <td>2864</td>\n",
       "      <td>2017-07-25</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>10930</td>\n",
       "      <td>2015-11-07</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16971</th>\n",
       "      <td>7338</td>\n",
       "      <td>2018-05-22</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16972</th>\n",
       "      <td>15111</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16973</th>\n",
       "      <td>564</td>\n",
       "      <td>2015-10-12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16974</th>\n",
       "      <td>16493</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>1762</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16976</th>\n",
       "      <td>1183</td>\n",
       "      <td>2016-09-21</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16978</th>\n",
       "      <td>5647</td>\n",
       "      <td>2017-02-20</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>1187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16979</th>\n",
       "      <td>10723</td>\n",
       "      <td>2013-09-22</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>2434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16981</th>\n",
       "      <td>1640</td>\n",
       "      <td>2016-06-29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16983</th>\n",
       "      <td>16960</td>\n",
       "      <td>2015-04-04</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>1875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16984</th>\n",
       "      <td>3763</td>\n",
       "      <td>2016-07-16</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16985</th>\n",
       "      <td>10140</td>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16986</th>\n",
       "      <td>12780</td>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16987</th>\n",
       "      <td>7882</td>\n",
       "      <td>2016-07-09</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16988</th>\n",
       "      <td>16357</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16990</th>\n",
       "      <td>8087</td>\n",
       "      <td>2016-03-10</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16992</th>\n",
       "      <td>4269</td>\n",
       "      <td>2017-09-17</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16993</th>\n",
       "      <td>13524</td>\n",
       "      <td>2018-05-05</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16995</th>\n",
       "      <td>7265</td>\n",
       "      <td>2018-06-04</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16996</th>\n",
       "      <td>2888</td>\n",
       "      <td>2018-07-13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16997</th>\n",
       "      <td>106</td>\n",
       "      <td>2017-01-26</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16998</th>\n",
       "      <td>10568</td>\n",
       "      <td>2016-03-07</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16999</th>\n",
       "      <td>15178</td>\n",
       "      <td>2017-07-22</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>1035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id became_member_on  gender  age_group  income_range  \\\n",
       "1              399       2017-07-15       1          3             3   \n",
       "3             7997       2017-05-09       1          4             3   \n",
       "5            15044       2018-04-26       2          4             2   \n",
       "8             3729       2018-02-09       2          4             1   \n",
       "12            3060       2017-11-11       2          3             1   \n",
       "13           11411       2017-09-11       1          4             1   \n",
       "14           14985       2014-02-13       2          2             1   \n",
       "15            3275       2016-02-11       1          4             2   \n",
       "16            6547       2014-11-13       2          3             1   \n",
       "18            6657       2017-12-31       2          3             1   \n",
       "19           11023       2018-05-01       1          4             1   \n",
       "20            4542       2016-05-04       1          3             2   \n",
       "21            4997       2017-09-09       2          4             3   \n",
       "22           12985       2017-06-16       1          4             2   \n",
       "24           16459       2013-08-11       2          3             2   \n",
       "25           13881       2018-04-28       1          3             2   \n",
       "27           13237       2017-09-26       1          2             1   \n",
       "28            7702       2017-09-11       1          3             1   \n",
       "29           15695       2015-01-21       2          3             1   \n",
       "30            5915       2014-08-23       2          4             3   \n",
       "31           13995       2017-09-16       3          3             1   \n",
       "32            1962       2017-09-25       2          1             2   \n",
       "33            8941       2017-11-17       1          4             2   \n",
       "34           15981       2016-07-09       2          3             1   \n",
       "35            1052       2016-08-14       1          4             1   \n",
       "37            9458       2014-05-11       2          3             2   \n",
       "38           10669       2017-08-12       1          2             2   \n",
       "40            7308       2015-11-12       2          4             1   \n",
       "41             170       2016-07-11       1          1             1   \n",
       "42           12891       2018-07-07       2          3             1   \n",
       "...            ...              ...     ...        ...           ...   \n",
       "16963         9354       2017-01-12       1          4             2   \n",
       "16964         6914       2018-04-08       2          3             2   \n",
       "16965        15132       2018-04-09       2          3             1   \n",
       "16966         1054       2015-01-26       2          3             1   \n",
       "16967        13778       2015-06-26       2          3             1   \n",
       "16968         2864       2017-07-25       2          4             2   \n",
       "16970        10930       2015-11-07       2          4             3   \n",
       "16971         7338       2018-05-22       1          3             2   \n",
       "16972        15111       2017-01-19       1          3             1   \n",
       "16973          564       2015-10-12       2          2             1   \n",
       "16974        16493       2016-01-13       1          3             2   \n",
       "16975         1762       2017-12-31       1          4             2   \n",
       "16976         1183       2016-09-21       1          3             1   \n",
       "16978         5647       2017-02-20       1          2             1   \n",
       "16979        10723       2013-09-22       1          4             1   \n",
       "16981         1640       2016-06-29       2          4             3   \n",
       "16983        16960       2015-04-04       1          4             2   \n",
       "16984         3763       2016-07-16       1          4             2   \n",
       "16985        10140       2018-06-19       2          2             1   \n",
       "16986        12780       2017-07-31       1          3             2   \n",
       "16987         7882       2016-07-09       2          3             1   \n",
       "16988        16357       2018-01-04       2          4             1   \n",
       "16990         8087       2016-03-10       1          4             1   \n",
       "16992         4269       2017-09-17       1          1             2   \n",
       "16993        13524       2018-05-05       2          3             3   \n",
       "16995         7265       2018-06-04       1          3             1   \n",
       "16996         2888       2018-07-13       2          4             2   \n",
       "16997          106       2017-01-26       2          3             2   \n",
       "16998        10568       2016-03-07       1          4             1   \n",
       "16999        15178       2017-07-22       1          4             2   \n",
       "\n",
       "       membership_year  membership_days  \n",
       "1                 2017             1042  \n",
       "3                 2017             1109  \n",
       "5                 2018              757  \n",
       "8                 2018              833  \n",
       "12                2017              923  \n",
       "13                2017              984  \n",
       "14                2014             2290  \n",
       "15                2016             1562  \n",
       "16                2014             2017  \n",
       "18                2017              873  \n",
       "19                2018              752  \n",
       "20                2016             1479  \n",
       "21                2017              986  \n",
       "22                2017             1071  \n",
       "24                2013             2476  \n",
       "25                2018              755  \n",
       "27                2017              969  \n",
       "28                2017              984  \n",
       "29                2015             1948  \n",
       "30                2014             2099  \n",
       "31                2017              979  \n",
       "32                2017              970  \n",
       "33                2017              917  \n",
       "34                2016             1413  \n",
       "35                2016             1377  \n",
       "37                2014             2203  \n",
       "38                2017             1014  \n",
       "40                2015             1653  \n",
       "41                2016             1411  \n",
       "42                2018              685  \n",
       "...                ...              ...  \n",
       "16963             2017             1226  \n",
       "16964             2018              775  \n",
       "16965             2018              774  \n",
       "16966             2015             1943  \n",
       "16967             2015             1792  \n",
       "16968             2017             1032  \n",
       "16970             2015             1658  \n",
       "16971             2018              731  \n",
       "16972             2017             1219  \n",
       "16973             2015             1684  \n",
       "16974             2016             1591  \n",
       "16975             2017              873  \n",
       "16976             2016             1339  \n",
       "16978             2017             1187  \n",
       "16979             2013             2434  \n",
       "16981             2016             1423  \n",
       "16983             2015             1875  \n",
       "16984             2016             1406  \n",
       "16985             2018              703  \n",
       "16986             2017             1026  \n",
       "16987             2016             1413  \n",
       "16988             2018              869  \n",
       "16990             2016             1534  \n",
       "16992             2017              978  \n",
       "16993             2018              748  \n",
       "16995             2018              718  \n",
       "16996             2018              679  \n",
       "16997             2017             1212  \n",
       "16998             2016             1537  \n",
       "16999             2017             1035  \n",
       "\n",
       "[14825 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    14825.000000\n",
       "mean      1188.478988\n",
       "std        419.205158\n",
       "min        666.000000\n",
       "25%        874.000000\n",
       "50%       1024.000000\n",
       "75%       1463.000000\n",
       "max       2489.000000\n",
       "Name: membership_days, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile['membership_days'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile['member_type']=pd.cut(profile['membership_days'],bins=[665,1199,1999,2488],labels=['new','regular','loyal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=profile['member_type'].astype('category').cat.categories.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "y={'member_type':{k:v for k,v in zip(x,list(range(1,len(x)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.replace(y,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to int64\n",
    "profile['member_type'] = profile['member_type'].values.astype(np.int64)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14825 entries, 1 to 16999\n",
      "Data columns (total 8 columns):\n",
      "customer_id         14825 non-null int64\n",
      "became_member_on    14825 non-null datetime64[ns]\n",
      "gender              14825 non-null int64\n",
      "age_group           14825 non-null int64\n",
      "income_range        14825 non-null int64\n",
      "membership_year     14825 non-null int64\n",
      "membership_days     14825 non-null int64\n",
      "member_type         14825 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(7)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile.drop(columns=['membership_days','became_member_on'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 14825 entries, 1 to 16999\n",
      "Data columns (total 6 columns):\n",
      "customer_id        14825 non-null int64\n",
      "gender             14825 non-null int64\n",
      "age_group          14825 non-null int64\n",
      "income_range       14825 non-null int64\n",
      "membership_year    14825 non-null int64\n",
      "member_type        14825 non-null int64\n",
      "dtypes: int64(6)\n",
      "memory usage: 810.7 KB\n"
     ]
    }
   ],
   "source": [
    "profile.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data processing on transcript**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>event</th>\n",
       "      <th>person</th>\n",
       "      <th>time</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>offer received</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>offer received</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>offer received</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>offer received</td>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>offer received</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            event                            person  time  \\\n",
       "0  offer received  78afa995795e4d85b5d9ceeca43f5fef     0   \n",
       "1  offer received  a03223e636434f42ac4c3df47e8bac43     0   \n",
       "2  offer received  e2127556f4f64592b11af22de27a7932     0   \n",
       "3  offer received  8ec6ce2a7e7949b1bf142def7d0e0586     0   \n",
       "4  offer received  68617ca6246f4fbc85e91a2a49552598     0   \n",
       "\n",
       "                                              value  \n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    306534.000000\n",
       "mean        366.382940\n",
       "std         200.326314\n",
       "min           0.000000\n",
       "25%         186.000000\n",
       "50%         408.000000\n",
       "75%         528.000000\n",
       "max         714.000000\n",
       "Name: time, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['time'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename time to time_h\n",
    "transcript.rename(columns={'time':'time_h'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rename person to customer_id\n",
    "transcript.rename(columns={'person':'customer_id'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all customers to 1 2 3 4...\n",
    "x=transcript['customer_id'].astype('category').cat.categories.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y={'customer_id':{k:v for k,v in zip(x,list(range(1,len(x)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript.replace(y,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>event</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>time_h</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>offer received</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>offer received</td>\n",
       "      <td>10736</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>offer received</td>\n",
       "      <td>15044</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>offer received</td>\n",
       "      <td>9525</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>offer received</td>\n",
       "      <td>6940</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>offer received</td>\n",
       "      <td>3729</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>offer received</td>\n",
       "      <td>13109</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>offer received</td>\n",
       "      <td>3060</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '3f207df678b143eea3cee63160fa8bed'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>offer received</td>\n",
       "      <td>11411</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>offer received</td>\n",
       "      <td>3275</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>offer received</td>\n",
       "      <td>7719</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>offer received</td>\n",
       "      <td>4062</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>offer received</td>\n",
       "      <td>4997</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>offer received</td>\n",
       "      <td>12985</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '3f207df678b143eea3cee63160fa8bed'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>offer received</td>\n",
       "      <td>14193</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>offer received</td>\n",
       "      <td>16459</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>offer received</td>\n",
       "      <td>13881</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>offer received</td>\n",
       "      <td>6733</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>offer received</td>\n",
       "      <td>15695</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>offer received</td>\n",
       "      <td>1962</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>offer received</td>\n",
       "      <td>8941</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>offer received</td>\n",
       "      <td>15981</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>offer received</td>\n",
       "      <td>1052</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>offer received</td>\n",
       "      <td>5051</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>offer received</td>\n",
       "      <td>10669</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>offer received</td>\n",
       "      <td>2432</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>offer received</td>\n",
       "      <td>7308</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>offer received</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>offer received</td>\n",
       "      <td>12891</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>offer received</td>\n",
       "      <td>9316</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306504</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8852</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.89}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306505</th>\n",
       "      <td>transaction</td>\n",
       "      <td>12352</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.48}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306506</th>\n",
       "      <td>offer completed</td>\n",
       "      <td>12352</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306507</th>\n",
       "      <td>offer viewed</td>\n",
       "      <td>9472</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306508</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8783</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.19}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306509</th>\n",
       "      <td>offer completed</td>\n",
       "      <td>8783</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306510</th>\n",
       "      <td>transaction</td>\n",
       "      <td>12489</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 14.31}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306511</th>\n",
       "      <td>transaction</td>\n",
       "      <td>10843</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.37}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306512</th>\n",
       "      <td>transaction</td>\n",
       "      <td>9447</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 6.92}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306513</th>\n",
       "      <td>transaction</td>\n",
       "      <td>12704</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 12.73}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306514</th>\n",
       "      <td>transaction</td>\n",
       "      <td>16084</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 8.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306515</th>\n",
       "      <td>transaction</td>\n",
       "      <td>745</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306516</th>\n",
       "      <td>transaction</td>\n",
       "      <td>8972</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 9.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306517</th>\n",
       "      <td>transaction</td>\n",
       "      <td>3421</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 11.7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306518</th>\n",
       "      <td>transaction</td>\n",
       "      <td>1410</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 40.67}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306519</th>\n",
       "      <td>transaction</td>\n",
       "      <td>16070</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 31.13}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306520</th>\n",
       "      <td>transaction</td>\n",
       "      <td>2896</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.6400000000000001}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306521</th>\n",
       "      <td>transaction</td>\n",
       "      <td>2999</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 17.35}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306522</th>\n",
       "      <td>transaction</td>\n",
       "      <td>13984</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.42}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306523</th>\n",
       "      <td>transaction</td>\n",
       "      <td>12201</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 18.35}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306524</th>\n",
       "      <td>transaction</td>\n",
       "      <td>14255</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 25.14}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306525</th>\n",
       "      <td>transaction</td>\n",
       "      <td>15868</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 43.58}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306526</th>\n",
       "      <td>transaction</td>\n",
       "      <td>2362</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 22.64}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306527</th>\n",
       "      <td>offer completed</td>\n",
       "      <td>2362</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306528</th>\n",
       "      <td>transaction</td>\n",
       "      <td>6148</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>transaction</td>\n",
       "      <td>12028</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.5899999999999999}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>transaction</td>\n",
       "      <td>6914</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 9.53}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>transaction</td>\n",
       "      <td>10723</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 3.61}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>transaction</td>\n",
       "      <td>7882</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 3.5300000000000002}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>transaction</td>\n",
       "      <td>12848</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.05}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  event  customer_id  time_h  \\\n",
       "0        offer received         7997       0   \n",
       "1        offer received        10736       0   \n",
       "2        offer received        15044       0   \n",
       "3        offer received         9525       0   \n",
       "4        offer received         6940       0   \n",
       "5        offer received         3729       0   \n",
       "6        offer received        13109       0   \n",
       "7        offer received         3060       0   \n",
       "8        offer received        11411       0   \n",
       "9        offer received         3275       0   \n",
       "10       offer received         7719       0   \n",
       "11       offer received         4062       0   \n",
       "12       offer received         4997       0   \n",
       "13       offer received        12985       0   \n",
       "14       offer received        14193       0   \n",
       "15       offer received        16459       0   \n",
       "16       offer received        13881       0   \n",
       "17       offer received         6733       0   \n",
       "18       offer received        15695       0   \n",
       "19       offer received         1962       0   \n",
       "20       offer received         8941       0   \n",
       "21       offer received        15981       0   \n",
       "22       offer received         1052       0   \n",
       "23       offer received         5051       0   \n",
       "24       offer received        10669       0   \n",
       "25       offer received         2432       0   \n",
       "26       offer received         7308       0   \n",
       "27       offer received          170       0   \n",
       "28       offer received        12891       0   \n",
       "29       offer received         9316       0   \n",
       "...                 ...          ...     ...   \n",
       "306504      transaction         8852     714   \n",
       "306505      transaction        12352     714   \n",
       "306506  offer completed        12352     714   \n",
       "306507     offer viewed         9472     714   \n",
       "306508      transaction         8783     714   \n",
       "306509  offer completed         8783     714   \n",
       "306510      transaction        12489     714   \n",
       "306511      transaction        10843     714   \n",
       "306512      transaction         9447     714   \n",
       "306513      transaction        12704     714   \n",
       "306514      transaction        16084     714   \n",
       "306515      transaction          745     714   \n",
       "306516      transaction         8972     714   \n",
       "306517      transaction         3421     714   \n",
       "306518      transaction         1410     714   \n",
       "306519      transaction        16070     714   \n",
       "306520      transaction         2896     714   \n",
       "306521      transaction         2999     714   \n",
       "306522      transaction        13984     714   \n",
       "306523      transaction        12201     714   \n",
       "306524      transaction        14255     714   \n",
       "306525      transaction        15868     714   \n",
       "306526      transaction         2362     714   \n",
       "306527  offer completed         2362     714   \n",
       "306528      transaction         6148     714   \n",
       "306529      transaction        12028     714   \n",
       "306530      transaction         6914     714   \n",
       "306531      transaction        10723     714   \n",
       "306532      transaction         7882     714   \n",
       "306533      transaction        12848     714   \n",
       "\n",
       "                                                    value  \n",
       "0        {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "1        {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "2        {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "3        {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "4        {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}  \n",
       "5        {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}  \n",
       "6        {'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}  \n",
       "7        {'offer id': '3f207df678b143eea3cee63160fa8bed'}  \n",
       "8        {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "9        {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "10       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "11       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "12       {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}  \n",
       "13       {'offer id': '3f207df678b143eea3cee63160fa8bed'}  \n",
       "14       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "15       {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "16       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "17       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "18       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "19       {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}  \n",
       "20       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "21       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "22       {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}  \n",
       "23       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "24       {'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}  \n",
       "25       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}  \n",
       "26       {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}  \n",
       "27       {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}  \n",
       "28       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}  \n",
       "29       {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}  \n",
       "...                                                   ...  \n",
       "306504                                   {'amount': 4.89}  \n",
       "306505                                   {'amount': 4.48}  \n",
       "306506  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...  \n",
       "306507   {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}  \n",
       "306508                                   {'amount': 1.19}  \n",
       "306509  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...  \n",
       "306510                                  {'amount': 14.31}  \n",
       "306511                                   {'amount': 2.37}  \n",
       "306512                                   {'amount': 6.92}  \n",
       "306513                                  {'amount': 12.73}  \n",
       "306514                                    {'amount': 8.2}  \n",
       "306515                                    {'amount': 2.6}  \n",
       "306516                                    {'amount': 9.2}  \n",
       "306517                                   {'amount': 11.7}  \n",
       "306518                                  {'amount': 40.67}  \n",
       "306519                                  {'amount': 31.13}  \n",
       "306520                     {'amount': 1.6400000000000001}  \n",
       "306521                                  {'amount': 17.35}  \n",
       "306522                                   {'amount': 4.42}  \n",
       "306523                                  {'amount': 18.35}  \n",
       "306524                                  {'amount': 25.14}  \n",
       "306525                                  {'amount': 43.58}  \n",
       "306526                                  {'amount': 22.64}  \n",
       "306527  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...  \n",
       "306528                                    {'amount': 2.2}  \n",
       "306529                     {'amount': 1.5899999999999999}  \n",
       "306530                                   {'amount': 9.53}  \n",
       "306531                                   {'amount': 3.61}  \n",
       "306532                     {'amount': 3.5300000000000002}  \n",
       "306533                                   {'amount': 4.05}  \n",
       "\n",
       "[306534 rows x 4 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"keys=[]\\nfor idx,row in transcript.iterrows():\\n    for rows in transcript['value']:\\n        if rows in keys:\\n            continue\\n        else:\\n            keys.append(rows)\\nkeys\""
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#value column extract values like offer id, amount, reward, offer_id\n",
    "'''keys=[]\n",
    "for idx,row in transcript.iterrows():\n",
    "    for rows in transcript['value']:\n",
    "        if rows in keys:\n",
    "            continue\n",
    "        else:\n",
    "            keys.append(rows)\n",
    "keys'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript['offer_id']=''\n",
    "transcript['amount']=0\n",
    "\n",
    "transcript['reward']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#put offerid amount and reward in respective column\n",
    "for idx, row in transcript.iterrows():\n",
    "    for k in row['value']:\n",
    "        if k == 'offer_id' or k == 'offer id':\n",
    "            transcript.at[idx, 'offer_id'] = row['value'][k]\n",
    "        if k == 'amount':\n",
    "            transcript.at[idx, 'amount'] = row['value'][k]\n",
    "        if k == 'reward':\n",
    "            transcript.at[idx, 'reward'] = row['value'][k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#put nan where NA is in offerid'\n",
    "transcript['offer_id']=transcript['offer_id'].apply(lambda x: 'N/A' if x == '' else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 7 columns):\n",
      "event          306534 non-null object\n",
      "customer_id    306534 non-null int64\n",
      "time_h         306534 non-null int64\n",
      "value          306534 non-null object\n",
      "offer_id       306534 non-null object\n",
      "amount         306534 non-null int64\n",
      "reward         306534 non-null int64\n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         9b98b8c7a33c4b65b9aebfe6a799e6d9\n",
       "1         0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "2         2906b810c7d4411798c6938adc9daaa5\n",
       "3         fafdcd668e3743c1bb461111dcafc2a4\n",
       "4         4d5c57ea9a6940dd891ad53e9dbe8da0\n",
       "5         f19421c1d4aa40978ebb69ca19b0e20d\n",
       "6         2298d6c36e964ae4a3e7e9706d1fb8c2\n",
       "7         3f207df678b143eea3cee63160fa8bed\n",
       "8         0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "9         0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "10        0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "11        0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "12        ae264e3637204a6fb9bb56bc8210ddfd\n",
       "13        3f207df678b143eea3cee63160fa8bed\n",
       "14        0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "15        fafdcd668e3743c1bb461111dcafc2a4\n",
       "16        0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "17        2906b810c7d4411798c6938adc9daaa5\n",
       "18        9b98b8c7a33c4b65b9aebfe6a799e6d9\n",
       "19        ae264e3637204a6fb9bb56bc8210ddfd\n",
       "20        2906b810c7d4411798c6938adc9daaa5\n",
       "21        9b98b8c7a33c4b65b9aebfe6a799e6d9\n",
       "22        4d5c57ea9a6940dd891ad53e9dbe8da0\n",
       "23        2906b810c7d4411798c6938adc9daaa5\n",
       "24        2298d6c36e964ae4a3e7e9706d1fb8c2\n",
       "25        2906b810c7d4411798c6938adc9daaa5\n",
       "26        f19421c1d4aa40978ebb69ca19b0e20d\n",
       "27        ae264e3637204a6fb9bb56bc8210ddfd\n",
       "28        9b98b8c7a33c4b65b9aebfe6a799e6d9\n",
       "29        fafdcd668e3743c1bb461111dcafc2a4\n",
       "                        ...               \n",
       "306504                                 N/A\n",
       "306505                                 N/A\n",
       "306506    fafdcd668e3743c1bb461111dcafc2a4\n",
       "306507    0b1e1539f2cc45b7b9fa7c272da2e1d7\n",
       "306508                                 N/A\n",
       "306509    fafdcd668e3743c1bb461111dcafc2a4\n",
       "306510                                 N/A\n",
       "306511                                 N/A\n",
       "306512                                 N/A\n",
       "306513                                 N/A\n",
       "306514                                 N/A\n",
       "306515                                 N/A\n",
       "306516                                 N/A\n",
       "306517                                 N/A\n",
       "306518                                 N/A\n",
       "306519                                 N/A\n",
       "306520                                 N/A\n",
       "306521                                 N/A\n",
       "306522                                 N/A\n",
       "306523                                 N/A\n",
       "306524                                 N/A\n",
       "306525                                 N/A\n",
       "306526                                 N/A\n",
       "306527    fafdcd668e3743c1bb461111dcafc2a4\n",
       "306528                                 N/A\n",
       "306529                                 N/A\n",
       "306530                                 N/A\n",
       "306531                                 N/A\n",
       "306532                                 N/A\n",
       "306533                                 N/A\n",
       "Name: offer_id, Length: 306534, dtype: object"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['offer_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          offer received\n",
       "1          offer received\n",
       "2          offer received\n",
       "3          offer received\n",
       "4          offer received\n",
       "5          offer received\n",
       "6          offer received\n",
       "7          offer received\n",
       "8          offer received\n",
       "9          offer received\n",
       "10         offer received\n",
       "11         offer received\n",
       "12         offer received\n",
       "13         offer received\n",
       "14         offer received\n",
       "15         offer received\n",
       "16         offer received\n",
       "17         offer received\n",
       "18         offer received\n",
       "19         offer received\n",
       "20         offer received\n",
       "21         offer received\n",
       "22         offer received\n",
       "23         offer received\n",
       "24         offer received\n",
       "25         offer received\n",
       "26         offer received\n",
       "27         offer received\n",
       "28         offer received\n",
       "29         offer received\n",
       "               ...       \n",
       "306504        transaction\n",
       "306505        transaction\n",
       "306506    offer completed\n",
       "306507       offer viewed\n",
       "306508        transaction\n",
       "306509    offer completed\n",
       "306510        transaction\n",
       "306511        transaction\n",
       "306512        transaction\n",
       "306513        transaction\n",
       "306514        transaction\n",
       "306515        transaction\n",
       "306516        transaction\n",
       "306517        transaction\n",
       "306518        transaction\n",
       "306519        transaction\n",
       "306520        transaction\n",
       "306521        transaction\n",
       "306522        transaction\n",
       "306523        transaction\n",
       "306524        transaction\n",
       "306525        transaction\n",
       "306526        transaction\n",
       "306527    offer completed\n",
       "306528        transaction\n",
       "306529        transaction\n",
       "306530        transaction\n",
       "306531        transaction\n",
       "306532        transaction\n",
       "306533        transaction\n",
       "Name: event, Length: 306534, dtype: object"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['event']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we dont want values with transaction and offer recieved\n",
    "transcript['event']=transcript[transcript['event'] != 'transaction']\n",
    "transcript['event']=transcript[transcript['event'] != 'offer received']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                     NaN\n",
       "1                     NaN\n",
       "2                     NaN\n",
       "3                     NaN\n",
       "4                     NaN\n",
       "5                     NaN\n",
       "6                     NaN\n",
       "7                     NaN\n",
       "8                     NaN\n",
       "9                     NaN\n",
       "10                    NaN\n",
       "11                    NaN\n",
       "12                    NaN\n",
       "13                    NaN\n",
       "14                    NaN\n",
       "15                    NaN\n",
       "16                    NaN\n",
       "17                    NaN\n",
       "18                    NaN\n",
       "19                    NaN\n",
       "20                    NaN\n",
       "21                    NaN\n",
       "22                    NaN\n",
       "23                    NaN\n",
       "24                    NaN\n",
       "25                    NaN\n",
       "26                    NaN\n",
       "27                    NaN\n",
       "28                    NaN\n",
       "29                    NaN\n",
       "               ...       \n",
       "306504                NaN\n",
       "306505                NaN\n",
       "306506    offer completed\n",
       "306507       offer viewed\n",
       "306508                NaN\n",
       "306509    offer completed\n",
       "306510                NaN\n",
       "306511                NaN\n",
       "306512                NaN\n",
       "306513                NaN\n",
       "306514                NaN\n",
       "306515                NaN\n",
       "306516                NaN\n",
       "306517                NaN\n",
       "306518                NaN\n",
       "306519                NaN\n",
       "306520                NaN\n",
       "306521                NaN\n",
       "306522                NaN\n",
       "306523                NaN\n",
       "306524                NaN\n",
       "306525                NaN\n",
       "306526                NaN\n",
       "306527    offer completed\n",
       "306528                NaN\n",
       "306529                NaN\n",
       "306530                NaN\n",
       "306531                NaN\n",
       "306532                NaN\n",
       "306533                NaN\n",
       "Name: event, Length: 306534, dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['event']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=transcript['event'].astype('category').cat.categories.tolist()\n",
    "y={'event':{k:v  for k,v in zip(x,list(range(1,len(x)+1)))}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace offer completed and viewed with 1 2\n",
    "transcript.replace(y,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>event</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>time_h</th>\n",
       "      <th>value</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10736</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15044</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9525</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6940</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3729</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13109</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3060</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '3f207df678b143eea3cee63160fa8bed'}</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11411</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3275</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7719</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4062</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4997</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12985</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '3f207df678b143eea3cee63160fa8bed'}</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14193</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16459</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13881</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6733</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15695</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1962</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8941</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15981</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1052</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5051</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10669</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2432</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7308</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12891</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9316</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>306504</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8852</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.89}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306505</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12352</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.48}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306506</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12352</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306507</th>\n",
       "      <td>2.0</td>\n",
       "      <td>9472</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306508</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8783</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.19}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306509</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8783</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306510</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12489</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 14.31}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306511</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10843</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.37}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306512</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9447</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 6.92}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306513</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12704</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 12.73}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306514</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16084</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 8.2}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306515</th>\n",
       "      <td>NaN</td>\n",
       "      <td>745</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.6}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306516</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8972</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 9.2}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306517</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3421</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 11.7}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306518</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1410</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 40.67}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306519</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16070</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 31.13}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306520</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2896</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.6400000000000001}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306521</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2999</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 17.35}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306522</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13984</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.42}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306523</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12201</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 18.35}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306524</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14255</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 25.14}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306525</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15868</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 43.58}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306526</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2362</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 22.64}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306527</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2362</td>\n",
       "      <td>714</td>\n",
       "      <td>{'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306528</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6148</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 2.2}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306529</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12028</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 1.5899999999999999}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306530</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6914</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 9.53}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306531</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10723</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 3.61}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306532</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7882</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 3.5300000000000002}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306533</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12848</td>\n",
       "      <td>714</td>\n",
       "      <td>{'amount': 4.05}</td>\n",
       "      <td>N/A</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>306534 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        event  customer_id  time_h  \\\n",
       "0         NaN         7997       0   \n",
       "1         NaN        10736       0   \n",
       "2         NaN        15044       0   \n",
       "3         NaN         9525       0   \n",
       "4         NaN         6940       0   \n",
       "5         NaN         3729       0   \n",
       "6         NaN        13109       0   \n",
       "7         NaN         3060       0   \n",
       "8         NaN        11411       0   \n",
       "9         NaN         3275       0   \n",
       "10        NaN         7719       0   \n",
       "11        NaN         4062       0   \n",
       "12        NaN         4997       0   \n",
       "13        NaN        12985       0   \n",
       "14        NaN        14193       0   \n",
       "15        NaN        16459       0   \n",
       "16        NaN        13881       0   \n",
       "17        NaN         6733       0   \n",
       "18        NaN        15695       0   \n",
       "19        NaN         1962       0   \n",
       "20        NaN         8941       0   \n",
       "21        NaN        15981       0   \n",
       "22        NaN         1052       0   \n",
       "23        NaN         5051       0   \n",
       "24        NaN        10669       0   \n",
       "25        NaN         2432       0   \n",
       "26        NaN         7308       0   \n",
       "27        NaN          170       0   \n",
       "28        NaN        12891       0   \n",
       "29        NaN         9316       0   \n",
       "...       ...          ...     ...   \n",
       "306504    NaN         8852     714   \n",
       "306505    NaN        12352     714   \n",
       "306506    1.0        12352     714   \n",
       "306507    2.0         9472     714   \n",
       "306508    NaN         8783     714   \n",
       "306509    1.0         8783     714   \n",
       "306510    NaN        12489     714   \n",
       "306511    NaN        10843     714   \n",
       "306512    NaN         9447     714   \n",
       "306513    NaN        12704     714   \n",
       "306514    NaN        16084     714   \n",
       "306515    NaN          745     714   \n",
       "306516    NaN         8972     714   \n",
       "306517    NaN         3421     714   \n",
       "306518    NaN         1410     714   \n",
       "306519    NaN        16070     714   \n",
       "306520    NaN         2896     714   \n",
       "306521    NaN         2999     714   \n",
       "306522    NaN        13984     714   \n",
       "306523    NaN        12201     714   \n",
       "306524    NaN        14255     714   \n",
       "306525    NaN        15868     714   \n",
       "306526    NaN         2362     714   \n",
       "306527    1.0         2362     714   \n",
       "306528    NaN         6148     714   \n",
       "306529    NaN        12028     714   \n",
       "306530    NaN         6914     714   \n",
       "306531    NaN        10723     714   \n",
       "306532    NaN         7882     714   \n",
       "306533    NaN        12848     714   \n",
       "\n",
       "                                                    value  \\\n",
       "0        {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}   \n",
       "1        {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "2        {'offer id': '2906b810c7d4411798c6938adc9daaa5'}   \n",
       "3        {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}   \n",
       "4        {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}   \n",
       "5        {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}   \n",
       "6        {'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}   \n",
       "7        {'offer id': '3f207df678b143eea3cee63160fa8bed'}   \n",
       "8        {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "9        {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "10       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "11       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "12       {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}   \n",
       "13       {'offer id': '3f207df678b143eea3cee63160fa8bed'}   \n",
       "14       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "15       {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}   \n",
       "16       {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "17       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}   \n",
       "18       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}   \n",
       "19       {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}   \n",
       "20       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}   \n",
       "21       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}   \n",
       "22       {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}   \n",
       "23       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}   \n",
       "24       {'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}   \n",
       "25       {'offer id': '2906b810c7d4411798c6938adc9daaa5'}   \n",
       "26       {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}   \n",
       "27       {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}   \n",
       "28       {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}   \n",
       "29       {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}   \n",
       "...                                                   ...   \n",
       "306504                                   {'amount': 4.89}   \n",
       "306505                                   {'amount': 4.48}   \n",
       "306506  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...   \n",
       "306507   {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "306508                                   {'amount': 1.19}   \n",
       "306509  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...   \n",
       "306510                                  {'amount': 14.31}   \n",
       "306511                                   {'amount': 2.37}   \n",
       "306512                                   {'amount': 6.92}   \n",
       "306513                                  {'amount': 12.73}   \n",
       "306514                                    {'amount': 8.2}   \n",
       "306515                                    {'amount': 2.6}   \n",
       "306516                                    {'amount': 9.2}   \n",
       "306517                                   {'amount': 11.7}   \n",
       "306518                                  {'amount': 40.67}   \n",
       "306519                                  {'amount': 31.13}   \n",
       "306520                     {'amount': 1.6400000000000001}   \n",
       "306521                                  {'amount': 17.35}   \n",
       "306522                                   {'amount': 4.42}   \n",
       "306523                                  {'amount': 18.35}   \n",
       "306524                                  {'amount': 25.14}   \n",
       "306525                                  {'amount': 43.58}   \n",
       "306526                                  {'amount': 22.64}   \n",
       "306527  {'offer_id': 'fafdcd668e3743c1bb461111dcafc2a4...   \n",
       "306528                                    {'amount': 2.2}   \n",
       "306529                     {'amount': 1.5899999999999999}   \n",
       "306530                                   {'amount': 9.53}   \n",
       "306531                                   {'amount': 3.61}   \n",
       "306532                     {'amount': 3.5300000000000002}   \n",
       "306533                                   {'amount': 4.05}   \n",
       "\n",
       "                                offer_id  amount  reward  \n",
       "0       9b98b8c7a33c4b65b9aebfe6a799e6d9       0       0  \n",
       "1       0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "2       2906b810c7d4411798c6938adc9daaa5       0       0  \n",
       "3       fafdcd668e3743c1bb461111dcafc2a4       0       0  \n",
       "4       4d5c57ea9a6940dd891ad53e9dbe8da0       0       0  \n",
       "5       f19421c1d4aa40978ebb69ca19b0e20d       0       0  \n",
       "6       2298d6c36e964ae4a3e7e9706d1fb8c2       0       0  \n",
       "7       3f207df678b143eea3cee63160fa8bed       0       0  \n",
       "8       0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "9       0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "10      0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "11      0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "12      ae264e3637204a6fb9bb56bc8210ddfd       0       0  \n",
       "13      3f207df678b143eea3cee63160fa8bed       0       0  \n",
       "14      0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "15      fafdcd668e3743c1bb461111dcafc2a4       0       0  \n",
       "16      0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "17      2906b810c7d4411798c6938adc9daaa5       0       0  \n",
       "18      9b98b8c7a33c4b65b9aebfe6a799e6d9       0       0  \n",
       "19      ae264e3637204a6fb9bb56bc8210ddfd       0       0  \n",
       "20      2906b810c7d4411798c6938adc9daaa5       0       0  \n",
       "21      9b98b8c7a33c4b65b9aebfe6a799e6d9       0       0  \n",
       "22      4d5c57ea9a6940dd891ad53e9dbe8da0       0       0  \n",
       "23      2906b810c7d4411798c6938adc9daaa5       0       0  \n",
       "24      2298d6c36e964ae4a3e7e9706d1fb8c2       0       0  \n",
       "25      2906b810c7d4411798c6938adc9daaa5       0       0  \n",
       "26      f19421c1d4aa40978ebb69ca19b0e20d       0       0  \n",
       "27      ae264e3637204a6fb9bb56bc8210ddfd       0       0  \n",
       "28      9b98b8c7a33c4b65b9aebfe6a799e6d9       0       0  \n",
       "29      fafdcd668e3743c1bb461111dcafc2a4       0       0  \n",
       "...                                  ...     ...     ...  \n",
       "306504                               N/A       4       0  \n",
       "306505                               N/A       4       0  \n",
       "306506  fafdcd668e3743c1bb461111dcafc2a4       0       2  \n",
       "306507  0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "306508                               N/A       1       0  \n",
       "306509  fafdcd668e3743c1bb461111dcafc2a4       0       2  \n",
       "306510                               N/A      14       0  \n",
       "306511                               N/A       2       0  \n",
       "306512                               N/A       6       0  \n",
       "306513                               N/A      12       0  \n",
       "306514                               N/A       8       0  \n",
       "306515                               N/A       2       0  \n",
       "306516                               N/A       9       0  \n",
       "306517                               N/A      11       0  \n",
       "306518                               N/A      40       0  \n",
       "306519                               N/A      31       0  \n",
       "306520                               N/A       1       0  \n",
       "306521                               N/A      17       0  \n",
       "306522                               N/A       4       0  \n",
       "306523                               N/A      18       0  \n",
       "306524                               N/A      25       0  \n",
       "306525                               N/A      43       0  \n",
       "306526                               N/A      22       0  \n",
       "306527  fafdcd668e3743c1bb461111dcafc2a4       0       2  \n",
       "306528                               N/A       2       0  \n",
       "306529                               N/A       1       0  \n",
       "306530                               N/A       9       0  \n",
       "306531                               N/A       3       0  \n",
       "306532                               N/A       3       0  \n",
       "306533                               N/A       4       0  \n",
       "\n",
       "[306534 rows x 7 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 7 columns):\n",
      "event          91304 non-null float64\n",
      "customer_id    306534 non-null int64\n",
      "time_h         306534 non-null int64\n",
      "value          306534 non-null object\n",
      "offer_id       306534 non-null object\n",
      "amount         306534 non-null int64\n",
      "reward         306534 non-null int64\n",
      "dtypes: float64(1), int64(4), object(2)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript['event']=transcript['event'].fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>event</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>time_h</th>\n",
       "      <th>value</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7997</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>10736</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>15044</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '2906b810c7d4411798c6938adc9daaa5'}</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9525</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6940</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   event  customer_id  time_h  \\\n",
       "0      0         7997       0   \n",
       "1      0        10736       0   \n",
       "2      0        15044       0   \n",
       "3      0         9525       0   \n",
       "4      0         6940       0   \n",
       "\n",
       "                                              value  \\\n",
       "0  {'offer id': '9b98b8c7a33c4b65b9aebfe6a799e6d9'}   \n",
       "1  {'offer id': '0b1e1539f2cc45b7b9fa7c272da2e1d7'}   \n",
       "2  {'offer id': '2906b810c7d4411798c6938adc9daaa5'}   \n",
       "3  {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}   \n",
       "4  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}   \n",
       "\n",
       "                           offer_id  amount  reward  \n",
       "0  9b98b8c7a33c4b65b9aebfe6a799e6d9       0       0  \n",
       "1  0b1e1539f2cc45b7b9fa7c272da2e1d7       0       0  \n",
       "2  2906b810c7d4411798c6938adc9daaa5       0       0  \n",
       "3  fafdcd668e3743c1bb461111dcafc2a4       0       0  \n",
       "4  4d5c57ea9a6940dd891ad53e9dbe8da0       0       0  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript.replace(replace_offer_id,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 306534 entries, 0 to 306533\n",
      "Data columns (total 7 columns):\n",
      "event          306534 non-null int64\n",
      "customer_id    306534 non-null int64\n",
      "time_h         306534 non-null int64\n",
      "value          306534 non-null object\n",
      "offer_id       306534 non-null object\n",
      "amount         306534 non-null int64\n",
      "reward         306534 non-null int64\n",
      "dtypes: int64(5), object(2)\n",
      "memory usage: 16.4+ MB\n"
     ]
    }
   ],
   "source": [
    "transcript.info()"
   ]
  },
  {
   "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>difficulty</th>\n",
       "      <th>duration_h</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward</th>\n",
       "      <th>channel_email</th>\n",
       "      <th>channel_mobile</th>\n",
       "      <th>channel_social</th>\n",
       "      <th>channel_web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.50</td>\n",
       "      <td>168</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>120</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.25</td>\n",
       "      <td>168</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.00</td>\n",
       "      <td>240</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   difficulty  duration_h  offer_id  offer_type  reward  channel_email  \\\n",
       "0        0.50         168         8           1     1.0              1   \n",
       "1        0.50         120         5           1     1.0              1   \n",
       "2        0.00          96         4           3     0.0              1   \n",
       "3        0.25         168         7           1     0.5              1   \n",
       "4        1.00         240         1           2     0.5              1   \n",
       "\n",
       "   channel_mobile  channel_social  channel_web  \n",
       "0               1               1            0  \n",
       "1               1               1            1  \n",
       "2               1               0            1  \n",
       "3               1               0            1  \n",
       "4               0               0            1  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio.rename(columns={'id':'offer_id'},inplace=True)\n",
    "portfolio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  1,  3, 10,  5,  9,  2,  4,  8,  6,  0])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['offer_id']=transcript['offer_id'].apply(lambda x: 0 if x =='N/A' else x)\n",
    "transcript['offer_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  1,  3, 10,  5,  9,  2,  4,  8,  6,  0])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript['offer_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "transcript.drop(transcript.index[transcript['event'] == 0],inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge datasets to master df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>event</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>time_h</th>\n",
       "      <th>value</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>reward_x</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration_h</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>reward_y</th>\n",
       "      <th>channel_email</th>\n",
       "      <th>channel_mobile</th>\n",
       "      <th>channel_social</th>\n",
       "      <th>channel_web</th>\n",
       "      <th>gender</th>\n",
       "      <th>age_group</th>\n",
       "      <th>income_range</th>\n",
       "      <th>membership_year</th>\n",
       "      <th>member_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3729</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>13995</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '5a8bc65990b245e5a138643cd4eb9837'}</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1052</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>12744</td>\n",
       "      <td>0</td>\n",
       "      <td>{'offer id': '5a8bc65990b245e5a138643cd4eb9837'}</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   event  customer_id  time_h  \\\n",
       "0      2         3729       0   \n",
       "1      2        13995       0   \n",
       "2      2         1052       0   \n",
       "3      2          170       0   \n",
       "4      2        12744       0   \n",
       "\n",
       "                                              value  offer_id  amount  \\\n",
       "0  {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}         9       0   \n",
       "1  {'offer id': '5a8bc65990b245e5a138643cd4eb9837'}         6       0   \n",
       "2  {'offer id': '4d5c57ea9a6940dd891ad53e9dbe8da0'}         5       0   \n",
       "3  {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}         8       0   \n",
       "4  {'offer id': '5a8bc65990b245e5a138643cd4eb9837'}         6       0   \n",
       "\n",
       "   reward_x  difficulty  duration_h  offer_type  reward_y  channel_email  \\\n",
       "0         0        0.25         120           1       0.5              1   \n",
       "1         0        0.00          72           3       0.0              1   \n",
       "2         0        0.50         120           1       1.0              1   \n",
       "3         0        0.50         168           1       1.0              1   \n",
       "4         0        0.00          72           3       0.0              1   \n",
       "\n",
       "   channel_mobile  channel_social  channel_web  gender  age_group  \\\n",
       "0               1               1            1     2.0        4.0   \n",
       "1               1               1            0     3.0        3.0   \n",
       "2               1               1            1     1.0        4.0   \n",
       "3               1               1            0     1.0        1.0   \n",
       "4               1               1            0     2.0        3.0   \n",
       "\n",
       "   income_range  membership_year  member_type  \n",
       "0           1.0           2018.0          1.0  \n",
       "1           1.0           2017.0          1.0  \n",
       "2           1.0           2016.0          2.0  \n",
       "3           1.0           2016.0          2.0  \n",
       "4           1.0           2014.0          3.0  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge all three df\n",
    "master_df=transcript.merge(portfolio,how=\"left\",on=\"offer_id\")\n",
    "master_df=master_df.merge(profile,how=\"left\",on=\"customer_id\")\n",
    "master_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis on master dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 91304 entries, 0 to 91303\n",
      "Data columns (total 20 columns):\n",
      "event              91304 non-null int64\n",
      "customer_id        91304 non-null int64\n",
      "time_h             91304 non-null int64\n",
      "value              91304 non-null object\n",
      "offer_id           91304 non-null int64\n",
      "amount             91304 non-null int64\n",
      "reward_x           91304 non-null int64\n",
      "difficulty         91304 non-null float64\n",
      "duration_h         91304 non-null int64\n",
      "offer_type         91304 non-null int64\n",
      "reward_y           91304 non-null float64\n",
      "channel_email      91304 non-null int64\n",
      "channel_mobile     91304 non-null int64\n",
      "channel_social     91304 non-null int64\n",
      "channel_web        91304 non-null int64\n",
      "gender             82304 non-null float64\n",
      "age_group          82304 non-null float64\n",
      "income_range       82304 non-null float64\n",
      "membership_year    82304 non-null float64\n",
      "member_type        82304 non-null float64\n",
      "dtypes: float64(7), int64(12), object(1)\n",
      "memory usage: 14.6+ MB\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import *\n",
    "import pylab as plt\n",
    "%matplotlib inline\n",
    "master_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 82304 entries, 0 to 91303\n",
      "Data columns (total 20 columns):\n",
      "event              82304 non-null int64\n",
      "customer_id        82304 non-null int64\n",
      "time_h             82304 non-null int64\n",
      "value              82304 non-null object\n",
      "offer_id           82304 non-null int64\n",
      "amount             82304 non-null int64\n",
      "reward_x           82304 non-null int64\n",
      "difficulty         82304 non-null float64\n",
      "duration_h         82304 non-null int64\n",
      "offer_type         82304 non-null int64\n",
      "reward_y           82304 non-null float64\n",
      "channel_email      82304 non-null int64\n",
      "channel_mobile     82304 non-null int64\n",
      "channel_social     82304 non-null int64\n",
      "channel_web        82304 non-null int64\n",
      "gender             82304 non-null float64\n",
      "age_group          82304 non-null float64\n",
      "income_range       82304 non-null float64\n",
      "membership_year    82304 non-null float64\n",
      "member_type        82304 non-null float64\n",
      "dtypes: float64(7), int64(12), object(1)\n",
      "memory usage: 13.2+ MB\n"
     ]
    }
   ],
   "source": [
    "master_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['event', 'customer_id', 'time_h', 'value', 'offer_id', 'amount',\n",
       "       'reward_x', 'difficulty', 'duration_h', 'offer_type', 'reward_y',\n",
       "       'channel_email', 'channel_mobile', 'channel_social', 'channel_web',\n",
       "       'gender', 'age_group', 'income_range', 'membership_year',\n",
       "       'member_type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "master_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_df.rename(columns={'reward_x':'reward'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finding correlation between labels\n",
    "corr=master_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9a5fd1fa90>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a5fe6a0b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#important features for training obtained from corr\n",
    "X = master_df[['time_h','offer_id','amount','reward','difficulty','duration_h','offer_type','gender','age_group','income_range', 'member_type']]\n",
    "Y = master_df['event']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split train test\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function for checking accuracy\n",
    "def predict_score(model):\n",
    "    pred = model.predict(X_test)\n",
    "    \n",
    "    # Calculate the absolute errors\n",
    "    errors = abs(pred - y_test)\n",
    "    \n",
    "    # Calculate mean absolute percentage error\n",
    "    mean_APE = 100 * (errors / y_test)\n",
    "    accuracy = 100 - np.mean(mean_APE)\n",
    "    \n",
    "    return round(accuracy, 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1) Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic regression classifier on training set: 60.65%.\n",
      "Prediction Accuracy: 60.3883%\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "print(f'Accuracy of Logistic regression classifier on training set: {round(logreg.score(X_train, y_train)*100,2)}%.')\n",
    "print(f'Prediction Accuracy: {predict_score(logreg)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2) Random Forest Classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 100.0%.\n",
      "Prediction Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators = 100, random_state = 42)\n",
    "\n",
    "rf.fit(X_train, y_train)\n",
    "print(f'Accuracy of SVM classifier on training set: {round(rf.score(X_train, y_train)*100,2)}%.')\n",
    "print(f'Prediction Accuracy: {predict_score(rf)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3) Support Vector Machine**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 99.99%.\n",
      "Prediction Accuracy: 99.9878%\n"
     ]
    }
   ],
   "source": [
    "svm = SVC(gamma = 'auto')\n",
    "\n",
    "svm.fit(X_train, y_train)\n",
    "print(f'Accuracy of SVM classifier on training set: {round(svm.score(X_train, y_train)*100,2)}%.')\n",
    "print(f'Prediction Accuracy: {predict_score(svm)}%')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4) Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 100.0%.\n",
      "Prediction Accuracy: 100.0%\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "\n",
    "dt.fit(X_train, y_train)\n",
    "print(f'Accuracy of Decision Tree classifier on training set: {round(dt.score(X_train, y_train)*100,2)}%.')\n",
    "print(f'Prediction Accuracy: {predict_score(dt)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5) Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM classifier on training set: 60.65%.\n",
      "Prediction Accuracy: 60.3883%\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB() \n",
    "gnb.fit(X_train, y_train) \n",
    "print(f'Accuracy of SVM classifier on training set: {round(gnb.score(X_train, y_train)*100,2)}%.')\n",
    "print(f'Prediction Accuracy: {predict_score(gnb)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6) K nearest neighbors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 99.91%.\n",
      "Prediction Accuracy: 99.8712%\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "print(f'Accuracy of K-NN classifier on training set: {round(knn.score(X_train, y_train)*100,2)}%.')\n",
    "print(f'Prediction Accuracy: {predict_score(knn)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [svm, dt, gnb, knn, rf, logreg]\n",
    "model_names = [type(n).__name__ for n in models]\n",
    "training_accuracy = [x.score(X_train, y_train)*100 for x in models]\n",
    "predection_accuracy = [predict_score(y) for y in models]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>SVC</th>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <th>GaussianNB</th>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <th>RandomForestRegressor</th>\n",
       "      <th>LogisticRegression</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Training Accuracy</th>\n",
       "      <td>99.99352</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.646708</td>\n",
       "      <td>99.914139</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.646708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicting Accuracy</th>\n",
       "      <td>99.98780</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.388300</td>\n",
       "      <td>99.871200</td>\n",
       "      <td>100.0</td>\n",
       "      <td>60.388300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          SVC  DecisionTreeClassifier  GaussianNB  \\\n",
       "Training Accuracy    99.99352                   100.0   60.646708   \n",
       "Predicting Accuracy  99.98780                   100.0   60.388300   \n",
       "\n",
       "                     KNeighborsClassifier  RandomForestRegressor  \\\n",
       "Training Accuracy               99.914139                  100.0   \n",
       "Predicting Accuracy             99.871200                  100.0   \n",
       "\n",
       "                     LogisticRegression  \n",
       "Training Accuracy             60.646708  \n",
       "Predicting Accuracy           60.388300  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = [training_accuracy, predection_accuracy]\n",
    "results_df = pd.DataFrame(results, columns = model_names, index=['Training Accuracy', 'Predicting Accuracy']) \n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Refinement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These models are giving very good accuracy, however Naive Bayes and Logistic regression is not upto the mark. \n",
    "In addition to that, I think SVC DT and RF are overfitted because it is very close to 100. That's why I chose KNN model for further refinement with GridSearchCV (sklearn pipeline)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions based on Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reconverting the values of the following features from numerical values to its original categorical values.\n",
    "master_df['event'] = master_df['event'].map({1: 'Completed', 2: 'Viewed'})\n",
    "master_df['offer_type'] = master_df['offer_type'].map({1: 'BOGO', 2: 'Discount', 3: 'Informational'})\n",
    "master_df['income_range'] = master_df['income_range'].map({1: 'Average', 2: 'Above-Average', 3:'High'})\n",
    "master_df['age_group'] = master_df['age_group'].map({1: 'teenager', 2: 'young-adult', 3:'adult', 4:'elderly'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is the common offer each age group ( teenagers, young-adults, adults and elderly)?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAGDCAYAAADOGUqaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XucVXW9//HXx0EF8q5oKRrKwQsIjjqRSXmjSK0wTVPjCHoy9RwvnUwN7Xe8lWZlNy95jhneIsVLKp1jmZhm4BV0NEhMNFSUBNFUAk3x8/tjr8ENzgzDYmY2MK/n47Efs/d3fdda37X32rDf+/td3x2ZiSRJkiRp+axR6wZIkiRJ0qrIMCVJkiRJJRimJEmSJKkEw5QkSZIklWCYkiRJkqQSDFOSJEmSVIJhSpLUJhFxT0Qc3cn77BERv46I1yLixqLs2xHxckT8rTPbsjKIiD4RkRHRrdZtkSQZpiRpuUXEzIj4Z0RsslR5Y/FBt88Kbj8j4l9aWX5kRCyKiPkR8Xqx38+uyD5rISpOjYinImJhRDwXERdExNpV1Q4GNgM2zsxDImJL4OtA/8z8YAe37+yI+MUKrH9VcZ7Mr7o91p5tXI62nF2cV4M7eD/9IuL6iJhbnJtPRcTFEdG7I/crSbVimJKkcv4KHN70ICIGAj06cf/3Z+Y6wAbAz4EbImKjTtx/m7XSi3IRcAwwElgX2A/YB7ihqs6Hgb9k5jtVj+dl5pwS7YiI6Oz/976XmetU3Xbq5P0TEQEcAbwCjOrA/fwL8CDwIrBzZq4HDAGeBj7ewjr2sElapRmmJKmca6mEgCajgGuqK0TE+hFxTfEt/bMR8f+aPsxHxL9ExB+K4WsvR8S4ovzeYvXHip6MQ1trRGa+C4yhEuS2KbbxlYiYERGvRMT4iNi8qk0ZESdFxDPFfr9f1aYlemJaG1IWEX0j4vcRMa/YztiI2KBq+cyI+EZEPA78Y+ltREQ/4D+AEZl5f2a+k5nTgC8A+0bEPhFxDnAmcGjxXBwL3AlsXjy+qtjWbhFxX0T8PSIei4i9qvZzT0ScFxGTgAXANkXP3jMR8UZE/DUiRjRzfPsCZ1Tt+7GifPPiOX2leI6/0trr05qIuDEi/lacA/dGxICqZT0i4gfFefNaREyMiOqwPqLoyXs5Ir65jF19Atgc+CpwWESsVbWfumI/LxfPxQnVr3lxDv88ImZHxAtRGWJZ18J+zgYmZebJmTkLIDPnZOaPM/P6Ynt7RcSs4tz4G3BlUd7sOdvcORhVw02L13JSVHq/XouI6RExtA1PvyS1C8OUJJXzALBeROxQfLg8FFh6SNjFwPpUQs6eVMLXUcWybwG/AzYEehd1ycw9iuU7FT0Z41prRPEh82hgPvBUROwDfAf4IvAh4Fng+qVWOxBoAHYBDgD+re2H/d6ui/1sDuwAbEnlw3S1w4HPABtU9Sw1GQrMysyHqgsz83kqz+2nMvMs4HxgXPFc/A+V3qsXi8dHRsQWwP8B3wY2Ak4Bbo6IXlWbPYJKD9i6wFwqPWL7Zea6wO5A49IHl5m/XWrfTT1K1wGziuM+GDh/BT68/wboB2wKPAKMrVp2IbBr0b6NgNOAd6uWfxzYjsrzeGZE7NDKfkYBvwaazqXqIaFfofKc1lM5Hz6/1LpXA+8A/wLsDAyjcr4155PAza20o8kHqRzTh4Fj2njOtuajwDPAJsBZwK9iJe2llbT6MUxJUnlNvVOfAqYDLzQtqApYp2fmG5k5E/gBlQ/2AG9T+TC5eWa+mZkTl3Pfu0XE34G/UQktB2bma8AIYExmPpKZbwGnAx+LJa/j+m5mvpKZzwE/pmq4Yltl5ozMvDMz38rMucAPqQTGahdl5vOZubCZTWwCzG5h87OL5W3xr8DtmXl7Zr6bmXcCk4H9q+pclZnTikD3DpVQsmNE9MjM2UWP2DJF5XqtjwPfKF6zRuAK3ntNm3NK0WPWdLu6aUFmjinOjbeoBNGdip6gNagE3K9m5guZuSgz7yvqNTknMxdm5mPAY0CzwwcjoidwCPDLzHwbuIklh/p9EfhJZs7KzFeBC6rW3YxK0PrPzPxHMbTyR8BhLRzrJlTOx6b1TyiOeX5E/Kyq3rvAWcW5s5C2nbOtmQP8ODPfLr58eJJKiJekDmeYkqTyrgW+BBzJUkP8qHywXIvKt+xNngW2KO6fRqV356GImBYRy9s79EBmbpCZm2Tmbpk5oSjfvHqfmTkfmFe1X4Dnl2rT5iyniNg0KhMNvBARr1PplVs6AD3fzKpNXqbSC9GcDxXL2+LDwCHVgYVK4Kne9uJ2ZOY/qITc44DZEfF/EbF9G/e1OfBKZr5RVVb9mjbnwuJ1arqNgsXD6y6IiKeL529mUX+T4tadyrVGLameyXABsE4L9Q6kEiBvLx6PBfar6rnbnCVfp+r7HwbWpPI8NT23/0OlJ60586h63jPzkszcgEpgX7Oq3tzMfLPqcVvO2da8kJlZ9bjUOS1JZRimJKmkzHyWykQU+wO/Wmrxy7zX+9RkK4req8z8W2Z+JTM3B44FfhqtzOC3HF6s3mdEfADYmKpeMypD8qrb9GJx/x9Az6plrc2W9x0ggUHFRAP/SiUcVsv3rfWe3wNbxlKzyxW9P7sBd7WybrXngWuXCiwfyMwLquos0Y7MvCMzP0Xlg/90oLrXpLX2vwhsFBHrVpUtfk2X05eoDLH8JJWhoH2K8qBy7rwJ9C2x3aWNohK0niuuUbqRSrBp6o2cTWWYaZPqc+N54C1gk6rndr3MHEDz7gIOakObmnteWzpn/1EUt3ZebhER1ede9TktSR3KMCVJK+bLwD5Fj8dimbmIyqx050XEuhHxYeBkiuuqIuKQeG+66FepfMBcVDx+iWIyiRJ+CRwVEfVRmWL8fODBYphhk1MjYsMiuHyV966laQT2iIitImJ9KsOtWrIuleu0/l5ct3Tq8jQyM/8C/DcwNioTSNQVEzDcDEyo6mlbll8An4uITxfb6F5MctDsVNwRsVlEDC8+sL9VHMOi5upSeR36FMPumq7nug/4TrGfQVRe/7EtrN+adYv9z6MSFM5vWlA1qcgPozLhRV1EfCyWnDJ+mYrXZSiVa6Tqi9tOwHd5b6jfDcBXI2KLqEwg8o2qdsymcl3fDyJivYhYIyoTjyw9nLPJ2cAnIuKHxb6Jys8HtHY9F7RyzhZDSF8A/rV4Hv6N94fMTYGTImLNiDik2N/tSFInMExJ0grIzKczc3ILi0+k8s36M8BEKh8axxTLPgI8GBHzgfFUro/5a7HsbODqYmjVF5ezPXcB/0UllMym8sFz6WtcbgOmUAlP/0dlanWK643GAY8Xy/+3lV2dQ2XCgteKbSzdM9cWJ1C55ugXVELNb4F7qMzo1yZFwDmAysx7c6n0ppxKy/+/rUHld6pepDJV+J5UZhVszo3F33kR8Uhx/3AqvUgvArdQufbnzlaaeFos+TtTTcMXr6EyHO0F4M9UJt2odgrwJ+Dhop3fbeWYWnIE0JiZvyt6Qv+WmX+jMgHHoIjYkUqv3O+ovOaPUgkh7/BewBxJZbjqn6mE/ptoYXhmEZB3o9LT9VhEvAFMovJc/VdLjWzDOfsVKq/pPGAAlUBb7UEqE3m8DJwHHJyZ81p9ZiSpncSSw4wlSauziEigX2bOqHVbtPKJiP2A/87MDy+z8kogIo4Ejs7MZn/HSpI6mj1TkiR1UVH5Pav9I6JbMTTvLCo9bpKkNjBMSZLUdQWVIZuvUhnm9wSVH0qWJLWBw/wkSZIkqQR7piRJkiSpBMOUJEmSJJXQrdYN6GybbLJJ9unTp9bNkCRJkrSSmjJlysuZ2WtZ9bpcmOrTpw+TJ7f0kzCSJEmSurqIeLYt9RzmJ0mSJEklGKYkSZIkqQTDlCRJkiSV0OWumZIkSZI629tvv82sWbN48803a90UVenevTu9e/dmzTXXLLW+YUqSJEnqYLNmzWLdddelT58+REStmyMgM5k3bx6zZs1i6623LrUNh/lJkiRJHezNN99k4403NkitRCKCjTfeeIV6Cw1TkiRJUicwSK18VvQ1MUxJkiRJq4lZs2ZxwAEH0K9fP/r27ctXv/pV/vnPfy5efvjhhzNo0CB+9KMfMX36dOrr69l55515+umnl3tfxx9/PPX19fTv358ePXpQX19PfX09N910U3se0krNMCVJkiStBjKTgw46iM9//vM89dRT/OUvf2H+/Pl885vfBOBvf/sb9913H48//jhf+9rXuPXWWznggAN49NFH6du3b5v2sWjRosX3L730UhobG7n99tvp27cvjY2NNDY2cvDBB3fI8a2MDFOSJEnSauD3v/893bt356ijjgKgrq6OH/3oR4wZM4YFCxYwbNgw5syZQ319Peeccw4//vGPueKKK9h7770B+MUvfsHgwYOpr6/n2GOPXRyc1llnHc4880w++tGPcv/99y+zHU8++SSDBw9e/PiJJ55Y/Lh3796MHj2awYMH89GPfpRnnnkGgJdeeomDDjqIhoYGBg8ezAMPPNCuz01HMUxJkiRJq4Fp06ax6667LlG23nrrsdVWWzFjxgzGjx+/uAfprLPO4rjjjuNrX/sad999N0888QTjxo1j0qRJNDY2UldXx9ixYwH4xz/+wY477siDDz7Ixz/+8WW2Y7vttqN79+5MnToVgCuvvHJxwAPYcMMNeeihhzj22GM5+eSTATjppJM47bTTmDx5MjfccANHH310ez0tHcqp0SVJkqTVQGY2O6FCS+XV7rrrLqZMmcJHPvIRABYuXMimm24KVHq4vvCFLyxXW7785S9z5ZVX8t3vfpcbb7yRRx99dPGyww8/HIARI0YwevRoACZMmMCTTz65uM6rr77KwoUL6dGjx3Ltt7MZpiRJkqTVwIABA7j55puXKHv99dd5/vnn6du3L3PmzGlx3cxk1KhRfOc733nfsu7du1NXV7dcbTnkkEM4//zzGTJkCB/72MfYYIMNFi9rKfA99NBDrLXWWsu1n1rrsDAVEWOAzwJzMnPHomwcsF1RZQPg75lZHxF9gCeApjj6QGYeV6yzK3AV0AO4HfhqZmZEbASMA/oAM4EvZuarHXU8klq366nX1LoJ7WrK90fWugmSJC2XoUOHMnr0aK655hpGjhzJokWL+PrXv86RRx5Jz549l7nuAQccwNe+9jU23XRTXnnlFd544w0+/OEPl2pLz5492WeffTjhhBO4+uqrl1g2btw4TjnlFK677jqGDBkCwCc/+UkuvfRSvva1rwHQ2NhIfX19qX13po68ZuoqYN/qgsw8NDPrM7MeuBn4VdXip5uWNQWpwmXAMUC/4ta0zdHAXZnZD7ireCxJkiR1SRHBLbfcwo033ki/fv3Ydttt6d69O+eff/4y1+3fvz/f/va3GTZsGIMGDeJTn/oUs2fPXqH2jBgxgjXXXJOhQ4cuUb5gwQIGDx7MZZddxg9+8AOgMjPgpEmTGDRoEP379+dnP/vZCu27s0RmdtzGKz1O/9vUM1VVHsBzwD6Z+VQr9T4E3J2Z2xePDwf2ysxjI+LJ4v7sot49mbkdy9DQ0JCTJ09e8YOTtAR7piRJatkTTzzBDjvsUOtmdKoLLriAt956i7POOmtxWe/evZk6deoSw/5qrbnXJiKmZGbDstat1TVTnwBeysynqsq2johHgdeB/5eZfwS2AGZV1ZlVlAFslpmzAYpAtWkntFuSJEnSMnzuc5/j+eef5/e//32tm9KhahWmDgeuq3o8G9gqM+cV10jdGhEDgOamHVnurrSIOIbKUEG22mqrEs2VJEmS1Fa//vWvmy2fNWtWs+Wrqk7/namI6AYcRGXyCAAy863MnFfcnwI8DWxLpSeqd9XqvYEXi/svFcP7moYDtjg9SWZenpkNmdnQq1ev9jwcSZIkSV1ULX6095PA9MxcHEsjoldE1BX3t6Ey0cQzxTC+NyJit+I6q5HAbcVq44FRxf1RVeWSJEmS1OE6LExFxHXA/cB2ETErIr5cLDqMJYf4AewBPB4RjwE3Acdl5ivFsn8HrgBmUOmx+k1RfgHwqYh4CvhU8ViSJEmSOkWHXTOVmYe3UH5kM2U3U5kqvbn6k4EdmymfBwx9/xqSJEmS1PFqMcxPkiRJUierq6ujvr6enXbaiV122YX77rtv8bJp06axzz77sO2229KvXz++9a1vUf0TSr/97W8ZPHgw22+/PfX19Rx66KE899xzAGQm3/72txf/ttXee+/NtGnTOv34aqFWs/lJkiRJXVZ7/z5jW34fsUePHjQ2NgJwxx13cPrpp/OHP/yBhQsXMnz4cC677DKGDRvGggUL+MIXvsBPf/pTjj/+eKZOncqJJ57I+PHjF/8e0/jx45k5cyZbbbUVl156Kffddx+PPfYYPXv25He/+x3Dhw9n2rRpdO/evV2Pc2Vjz5QkSZLUxbz++utsuOGGAPzyl79kyJAhDBs2DICePXtyySWXcMEFlSkJvvvd73LGGWcs8cO2w4cPZ4899li8/OKLL6Znz54ADBs2jN13352xY8d25iHVhD1TkiRJUhewcOFC6uvrefPNN5k9e/biH9SdNm0au+666xJ1+/bty/z583n99deZNm0ap5xySrPbfP311/nHP/5B3759lyhvaGjoEkP97JmSJEmSuoCmYX7Tp0/nt7/9LSNHjiQzyUwqv0L0fkuXz5s3j/r6erbddlsuvPDCFvfV2jZXJ/ZMSVIXMOTiIbVuQruadOKkWjdBklZpH/vYx3j55ZeZO3cuAwYM4N57711i+TPPPMM666zDuuuuy4ABA3jkkUfYaaed2HjjjWlsbOTCCy9k/vz5rLfeenzgAx/gmWeeYZtttlm8/iOPPMKee+7Z2YfV6eyZkiRJkrqY6dOns2jRIjbeeGNGjBjBxIkTmTBhAlAZDnjSSSdx2mmnAXDaaadx3nnn8cQTTyxef8GCBYvvn3rqqZx00kksXLgQgAkTJjBx4kS+9KUvdeIR1YY9U5IkSVIX0HTNFFSG4V199dXU1dXRo0cPbrvtNk488USOP/54Fi1axBFHHMEJJ5wAwMCBA/nJT37CyJEjeeONN9h4443ZaqutOOeccwA48cQTefXVVxk4cCB1dXV88IMf5LbbbqNHjx41O9bOYpiSJEmSOllbpjJvb4sWLWpx2cCBA7nnnntaXP6Zz3yGz3zmM80uiwjOOusszjrrrBVt4irHYX6SJEmSVIJhSpIkSZJKMExJkiRJUgmGKUmSJEkqwTAlSZIkSSUYpiRJkiSpBMOUJEmS1AWss846y6zzxz/+kQEDBlBfX7/4R3g7WmNjI7fffvvix+PHj+eCCy5o9/205fiXl78zJUmSJHWy584d2K7b2+rMP7XLdsaOHcspp5zCUUcd1ab6ixYtoq6uboX22djYyOTJk9l///0BGD58OMOHD1+hbXYWe6YkSZKkLuSee+5hr7324uCDD2b77bdnxIgRZCZXXHEFN9xwA+eee+7islNPPZUdd9yRgQMHMm7cuMXr77333nzpS19i4MCBzJw5k+23356jjz6aHXfckREjRjBhwgSGDBlCv379eOihhwB46KGH2H333dl5553ZfffdefLJJ/nnP//JmWeeybhx46ivr2fcuHFcddVVnHDCCQA8++yzDB06lEGDBjF06FCee+45AI488khOOukkdt99d7bZZhtuuukmAObPn8/QoUPZZZddGDhwILfddluHPpf2TEmSJEldzKOPPsq0adPYfPPNGTJkCJMmTeLoo49m4sSJfPazn+Xggw/m5ptvprGxkccee4yXX36Zj3zkI+yxxx5AJRhNnTqVrbfempkzZzJjxgxuvPFGLr/8cj7ykY/wy1/+kokTJzJ+/HjOP/98br31VrbffnvuvfdeunXrxoQJEzjjjDO4+eabOffcc5k8eTKXXHIJAFddddXidp5wwgmMHDmSUaNGMWbMGE466SRuvfVWAGbPns3EiROZPn06w4cP5+CDD6Z79+7ccsstrLfeerz88svstttuDB8+nIjokOfRMCVJkiR1MYMHD6Z3794A1NfXM3PmTD7+8Y8vUWfixIkcfvjh1NXVsdlmm7Hnnnvy8MMPs9566zF48GC23nrrxXW33nprBg6sDF0cMGAAQ4cOJSIW91wBvPbaa4waNYqnnnqKiODtt99eZjvvv/9+fvWrXwFwxBFHcNpppy1e9vnPf5411liD/v3789JLLwGQmZxxxhnce++9rLHGGrzwwgu89NJLfPCDHyz/ZLXCYX6SJElSF7P22msvvl9XV8c777zzvjqZ2eL6H/jAB1rc3hprrLH48RprrLF42//1X//F3nvvzdSpU/n1r3/Nm2++udztru5hqt5nU1vHjh3L3LlzmTJlCo2NjWy22Wal9tNWhilJkiRJ77PHHnswbtw4Fi1axNy5c7n33nsZPHhw6e299tprbLHFFsCSQ/nWXXdd3njjjWbX2X333bn++uuBSlBauvesuX1suummrLnmmtx99908++yzpdvbFoYpSZIkSe9z4IEHMmjQIHbaaSf22Wcfvve9763QcLnTTjuN008/nSFDhrBo0aLF5XvvvTd//vOfF09AUe2iiy7iyiuvZNCgQVx77bX85Cc/aXUfI0aMYPLkyTQ0NDB27Fi233770u1ti2it+2511NDQkJMnT651M6TVzq6nXlPrJrSrKd8fWesmtKshFw+pdRPa1aQTJ9W6CZK0XJ544gl22GGHWjdDzWjutYmIKZnZsKx17ZmSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCYYpSZIkSSrBMCVJkiRJJRimJEmSpC6grq6O+vp6BgwYwE477cQPf/hD3n33XQAmT57MSSedVNP2nX/++TXdfxndat0ASZIkqatp79//a8vv7/Xo0YPGxkYA5syZw5e+9CVee+01zjnnHBoaGmhoWObPKnWo888/nzPOOKOmbVhe9kxJkiRJXcymm27K5ZdfziWXXEJmcs899/DZz34WgD/84Q/U19dTX1/PzjvvzBtvvAHA9773PQYOHMhOO+3E6NGjAWhsbGS33XZj0KBBHHjggbz66qsA7LXXXkyePBmAl19+mT59+gBw1VVXcdBBB7HvvvvSr18/TjvtNABGjx7NwoULqa+vZ8SIEZ35VKwQe6YkSZKkLmibbbbh3XffZc6cOUuUX3jhhVx66aUMGTKE+fPn0717d37zm99w66238uCDD9KzZ09eeeUVAEaOHMnFF1/MnnvuyZlnnsk555zDj3/841b329jYyKOPPsraa6/Ndtttx4knnsgFF1zAJZdcsrjnbFVhz5QkSZLURWXm+8qGDBnCySefzEUXXcTf//53unXrxoQJEzjqqKPo2bMnABtttBGvvfYaf//739lzzz0BGDVqFPfee+8y9zl06FDWX399unfvTv/+/Xn22Wfb96A6UYeFqYgYExFzImJqVdnZEfFCRDQWt/2rlp0eETMi4smI+HRV+b5F2YyIGF1VvnVEPBgRT0XEuIhYq6OORZIkSVrdPPPMM9TV1bHpppsuUT569GiuuOIKFi5cyG677cb06dPJTCKizdvu1q3b4skt3nzzzSWWrb322ovv19XV8c4776zAUdRWR/ZMXQXs20z5jzKzvrjdDhAR/YHDgAHFOj+NiLqIqAMuBfYD+gOHF3UBvltsqx/wKvDlDjwWSZIkabUxd+5cjjvuOE444YT3haSnn36agQMH8o1vfIOGhgamT5/OsGHDGDNmDAsWLADglVdeYf3112fDDTfkj3/8IwDXXnvt4l6qPn36MGXKFABuuummNrVpzTXX5O23326vQ+wUHXbNVGbeGxF92lj9AOD6zHwL+GtEzAAGF8tmZOYzABFxPXBARDwB7AN8qahzNXA2cFn7tF6SJElavTRN8PD222/TrVs3jjjiCE4++eT31fvxj3/M3XffTV1dHf3792e//fZj7bXXprGxkYaGBtZaay32339/zj//fK6++mqOO+44FixYwDbbbMOVV14JwCmnnMIXv/hFrr32WvbZZ582te+YY45h0KBB7LLLLowdO7Zdj72jRHPjJNtt45Uw9b+ZuWPx+GzgSOB1YDLw9cx8NSIuAR7IzF8U9X4O/KbYzL6ZeXRRfgTwUSrB6YHM/JeifEvgN037aU1DQ0M2zSwiqf3seuo1tW5Cu5ry/ZG1bkK7au8peGutLVMAS9LK5IknnmCHHXaodTPUjOZem4iYkpnLnCu+syeguAzoC9QDs4EfFOXNDcDMEuXNiohjImJyREyeO3fu8rVYkiRJkprRqWEqM1/KzEWZ+S7wM94byjcL2LKqam/gxVbKXwY2iIhuS5W3tN/LM7MhMxt69erVPgcjSZIkqUvr1DAVER+qengg0DTT33jgsIhYOyK2BvoBDwEPA/2KmfvWojJJxfisjE28Gzi4WH8UcFtnHIMkSZIkQQdOQBER1wF7AZtExCzgLGCviKinMiRvJnAsQGZOi4gbgD8D7wDHZ+aiYjsnAHcAdcCYzJxW7OIbwPUR8W3gUeDnHXUskiRJ0opa3unF1fFWdP6IjpzN7/BmilsMPJl5HnBeM+W3A7c3U/4M7w0TlCRJklZa3bt3Z968eWy88cYGqpVEZjJv3jy6d+9eehsdFqYkSZIkVfTu3ZtZs2bhZGgrl+7du9O7d+/S6xumJEmSpA625pprsvXWW9e6GWpnnT01uiRJkiStFgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsHZ/FbArqdeU+smtKsp3x9Z6yZIkiRJqwx7piRJkiSpBMOUJEmSJJVgmJIkSZKkEgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsHfmdJqbcjFQ2rdhHY16cRJtW6CJEmSCvZMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCYYpSZIkSSrBMCVJkiRJJRimJEmSJKkEw5QkSZIklWCYkiRJkqQSDFOSJEmSVIJhSpIkSZJKMExJkiRJUgmGKUmSJEkqwTAlSZIkSSV0WJg3kvcLAAAa6klEQVSKiDERMSciplaVfT8ipkfE4xFxS0RsUJT3iYiFEdFY3P67ap1dI+JPETEjIi6KiCjKN4qIOyPiqeLvhh11LJIkSZK0tI7smboK2HepsjuBHTNzEPAX4PSqZU9nZn1xO66q/DLgGKBfcWva5mjgrszsB9xVPJYkSZKkTtFhYSoz7wVeWarsd5n5TvHwAaB3a9uIiA8B62Xm/ZmZwDXA54vFBwBXF/evriqXJEmSpA5Xy2um/g34TdXjrSPi0Yj4Q0R8oijbAphVVWdWUQawWWbOBij+btrSjiLimIiYHBGT586d235HIEmSJKnLqkmYiohvAu8AY4ui2cBWmbkzcDLwy4hYD4hmVs/l3V9mXp6ZDZnZ0KtXr7LNliRJkqTFunX2DiNiFPBZYGgxdI/MfAt4q7g/JSKeBral0hNVPRSwN/Bicf+liPhQZs4uhgPO6axjkCRJkqRO7ZmKiH2BbwDDM3NBVXmviKgr7m9DZaKJZ4rhe29ExG7FLH4jgduK1cYDo4r7o6rKJUmSJKnDdVjPVERcB+wFbBIRs4CzqMzetzZwZzHD+QPFzH17AOdGxDvAIuC4zGyavOLfqcwM2IPKNVZN11ldANwQEV8GngMO6ahjkSRJkqSldViYyszDmyn+eQt1bwZubmHZZGDHZsrnAUNXpI2SJEmSVFYtZ/OTJEmSpFWWYUqSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCYYpSZIkSSrBMCVJkiRJJRimJEmSJKkEw5QkSZIklWCYkiRJkqQSDFOSJEmSVIJhSpIkSZJKMExJkiRJUgmGKUmSJEkqwTAlSZIkSSUYpiRJkiSpBMOUJEmSJJVgmJIkSZKkEgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCR0apiJiTETMiYipVWUbRcSdEfFU8XfDojwi4qKImBERj0fELlXrjCrqPxURo6rKd42IPxXrXBQR0ZHHI0mSJElNOrpn6ipg36XKRgN3ZWY/4K7iMcB+QL/idgxwGVTCF3AW8FFgMHBWUwAr6hxTtd7S+5IkSZKkDtGhYSoz7wVeWar4AODq4v7VwOeryq/JigeADSLiQ8CngTsz85XMfBW4E9i3WLZeZt6fmQlcU7UtSZIkSepQtbhmarPMnA1Q/N20KN8CeL6q3qyirLXyWc2Uv09EHBMRkyNi8ty5c9vlICRJkiR1bSvTBBTNXe+UJcrfX5h5eWY2ZGZDr169VqCJkiRJklRRizD1UjFEj+LvnKJ8FrBlVb3ewIvLKO/dTLkkSZIkdbgWw1REvBoRrzRzezUilr4OanmMB5pm5BsF3FZVPrKY1W834LViGOAdwLCI2LCYeGIYcEex7I2I2K2YxW9k1bYkSZIkqUN1a2XZJiu68Yi4DtgL2CQiZlGZle8C4IaI+DLwHHBIUf12YH9gBrAAOAogM1+JiG8BDxf1zs3MpjD371RmDOwB/Ka4SZIkSVKHazFMZeai6sfFFOXdq4qWOaQuMw9vYdHQZuomcHwL2xkDjGmmfDKw47LaIUmSJEntbZnXTEXEZyLiL1SuUXqw+Pv7jm6YJEmSJK3M2jIBxXnAEODJzNySyu8+3dORjZIkSZKklV1bwtQ7mTkXWCMiIjPvBHbp4HZJkiRJ0kqttQkomrwWER8AJgLXRMQc4N2ObZYkSZIkrdza0jP1eeBN4D+pDO97AfhsB7ZJkiRJklZ6bQlTp2fmosx8OzN/npk/BE7u6IZJkiRJ0sqsLWFq32bKPtPeDZEkSZKkVUmL10xFxLHAccC2EfFI1aJ1gckd3TBJkiRJWpm1NgHFDcBdwHeA0VXlb2TmnA5tlSRJkiSt5FoMU5n5KvAqcEhE7Ah8vFj0R8AwJUmSJKlLW+Y1UxFxPJVeqq2K2w0R8R8d3TBJkiRJWpm15XemjgUGZ+Z8gIg4H7gP+GlHNkySJEmSVmZtmc0vgLerHr9dlEmSJElSl9XabH7dMvMd4FrggYi4uVh0IHB1ZzROkiRJklZWrQ3zewjYJTO/FxF3A5+g0iN1XGY+3CmtkyRJkqSVVGthavFQviI8GaAkSZIkqdBamOoVESe3tDAzf9gB7ZEkSZKkVUJrYaoOWAcnm5AkSZKk92ktTM3OzHM7rSWSJEmStAppbWp0e6QkSZIkqQWthamhndYKSZIkSVrFtBimMvOVzmyIJEmSJK1KWuuZkiRJkiS1wDAlSZIkSSUYpiRJkiSpBMOUJEmSJJVgmJIkSZKkEgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBI6PUxFxHYR0Vh1ez0i/jMizo6IF6rK969a5/SImBERT0bEp6vK9y3KZkTE6M4+FkmSJEldV7fO3mFmPgnUA0REHfACcAtwFPCjzLywun5E9AcOAwYAmwMTImLbYvGlwKeAWcDDETE+M//cKQciSZIkqUvr9DC1lKHA05n5bES0VOcA4PrMfAv4a0TMAAYXy2Zk5jMAEXF9UdcwJUmSJKnD1fqaqcOA66oenxARj0fEmIjYsCjbAni+qs6soqyl8veJiGMiYnJETJ47d277tV6SJElSl1WzMBURawHDgRuLosuAvlSGAM4GftBUtZnVs5Xy9xdmXp6ZDZnZ0KtXrxVqtyRJkiRBbYf57Qc8kpkvATT9BYiInwH/WzycBWxZtV5v4MXifkvlkiRJktShajnM73CqhvhFxIeqlh0ITC3ujwcOi4i1I2JroB/wEPAw0C8iti56uQ4r6kqSJElSh6tJz1RE9KQyC9+xVcXfi4h6KkP1ZjYty8xpEXEDlYkl3gGOz8xFxXZOAO4A6oAxmTmt0w5CkiRJUpdWkzCVmQuAjZcqO6KV+ucB5zVTfjtwe7s3UJIkSZKWodaz+UmSJEnSKskwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCYYpSZIkSSrBMCVJkiRJJRimJEmSJKkEw5QkSZIklWCYkiRJkqQSDFOSJEmSVIJhSpIkSZJKMExJkiRJUgmGKUmSJEkqwTAlSZIkSSUYpiRJkiSpBMOUJEmSJJVgmJIkSZKkEgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCYYpSZIkSSrBMCVJkiRJJRimJEmSJKmEmoWpiJgZEX+KiMaImFyUbRQRd0bEU8XfDYvyiIiLImJGRDweEbtUbWdUUf+piBhVq+ORJEmS1LXUumdq78ysz8yG4vFo4K7M7AfcVTwG2A/oV9yOAS6DSvgCzgI+CgwGzmoKYJIkSZLUkWodppZ2AHB1cf9q4PNV5ddkxQPABhHxIeDTwJ2Z+UpmvgrcCezb2Y2WJEmS1PXUMkwl8LuImBIRxxRlm2XmbIDi76ZF+RbA81XrzirKWipfQkQcExGTI2Ly3Llz2/kwJEmSJHVF3Wq47yGZ+WJEbArcGRHTW6kbzZRlK+VLFmReDlwO0NDQ8L7lkiRJkrS8atYzlZkvFn/nALdQuebppWL4HsXfOUX1WcCWVav3Bl5spVySJEmSOlRNwlREfCAi1m26DwwDpgLjgaYZ+UYBtxX3xwMji1n9dgNeK4YB3gEMi4gNi4knhhVlkiRJktShajXMbzPglohoasMvM/O3EfEwcENEfBl4DjikqH87sD8wA1gAHAWQma9ExLeAh4t652bmK513GJIkSZK6qpqEqcx8BtipmfJ5wNBmyhM4voVtjQHGtHcbJUmSJKk1K9vU6JIkSZK0SjBMSZIkSVIJtZwaXZJWWs+dO7DWTWhfG65X6xZIkrTasWdKkiRJkkowTEmSJElSCYYpSZIkSSrBMCVJkiRJJRimJEmSJKkEw5QkSZIklWCYkiRJkqQSDFOSJEmSVIJhSpIkSZJK6FbrBkiSJGn1s+up19S6Ce1qyvdH1roJWgnZMyVJkiRJJRimJEmSJKkEw5QkSZIkleA1U5IkSVIXM+TiIbVuQruadOKkmuzXnilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSV4AQUWuy5cwfWugntb8P1at0CSZIkrabsmZIkSZKkEgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkowTEmSJElSCZ0epiJiy4i4OyKeiIhpEfHVovzsiHghIhqL2/5V65weETMi4smI+HRV+b5F2YyIGN3ZxyJJkiSp6+pWg32+A3w9Mx+JiHWBKRFxZ7HsR5l5YXXliOgPHAYMADYHJkTEtsXiS4FPAbOAhyNifGb+uVOOQpIkSVKX1ulhKjNnA7OL+29ExBPAFq2scgBwfWa+Bfw1ImYAg4tlMzLzGYCIuL6oa5iSJEmS1OFqes1URPQBdgYeLIpOiIjHI2JMRGxYlG0BPF+12qyirKVySZIkSepwNQtTEbEOcDPwn5n5OnAZ0Beop9Jz9YOmqs2snq2UN7evYyJickRMnjt37gq3XZIkSZJqEqYiYk0qQWpsZv4KIDNfysxFmfku8DPeG8o3C9iyavXewIutlL9PZl6emQ2Z2dCrV6/2PRhJkiRJXVItZvML4OfAE5n5w6ryD1VVOxCYWtwfDxwWEWtHxNZAP+Ah4GGgX0RsHRFrUZmkYnxnHIMkSZIk1WI2vyHAEcCfIqKxKDsDODwi6qkM1ZsJHAuQmdMi4gYqE0u8AxyfmYsAIuIE4A6gDhiTmdM680AkSZIkdV21mM1vIs1f73R7K+ucB5zXTPntra0nSZIkSR2lprP5SZIkSdKqyjAlSZIkSSUYpiRJkiSpBMOUJEmSJJVgmJIkSZKkEgxTkiRJklSCYUqSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkroVusGSJK0Otn11Gtq3YR2NeX7I2vdBElaadkzJUmSJEklGKYkSZIkqQSH+UmSpC5jyMVDat2EdjXpxEm1boLUpdkzJUmSJEklGKYkSZIkqQTDlCRJkiSV4DVTkiRJ0jI8d+7AWjehfW24Xq1bsFqwZ0qSJEmSSjBMSZIkSVIJhilJkiRJKsEwJUmSJEklGKYkSZIkqQTDlCRJkiSVYJiSJEmSpBIMU5IkSZJUgmFKkiRJkkroVusGSJKklddz5w6sdRPa14br1boFklYj9kxJkiRJUgmGKUmSJEkqYZUPUxGxb0Q8GREzImJ0rdsjSZIkqWtYpcNURNQBlwL7Af2BwyOif21bJUmSJKkrWKXDFDAYmJGZz2TmP4HrgQNq3CZJkiRJXcCqHqa2AJ6vejyrKJMkSZKkDrWqT40ezZTl+ypFHAMcUzycHxFPdmirVlEf7rxdbQK83Hm7W33ESc2d8uoIvh9Wbr4XOo/vhZWb74XO43th5dYB74U2veSrepiaBWxZ9bg38OLSlTLzcuDyzmqUWhcRkzOzodbtkFYGvh+kCt8LUoXvhVXLqj7M72GgX0RsHRFrAYcB42vcJkmSJEldwCrdM5WZ70TECcAdQB0wJjOn1bhZkiRJkrqAVTpMAWTm7cDttW6HlotDLqX3+H6QKnwvSBW+F1Yhkfm++RokSZIkScuwql8zJUmSJEk1YZhSiyJig4j4j1q3Q9L7RcQ9EdHqbE8RcXZEnFLcPzIiNu+c1kltU5yXlyyjTp+ImFrcr4+I/TundVL7ioiZEbFJM+WL/61ejm0t872jzmGYUms2AFaZMBURdbVug7QSOxIwTGlVVw8YptSlRcQqP+fB6sQwpdZcAPSNiMaI+H5EnBoRD0fE4xFxTlOliPjXiHioqPc/TaEmIuZHxHkR8VhEPBARmxXln4uIByPi0YiYUFXeKyLujIhHiu082/QNzjL2cW5EPAh8rLOfIK0+IuJbEfHVqsfnRcRXi3N/akT8KSIOLZbtFRH/W1X3kog4srg/MyLOKc7jP0XE9kV5i+f3Uu0YHBH3Fe+P+yJiu6K8R0RcX7z/xgE9qtaZX3X/4Ii4aqltHgw0AGOL91APpE4QEbdGxJSImBYRxxRlR0XEXyLiD8CQqrpXFedq0+P5S21rLeBc4NDiPD60kw5DWm4tfW6pWv7NiHgyIiYA21WV942I3xbvmz9W/R9yVUT8MCLuBr5bVX/diPhrRKxZPF6v+H9ozc45Uhmm1JrRwNOZWQ/cCfQDBlP5ZnDXiNgjInYADgWGFPUWASOK9T8APJCZOwH3Al8pyicCu2XmzsD1wGlF+VnA7zNzF+AWYCuANuxjamZ+NDMndsSToC7j58AogIhYg8rv1s2icr7vBHwS+H5EfKgN23q5OI8vA5qGbjR7fjdjOrBH8f44Ezi/KP93YEFmDgLOA3Zt64Fl5k3AZGBEZtZn5sK2riutoH/LzF2phPmTImIL4BwqIepTQP+2bigz/0nlPTGuOI/HdUSDpRW1jM8tRMSuVP6P2Rk4CPhI1eqXAycW75tTgJ9WLdsW+GRmfr2pIDPfAO4BPlMUHQbcnJlvt/NhqQV2E6qthhW3R4vH61AJV4OofKh7OCKg8m35nKLOP4Gmb++nUPmPE6A3MK74ULoW8Nei/OPAgQCZ+duIeLUoH9rKPhYBN7fXQarrysyZETEvInYGNqNyrn8cuC4zFwEvFd+kfwR4fRmb+1XxdwqV/yih5fN7aesDV0dEPyCBpm8X9wAuKtZ/PCIeX95jlGrgpIg4sLi/JXAEcE9mzgUoelm3rVXjpA7S2ucWgE8At2TmAoCIGF/8XQfYHbixWA9g7ar1biz+P1raFVS+mL4VOIr3vrxWJzBMqa0C+E5m/s8ShREnAldn5unNrPN2vjf3/iLeO98uBn6YmeMjYi/g7Kp9tLTvlvbxZgv/sEhlXEHl2qIPAmOofIHQnHdYsme/+1LL3yr+Vp/3zZ7fEXE87/3Htz/wLeDuzDwwIvpQ+caxSUu/ZVFdvnRbpJoo/n3/JPCxzFwQEfdQ6XndoYVVFr+vovJJcq1OaKbUEZr93NI0HLzQ3L/nawB/L3qzmvOP5gozc1JUJmrZE6jLzKkl2qySHOan1rwBrFvcvwP4t+JbEyJii4jYFLgLOLi4T0RsFBEfXsZ21wdeKO6PqiqfCHyx2M4wYMOivMw+pDJuAfal0vt0B5XhqYdGRF1E9KLSO/QQ8CzQPyLWjoj1qXwLuSzNnt+ZeWkxZKk+M19kyffHkVXr30sxTCQidqTSK9zkpYjYoRieeCDNq34/S51hfeDVIkhtD+xG5Rv6vSJi4+KajkOq6s/kveGrB/Ber2w1z2OtCpb1ueVe4MDiWth1gc8BZObrwF8j4pBivYiIndq4z2uA64Ar2+sg1DaGKbUoM+cBk6IyJe2ngF8C90fEn4CbgHUz88/A/wN+Vww7uhNY1jUlZ1Ppwv4j8HJV+TnAsIh4BNgPmA28UXIf0nIrrsm4G7ih6PG8BXgceAz4PXBaZv4tM58HbiiWjeW94a+tafb8bqbe94DvRMQkoPqC5cuAdYr3wGlUQl2T0VSG1P6+2G5zrgL+2wko1Il+C3QrztlvAQ9QOT/PBu4HJgCPVNX/GbBnRDwE/P/27iXUqiqO4/j3l/YwhOwhTno4CILUEJVCtIgiqFEvpcwkw2Y1CaIGRQ0iauAgMqpBCBmVGiWBmCJkkkVFhWZ3oFE5CZr0EIpCtH+Ds9LL5ajX07Vzrn4/o33W3nvttQ9ncP7rvx7X0L0XfhudjgwXoNDAOt7/lqr6ClgH7KQzVeGjYbcvBVYk2QUM0elYGI036HTSvfWfX0AnJEdGYUn9leRs4FBVHUwyH3j5GKluacy1zM5XwOKq+naM6/b3LUk6KdpKmLdW1bJ+t+V045wpDZJLgfXtD+0BnECp/1GSK+lkdzaMdSDV+PuWJI25JKvojHhwD7Y+MDMlSZIkST1wzpQkSZIk9cBgSpIkSZJ6YDAlSZIkST0wmJIkDbQktyeptlfRWNZ7b5Kvkwwl2ZXk1SRTxvIZkqRTm8GUJGnQLaGz6fHdY1VhkpuBh4FbqmoGMAf4BJjW5doJI8skSQKDKUnSAEsyGVgArGBYMJXkjCQvtazSxiSb2j4rJJmbZHuSL5NsSdJtk+/HgUeq6keAqjpUVaurak+rY1+SJ5PsABYnmZ3k05bJ2pDk/Hbdh0nmteOLkuxrx8uTvJdkc5I9SZ46aV+SJKlvDKYkSYPsNmBzVe0Ffkkyp5XfAUwHZgEPAPMBkpwJrAIWVdVcYDXwTJd6Z9DZoPlY/qqqhVW1FlgDPFZVVwG7gdEER1cDS4HZdAKyeaO4R5I0jhhMSZIG2RJgbTte2z4DLATerqq/q+onYFsrvwKYCWxNshN4Arj4WA9IMivJziTfJblr2Kl17fx5wJSq2t7KXwOuG0Xbt1bVz1X1J/Bua7Mk6RQysd8NkCSpmyQXAjcAM5MUMAGoJI8COdptwFBVzT9O9UN05kltq6rdwOwkLwKThl3zxyiaeZAjHZPnjDhXx/ksSRrnzExJkgbVImBNVV1WVdOr6hLgBzoZnh3AnW3u1DTg+nbPHmBqksPD/pLM6FL3s8DKJMOzVpO6XEdV7Qd+TXJtK1oG/Jul2gfMHdbe4W5KckGSSXSGK348mpeWJI0fZqYkSYNqCfDciLJ3gHuAB4EbgW+AvcBnwP6qOtAWonihDc+bCDxPJxN1WFVtSjIVeL+t1vdbq2vLUdpyH/BKknOB74H7W/lKYH2SZcAHI+7ZAbwOXA68WVVfnMjLS5IGX6ocdSBJGn+STK6q39twwM+BBW3+VN8lWQ7Mq6qH+t0WSdLJY2ZKkjRebWyb7J4FPD0ogZQk6fRhZkqSJEmSeuACFJIkSZLUA4MpSZIkSeqBwZQkSZIk9cBgSpIkSZJ6YDAlSZIkST0wmJIkSZKkHvwDCcHhWKiRR04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a5fba1cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"age_group\", hue=\"offer_type\", data=master_df)\n",
    "plt.title('Most Popular Offers to Each Age Group')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Age Group')\n",
    "xlabels = ['teenager','young-adult','adult','elderly']\n",
    "g.set_xticklabels(xlabels)\n",
    "plt.xticks(rotation = 0)\n",
    "plt.legend(title='Offer Type')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most common offer type among all age groups is the BOGO , followed by the Discount Offers. Whereas, the least common offer to be sent is the informational offers. I believe that BOGO offers are more attractive compared to other offers provided by Starbucks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the demographic data of the customers who gets the highest income range , males or females?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a5a4882b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x=\"gender\", hue=\"income_range\", data= master_df[master_df[\"gender\"] != 3])\n",
    "plt.title('Income Range vs Gender')\n",
    "plt.ylabel('Income Range')\n",
    "xlabels = ['Female', 'Male']\n",
    "g.set_xticklabels(xlabels)\n",
    "plt.xlabel('Gender')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers with High income (Above 90,000) are mostly female customers. Whereas, Average Income(30,000 - 60,000) customers are mostly males."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How many new members Starbucks got each year?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a5fba1b38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "sns.countplot(master_df['membership_year'])\n",
    "plt.title('Number of Profiles In Each Year')\n",
    "plt.ylabel('Number of Profiles')\n",
    "plt.xlabel('Year')\n",
    "plt.xticks()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2017 was the best year for Starbucks in terms of the number of new members."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Which type of promotions(offers) each gender likes?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a5a458128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x='gender', hue=\"offer_type\", data= master_df[master_df[\"gender\"] != 3])\n",
    "plt.title('Most Popular Offers to Each Gender')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Gender')\n",
    "xlabels = ['Female', 'Male']\n",
    "g.set_xticklabels(xlabels)\n",
    "plt.legend(title='Offer Type')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both genders liked BOGO and lest liked is Infomational offer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is average time to complete offer?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(372.24939705130396, 15.510391543804332)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tran_avg_len = master_df.groupby(['customer_id', 'offer_id'])['time_h'].mean().reset_index()\n",
    "tran_avg_len['time_h'].mean(), tran_avg_len['time_h'].mean() / 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean time it takes a customer to complete an offer is less than 16 days (372 hours)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From all the offers the customers viewed , how many offers they completed?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a5a425748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "g = sns.countplot(x='gender', hue=\"event\", data= master_df[master_df[\"gender\"] != 3])\n",
    "plt.title('Most Popular Offers to Each Gender')\n",
    "plt.ylabel('Total')\n",
    "plt.xlabel('Gender')\n",
    "xlabels = ['Female', 'Male']\n",
    "g.set_xticklabels(xlabels)\n",
    "plt.legend(title='Offer Type')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Females responded highly in terms of completing offer compared to male."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I believe I got to a point where I had very good results when it comes to the prediction model. I understood the data provided by Starbucks very well.\n",
    "\n",
    "I believe that the idea of improving the data collection or fixing issues related with the missing data or NaNs would be really helpful. In my opinion, I think that we can get great insights from this data set and great prediction models might be also built to solve problems statemetns associated with this data set . Examples for these model are the following :\n",
    "\n",
    "Building a model that can predict which kind of offers to be sent to which customer?\n",
    "Building a model that can predict which customers would buy any way ( regardless if there is an offer or not)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I really enjoyed working on this project which allowed me to polish my skills in Data Preprocessing and Modeling. The most difficult part in my opinion was understanding data. \n",
    "\n",
    "The Preprocessing Step was the longest and most challanging part to complete. The Dataset itself is inspiring. Honestly,Everything was amazing."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}