Analysis_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": [
    "# Analysis Data\n",
    "\n",
    "## Portfolio\n",
    "\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) \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>channels</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_type</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>informational</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>bogo</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email]</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>discount</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>discount</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>[email, mobile, social]</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>informational</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>[web, email, mobile, social]</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>bogo</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>[web, email, mobile]</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>discount</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reward                      channels  difficulty  duration     offer_type  \\\n",
       "0      10       [email, mobile, social]          10         7           bogo   \n",
       "1      10  [web, email, mobile, social]          10         5           bogo   \n",
       "2       0          [web, email, mobile]           0         4  informational   \n",
       "3       5          [web, email, mobile]           5         7           bogo   \n",
       "4       5                  [web, email]          20        10       discount   \n",
       "5       3  [web, email, mobile, social]           7         7       discount   \n",
       "6       2  [web, email, mobile, social]          10        10       discount   \n",
       "7       0       [email, mobile, social]           0         3  informational   \n",
       "8       5  [web, email, mobile, social]           5         5           bogo   \n",
       "9       2          [web, email, mobile]          10         7       discount   \n",
       "\n",
       "                                 id  \n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd  \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0  \n",
       "2  3f207df678b143eea3cee63160fa8bed  \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9  \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7  \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2  \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4  \n",
       "7  5a8bc65990b245e5a138643cd4eb9837  \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d  \n",
       "9  2906b810c7d4411798c6938adc9daaa5  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>event</th>\n",
       "      <th>value</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113955</th>\n",
       "      <td>6ccf170a9dc044b7947456417852bc43</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236658</th>\n",
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       "      <td>transaction</td>\n",
       "      <td>{'amount': 1.0}</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23151</th>\n",
       "      <td>5000aa9e8fc84433a6bb022471ec1578</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 18.3}</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28421</th>\n",
       "      <td>bc732350a9684a669cc72bea24c48e4c</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>{'offer_id': '2298d6c36e964ae4a3e7e9706d1fb8c2...</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154250</th>\n",
       "      <td>52795bbd3ca440acb6c213dc17628b99</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '5a8bc65990b245e5a138643cd4eb9837'}</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304861</th>\n",
       "      <td>1615981b0a114e42bc1b0f65b2ac4c8b</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118555</th>\n",
       "      <td>c14688e439ac4629b82c0144a93bdcf1</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22552</th>\n",
       "      <td>50957b9a771f4e5d9df530c13a1184f1</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83860</th>\n",
       "      <td>e3d7fa1e32ac4eeb9003f2b9af563ac8</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 0.9500000000000001}</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110225</th>\n",
       "      <td>394959bac98241098e68e063d28687e0</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 0.18}</td>\n",
       "      <td>330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person            event  \\\n",
       "113955  6ccf170a9dc044b7947456417852bc43   offer received   \n",
       "236658  10c8734cbcc744a1975c581e4382d168      transaction   \n",
       "23151   5000aa9e8fc84433a6bb022471ec1578      transaction   \n",
       "28421   bc732350a9684a669cc72bea24c48e4c  offer completed   \n",
       "154250  52795bbd3ca440acb6c213dc17628b99   offer received   \n",
       "304861  1615981b0a114e42bc1b0f65b2ac4c8b     offer viewed   \n",
       "118555  c14688e439ac4629b82c0144a93bdcf1   offer received   \n",
       "22552   50957b9a771f4e5d9df530c13a1184f1     offer viewed   \n",
       "83860   e3d7fa1e32ac4eeb9003f2b9af563ac8      transaction   \n",
       "110225  394959bac98241098e68e063d28687e0      transaction   \n",
       "\n",
       "                                                    value  time  \n",
       "113955   {'offer id': 'ae264e3637204a6fb9bb56bc8210ddfd'}   336  \n",
       "236658                                    {'amount': 1.0}   546  \n",
       "23151                                    {'amount': 18.3}    24  \n",
       "28421   {'offer_id': '2298d6c36e964ae4a3e7e9706d1fb8c2...    42  \n",
       "154250   {'offer id': '5a8bc65990b245e5a138643cd4eb9837'}   408  \n",
       "304861   {'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}   708  \n",
       "118555   {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}   336  \n",
       "22552    {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}    24  \n",
       "83860                      {'amount': 0.9500000000000001}   210  \n",
       "110225                                   {'amount': 0.18}   330  "
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "scrolled": true
   },
   "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": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.event.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "person    0\n",
       "event     0\n",
       "value     0\n",
       "time      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
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       "      <th>7015</th>\n",
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       "      <td>118</td>\n",
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       "      <td>20161219</td>\n",
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      ],
      "text/plain": [
       "      gender  age                                id  became_member_on   income\n",
       "7015    None  118  0223ed57fef54575ac0b256937dc68fa          20170624      NaN\n",
       "388        M   35  027596e6ed19471392923eb833b25fd3          20170829  64000.0\n",
       "9123       M   25  4c7588114f90431095e3ee47988ff530          20171222  58000.0\n",
       "1722       F   64  2c49fcc99b194fbd98c68f159a01da87          20160913  72000.0\n",
       "5388       F   35  1ce8e5faa25f4daabdf8c43c1168cb17          20170720  33000.0\n",
       "13133      M   38  3b346dd87f574892b5080d676b4e69ff          20170708  56000.0\n",
       "14707      F   49  24be0137591244c7b191b11c0da21218          20160308  72000.0\n",
       "3268       M   59  cc21c82c8f074b6c9b44ce98a1917bfd          20160808  60000.0\n",
       "15641      O   48  3d6dda5a29c345fa99982972c7012b15          20170623  94000.0\n",
       "14916   None  118  c134adbefe8449ff9004f03d0cdf11f7          20161219      NaN"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender              2175\n",
       "age                    0\n",
       "id                     0\n",
       "became_member_on       0\n",
       "income              2175\n",
       "dtype: int64"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e43a10f28>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(profile.age,kde=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": true
   },
   "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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>became_member_on</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68be06ca386d4c31939f3a4f0e3dd783</td>\n",
       "      <td>20170212</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>38fe809add3b4fcf9315a9694bb96ff5</td>\n",
       "      <td>20180712</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a03223e636434f42ac4c3df47e8bac43</td>\n",
       "      <td>20170804</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>8ec6ce2a7e7949b1bf142def7d0e0586</td>\n",
       "      <td>20170925</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>68617ca6246f4fbc85e91a2a49552598</td>\n",
       "      <td>20171002</td>\n",
       "      <td>NaN</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>16980</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>5c686d09ca4d475a8f750f2ba07e0440</td>\n",
       "      <td>20160901</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16982</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>d9ca82f550ac4ee58b6299cf1e5c824a</td>\n",
       "      <td>20160415</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16989</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>ca45ee1883624304bac1e4c8a114f045</td>\n",
       "      <td>20180305</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16991</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>a9a20fa8b5504360beb4e7c8712f8306</td>\n",
       "      <td>20160116</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16994</th>\n",
       "      <td>None</td>\n",
       "      <td>118</td>\n",
       "      <td>c02b10e8752c4d8e9b73f918558531f7</td>\n",
       "      <td>20151211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2175 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender  age                                id  became_member_on  income\n",
       "0       None  118  68be06ca386d4c31939f3a4f0e3dd783          20170212     NaN\n",
       "2       None  118  38fe809add3b4fcf9315a9694bb96ff5          20180712     NaN\n",
       "4       None  118  a03223e636434f42ac4c3df47e8bac43          20170804     NaN\n",
       "6       None  118  8ec6ce2a7e7949b1bf142def7d0e0586          20170925     NaN\n",
       "7       None  118  68617ca6246f4fbc85e91a2a49552598          20171002     NaN\n",
       "...      ...  ...                               ...               ...     ...\n",
       "16980   None  118  5c686d09ca4d475a8f750f2ba07e0440          20160901     NaN\n",
       "16982   None  118  d9ca82f550ac4ee58b6299cf1e5c824a          20160415     NaN\n",
       "16989   None  118  ca45ee1883624304bac1e4c8a114f045          20180305     NaN\n",
       "16991   None  118  a9a20fa8b5504360beb4e7c8712f8306          20160116     NaN\n",
       "16994   None  118  c02b10e8752c4d8e9b73f918558531f7          20151211     NaN\n",
       "\n",
       "[2175 rows x 5 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile[profile['age']>110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_df = profile.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_df.dropna(inplace=True)\n",
    "profile_df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e439d8780>"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(profile_df.income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29e4399bc88>"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_count = profile_df.gender.value_counts()\n",
    "genders = profile_df.gender.unique()\n",
    "sns.barplot(gender_count,y=genders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "ages_df = profile_df\n",
    "ages_df.loc[(profile_df['age'] >10) & (profile_df['age'] <=19 ), 'Age'] = \"10's\"\n",
    "ages_df.loc[(profile_df['age'] > 20) & (profile_df['age'] <= 29), 'Age'] = \"20's\"\n",
    "ages_df.loc[(profile_df['age'] > 30) & (profile_df['age'] <= 39), 'Age'] = \"30's\"\n",
    "ages_df.loc[(profile_df['age'] > 40) & (profile_df['age'] <= 49), 'Age'] = \"40's\"\n",
    "ages_df.loc[(profile_df['age'] > 50) & (profile_df['age'] <= 59), 'Age'] = \"50's\"\n",
    "ages_df.loc[(profile_df['age'] > 60) & (profile_df['age'] <= 69), 'Age'] = \"60's\"\n",
    "ages_df.loc[(profile_df['age'] > 70) & (profile_df['age'] <= 79), 'Age'] = \"70's\"\n",
    "ages_df.loc[(profile_df['age'] > 80) & (profile_df['age'] <= 89), 'Age'] = \"80's\"\n",
    "ages_df.loc[(profile_df['age'] > 90) & (profile_df['age'] <= 99), 'Age'] = \"90's\"\n",
    "ages_df.loc[profile_df['age'] > 100, 'Age'] = \"100's\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df = ages_df[ages_df['gender'] == 'F']\n",
    "male_df = ages_df[ages_df['gender'] == 'M']\n",
    "other_df = ages_df[ages_df['gender'] == 'O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Other income')"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,5),\n",
    "                    nrows=1,\n",
    "                    ncols=3)\n",
    "plt.sca(ax[0])\n",
    "male_age = male_df.sort_values(by='age')\n",
    "sns.barplot(x=male_age.Age,y=male_df.income)\n",
    "plt.ylim(0,100000)\n",
    "plt.title('Male income')\n",
    "\n",
    "plt.sca(ax[1])\n",
    "female_age = female_df.sort_values(by='age')\n",
    "sns.barplot(x=female_age.Age,y=female_df.income)\n",
    "plt.ylim(0,100000)\n",
    "plt.title('Female income')\n",
    "\n",
    "plt.sca(ax[2])\n",
    "other_age = other_df.sort_values(by='age')\n",
    "plt.ylim(0,100000)\n",
    "sns.barplot(x=other_age.Age,y=other_df.income)\n",
    "plt.title('Other income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "\n",
    "actual_date = date.today()\n",
    "actual_date = pd.to_datetime(actual_date)\n",
    "profile_df['became_member_on'] = pd.to_datetime(profile_df['became_member_on'], format='%Y%m%d')\n",
    "profile_df.became_member_on = (actual_date - profile_df['became_member_on'])/np.timedelta64(1,'D')\n",
    "profile_df.rename(columns={'became_member_on': 'membership_days'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29e438d2b00>]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "member = profile_df.groupby(['membership_days'])['id'].count()\n",
    "plt.figure(figsize=(16,4))\n",
    "plt.xlabel('Membership Days')\n",
    "plt.ylabel('Number of people')\n",
    "plt.plot(member)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding gender\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "encoder = MultiLabelBinarizer()\n",
    "profile_encoded = encoder.fit(profile_df['gender'])\n",
    "gender_df = pd.DataFrame(profile_encoded.transform(profile_df['gender']),columns=profile_encoded.classes_)\n",
    "profile_df = profile_df.drop(columns=['gender'],axis=1)\n",
    "profile_df = pd.concat([profile_df,gender_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>id</th>\n",
       "      <th>membership_days</th>\n",
       "      <th>income</th>\n",
       "      <th>Age</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55</td>\n",
       "      <td>0610b486422d4921ae7d2bf64640c50b</td>\n",
       "      <td>1046.0</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>50's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>78afa995795e4d85b5d9ceeca43f5fef</td>\n",
       "      <td>1113.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>70's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>e2127556f4f64592b11af22de27a7932</td>\n",
       "      <td>761.0</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>60's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>389bc3fa690240e798340f5a15918d5c</td>\n",
       "      <td>837.0</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>60's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>58</td>\n",
       "      <td>2eeac8d8feae4a8cad5a6af0499a211d</td>\n",
       "      <td>927.0</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>50's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14820</th>\n",
       "      <td>45</td>\n",
       "      <td>6d5f3a774f3d4714ab0c092238f3a1d7</td>\n",
       "      <td>722.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>40's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14821</th>\n",
       "      <td>61</td>\n",
       "      <td>2cb4f97358b841b9a9773a7aa05a9d77</td>\n",
       "      <td>683.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>60's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14822</th>\n",
       "      <td>49</td>\n",
       "      <td>01d26f638c274aa0b965d24cefe3183f</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>40's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14823</th>\n",
       "      <td>83</td>\n",
       "      <td>9dc1421481194dcd9400aec7c9ae6366</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>80's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14824</th>\n",
       "      <td>62</td>\n",
       "      <td>e4052622e5ba45a8b96b59aba68cf068</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>60's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age                                id  membership_days    income   Age  \\\n",
       "0       55  0610b486422d4921ae7d2bf64640c50b           1046.0  112000.0  50's   \n",
       "1       75  78afa995795e4d85b5d9ceeca43f5fef           1113.0  100000.0  70's   \n",
       "2       68  e2127556f4f64592b11af22de27a7932            761.0   70000.0  60's   \n",
       "3       65  389bc3fa690240e798340f5a15918d5c            837.0   53000.0  60's   \n",
       "4       58  2eeac8d8feae4a8cad5a6af0499a211d            927.0   51000.0  50's   \n",
       "...    ...                               ...              ...       ...   ...   \n",
       "14820   45  6d5f3a774f3d4714ab0c092238f3a1d7            722.0   54000.0  40's   \n",
       "14821   61  2cb4f97358b841b9a9773a7aa05a9d77            683.0   72000.0  60's   \n",
       "14822   49  01d26f638c274aa0b965d24cefe3183f           1216.0   73000.0  40's   \n",
       "14823   83  9dc1421481194dcd9400aec7c9ae6366           1541.0   50000.0  80's   \n",
       "14824   62  e4052622e5ba45a8b96b59aba68cf068           1039.0   82000.0  60's   \n",
       "\n",
       "       F  M  O  \n",
       "0      1  0  0  \n",
       "1      1  0  0  \n",
       "2      0  1  0  \n",
       "3      0  1  0  \n",
       "4      0  1  0  \n",
       "...   .. .. ..  \n",
       "14820  1  0  0  \n",
       "14821  0  1  0  \n",
       "14822  0  1  0  \n",
       "14823  1  0  0  \n",
       "14824  1  0  0  \n",
       "\n",
       "[14825 rows x 8 columns]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_df = portfolio.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "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": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript_df = transcript.copy()\n",
    "transcript_df.event.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>164248</th>\n",
       "      <td>6731c03f14924f00b9c7507d9490657c</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>408</td>\n",
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       "      <td>{'amount': 16.37}</td>\n",
       "      <td>468</td>\n",
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       "    <tr>\n",
       "      <th>3393</th>\n",
       "      <td>cd9bac9e8aea4609929a55b9b468c88e</td>\n",
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       "      <td>offer received</td>\n",
       "      <td>{'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}</td>\n",
       "      <td>0</td>\n",
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       "      <th>12489</th>\n",
       "      <td>2dfb4bd2746d441393a3f2ed9ef03ce5</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '5a8bc65990b245e5a138643cd4eb9837'}</td>\n",
       "      <td>0</td>\n",
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       "      <th>265792</th>\n",
       "      <td>e242bb9c734c4c6093feadfd95cf5e87</td>\n",
       "      <td>offer completed</td>\n",
       "      <td>{'offer_id': '4d5c57ea9a6940dd891ad53e9dbe8da0...</td>\n",
       "      <td>588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275191</th>\n",
       "      <td>079a60e8e7694ffb8d9a4ddf45e85897</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 15.12}</td>\n",
       "      <td>606</td>\n",
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       "      <th>52063</th>\n",
       "      <td>65824e7a3bd14f29a344c0a9f3f47e8d</td>\n",
       "      <td>transaction</td>\n",
       "      <td>{'amount': 9.26}</td>\n",
       "      <td>156</td>\n",
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       "    <tr>\n",
       "      <th>139656</th>\n",
       "      <td>dd31ce6c87d34180ba57901368e03b33</td>\n",
       "      <td>offer viewed</td>\n",
       "      <td>{'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}</td>\n",
       "      <td>372</td>\n",
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       "    <tr>\n",
       "      <th>250610</th>\n",
       "      <td>50c3063af3c04074b817b1488e238e66</td>\n",
       "      <td>offer received</td>\n",
       "      <td>{'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person            event  \\\n",
       "164248  6731c03f14924f00b9c7507d9490657c     offer viewed   \n",
       "193135  17cd1a02965e439da1f0029d50792c56      transaction   \n",
       "3393    cd9bac9e8aea4609929a55b9b468c88e   offer received   \n",
       "11850   73c456d983484fbb884aa78ac723c36b   offer received   \n",
       "12489   2dfb4bd2746d441393a3f2ed9ef03ce5   offer received   \n",
       "265792  e242bb9c734c4c6093feadfd95cf5e87  offer completed   \n",
       "275191  079a60e8e7694ffb8d9a4ddf45e85897      transaction   \n",
       "52063   65824e7a3bd14f29a344c0a9f3f47e8d      transaction   \n",
       "139656  dd31ce6c87d34180ba57901368e03b33     offer viewed   \n",
       "250610  50c3063af3c04074b817b1488e238e66   offer received   \n",
       "\n",
       "                                                    value  time  \n",
       "164248   {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}   408  \n",
       "193135                                  {'amount': 16.37}   468  \n",
       "3393     {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}     0  \n",
       "11850    {'offer id': 'fafdcd668e3743c1bb461111dcafc2a4'}     0  \n",
       "12489    {'offer id': '5a8bc65990b245e5a138643cd4eb9837'}     0  \n",
       "265792  {'offer_id': '4d5c57ea9a6940dd891ad53e9dbe8da0...   588  \n",
       "275191                                  {'amount': 15.12}   606  \n",
       "52063                                    {'amount': 9.26}   156  \n",
       "139656   {'offer id': 'f19421c1d4aa40978ebb69ca19b0e20d'}   372  \n",
       "250610   {'offer id': '2298d6c36e964ae4a3e7e9706d1fb8c2'}   576  "
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcript_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_df = transcript_df\n",
    "event_df['event'] = event_df.event.str.replace(' ','_')\n",
    "response_df = event_df.copy()\n",
    "trans_encod =pd.get_dummies(event_df['event'])\n",
    "event_df = pd.concat([event_df,trans_encod],axis=1)\n",
    "event_df = event_df.drop(columns=['event'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_df = event_df.set_index('person')\n",
    "profile_df = profile_df.set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 14825 entries, 0610b486422d4921ae7d2bf64640c50b to e4052622e5ba45a8b96b59aba68cf068\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   age              14825 non-null  int64  \n",
      " 1   membership_days  14825 non-null  float64\n",
      " 2   income           14825 non-null  float64\n",
      " 3   Age              13308 non-null  object \n",
      " 4   F                14825 non-null  int32  \n",
      " 5   M                14825 non-null  int32  \n",
      " 6   O                14825 non-null  int32  \n",
      "dtypes: float64(2), int32(3), int64(1), object(1)\n",
      "memory usage: 752.8+ KB\n"
     ]
    }
   ],
   "source": [
    "profile_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "#profile_df = profile_df.reset_index()\n",
    "profile_df = profile_df.rename(columns ={'id': 'person'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>e2127556f4f64592b11af22de27a7932</th>\n",
       "      <td>68</td>\n",
       "      <td>761.0</td>\n",
       "      <td>70000.0</td>\n",
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       "      <td>53000.0</td>\n",
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       "      <td>54000.0</td>\n",
       "      <td>40's</td>\n",
       "      <td>1</td>\n",
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       "      <th>2cb4f97358b841b9a9773a7aa05a9d77</th>\n",
       "      <td>61</td>\n",
       "      <td>683.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>60's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01d26f638c274aa0b965d24cefe3183f</th>\n",
       "      <td>49</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>73000.0</td>\n",
       "      <td>40's</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9dc1421481194dcd9400aec7c9ae6366</th>\n",
       "      <td>83</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>80's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e4052622e5ba45a8b96b59aba68cf068</th>\n",
       "      <td>62</td>\n",
       "      <td>1039.0</td>\n",
       "      <td>82000.0</td>\n",
       "      <td>60's</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14825 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  age  membership_days    income   Age  F  M  \\\n",
       "id                                                                             \n",
       "0610b486422d4921ae7d2bf64640c50b   55           1046.0  112000.0  50's  1  0   \n",
       "78afa995795e4d85b5d9ceeca43f5fef   75           1113.0  100000.0  70's  1  0   \n",
       "e2127556f4f64592b11af22de27a7932   68            761.0   70000.0  60's  0  1   \n",
       "389bc3fa690240e798340f5a15918d5c   65            837.0   53000.0  60's  0  1   \n",
       "2eeac8d8feae4a8cad5a6af0499a211d   58            927.0   51000.0  50's  0  1   \n",
       "...                               ...              ...       ...   ... .. ..   \n",
       "6d5f3a774f3d4714ab0c092238f3a1d7   45            722.0   54000.0  40's  1  0   \n",
       "2cb4f97358b841b9a9773a7aa05a9d77   61            683.0   72000.0  60's  0  1   \n",
       "01d26f638c274aa0b965d24cefe3183f   49           1216.0   73000.0  40's  0  1   \n",
       "9dc1421481194dcd9400aec7c9ae6366   83           1541.0   50000.0  80's  1  0   \n",
       "e4052622e5ba45a8b96b59aba68cf068   62           1039.0   82000.0  60's  1  0   \n",
       "\n",
       "                                  O  \n",
       "id                                   \n",
       "0610b486422d4921ae7d2bf64640c50b  0  \n",
       "78afa995795e4d85b5d9ceeca43f5fef  0  \n",
       "e2127556f4f64592b11af22de27a7932  0  \n",
       "389bc3fa690240e798340f5a15918d5c  0  \n",
       "2eeac8d8feae4a8cad5a6af0499a211d  0  \n",
       "...                              ..  \n",
       "6d5f3a774f3d4714ab0c092238f3a1d7  0  \n",
       "2cb4f97358b841b9a9773a7aa05a9d77  0  \n",
       "01d26f638c274aa0b965d24cefe3183f  0  \n",
       "9dc1421481194dcd9400aec7c9ae6366  0  \n",
       "e4052622e5ba45a8b96b59aba68cf068  0  \n",
       "\n",
       "[14825 rows x 7 columns]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profile_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = profile_df.merge(event_df, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "female_df = merged_df[merged_df['F'] == 1]\n",
    "male_df = merged_df[merged_df['M'] == 1]\n",
    "other_df = merged_df[merged_df['O'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotOffer(df,df_name):\n",
    "    receivedOffer = df.offer_received.sum()\n",
    "    viewedOffer = df.offer_viewed.sum()\n",
    "    completedOffer = df.offer_completed.sum()\n",
    "    #d = 'offer_received':[receivedOffer],'offer_viewed':[viewedOffer],'offer_completed':[completedOffer]}\n",
    "    diff_r_v = ((receivedOffer-viewedOffer)/receivedOffer) * 100\n",
    "    diff_v_c = ((viewedOffer-completedOffer)/viewedOffer) * 100\n",
    "    diff_r_c = ((receivedOffer-completedOffer)/receivedOffer) * 100\n",
    "    \n",
    "    print(df_name + ' diffrence Received and Viewed: -{:.2f}%'.format(diff_r_v))\n",
    "    print(df_name + ' diffrence Viewed and Completed: -{:.2f}%'.format(diff_v_c))\n",
    "    print(df_name + ' diffrence Received and Completed: -{:.2f}%'.format(diff_r_c))\n",
    "    print('\\n')\n",
    "    return [receivedOffer,viewedOffer,completedOffer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male diffrence Received and Viewed: -25.78%\n",
      "Male diffrence Viewed and Completed: -41.82%\n",
      "Male diffrence Received and Completed: -56.82%\n",
      "\n",
      "\n",
      "Female diffrence Received and Viewed: -24.29%\n",
      "Female diffrence Viewed and Completed: -25.54%\n",
      "Female diffrence Received and Completed: -43.63%\n",
      "\n",
      "\n",
      "Others diffrence Received and Viewed: -15.61%\n",
      "Others diffrence Viewed and Completed: -35.19%\n",
      "Others diffrence Received and Completed: -45.31%\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Other offers')"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(15,5),\n",
    "                    nrows=1,\n",
    "                    ncols=3)\n",
    "offers = ['offer_received','offer_viewed','offer_completed']\n",
    "plt.sca(ax[0])\n",
    "sns.barplot(x=offers,y=plotOffer(male_df , 'Male'))\n",
    "plt.ylim(0,50000)\n",
    "plt.title('Male offers')\n",
    "\n",
    "plt.sca(ax[1])\n",
    "sns.barplot(x=offers,y=plotOffer(female_df,'Female'))\n",
    "plt.ylim(0,50000)\n",
    "plt.title('Female offers')\n",
    "\n",
    "plt.sca(ax[2])\n",
    "sns.barplot(x=offers,y=plotOffer(other_df,'Others'))\n",
    "plt.ylim(0,3000)\n",
    "plt.title('Other offers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_df = response_df[response_df['event'] == 'offer_completed'].copy()\n",
    "completed_df = pd.concat([completed_df, completed_df['value'].apply(pd.Series)], axis=1)\n",
    "completed_df.drop(['value','reward','time'],inplace=True, axis=1)\n",
    "completed_df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "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>person</th>\n",
       "      <th>event</th>\n",
       "      <th>offer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12658</th>\n",
       "      <td>9fa9ae8f57894cc9a3b8a9bbe0fc1b2f</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12672</th>\n",
       "      <td>fe97aa22dd3e48c8b143116a8403dd52</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12679</th>\n",
       "      <td>629fc02d56414d91bca360decdfa9288</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12692</th>\n",
       "      <td>676506bad68e4161b9bbaffeb039626b</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12697</th>\n",
       "      <td>8f7dd3b2afe14c078eb4f6e6fe4ba97d</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306470</th>\n",
       "      <td>452cdae2c68e4732a4177d3929562690</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306475</th>\n",
       "      <td>0c027f5f34dd4b9eba0a25785c611273</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306497</th>\n",
       "      <td>a6f84f4e976f44508c358cc9aba6d2b3</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306509</th>\n",
       "      <td>8431c16f8e1d440880db371a68f82dd0</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306527</th>\n",
       "      <td>24f56b5e1849462093931b164eb803b5</td>\n",
       "      <td>offer_completed</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28996 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  person            event  \\\n",
       "12658   9fa9ae8f57894cc9a3b8a9bbe0fc1b2f  offer_completed   \n",
       "12672   fe97aa22dd3e48c8b143116a8403dd52  offer_completed   \n",
       "12679   629fc02d56414d91bca360decdfa9288  offer_completed   \n",
       "12692   676506bad68e4161b9bbaffeb039626b  offer_completed   \n",
       "12697   8f7dd3b2afe14c078eb4f6e6fe4ba97d  offer_completed   \n",
       "...                                  ...              ...   \n",
       "306470  452cdae2c68e4732a4177d3929562690  offer_completed   \n",
       "306475  0c027f5f34dd4b9eba0a25785c611273  offer_completed   \n",
       "306497  a6f84f4e976f44508c358cc9aba6d2b3  offer_completed   \n",
       "306509  8431c16f8e1d440880db371a68f82dd0  offer_completed   \n",
       "306527  24f56b5e1849462093931b164eb803b5  offer_completed   \n",
       "\n",
       "                                offer_id  \n",
       "12658   2906b810c7d4411798c6938adc9daaa5  \n",
       "12672   fafdcd668e3743c1bb461111dcafc2a4  \n",
       "12679   9b98b8c7a33c4b65b9aebfe6a799e6d9  \n",
       "12692   ae264e3637204a6fb9bb56bc8210ddfd  \n",
       "12697   4d5c57ea9a6940dd891ad53e9dbe8da0  \n",
       "...                                  ...  \n",
       "306470  fafdcd668e3743c1bb461111dcafc2a4  \n",
       "306475  2298d6c36e964ae4a3e7e9706d1fb8c2  \n",
       "306497  2298d6c36e964ae4a3e7e9706d1fb8c2  \n",
       "306509  fafdcd668e3743c1bb461111dcafc2a4  \n",
       "306527  fafdcd668e3743c1bb461111dcafc2a4  \n",
       "\n",
       "[28996 rows x 3 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "viewed_df = response_df[response_df['event'] == 'offer_viewed'].copy()\n",
    "viewed_df = pd.concat([viewed_df, viewed_df['value'].apply(pd.Series)], axis=1)\n",
    "viewed_df.drop(['value','time'],inplace=True, axis=1)\n",
    "viewed_df.rename(columns={'offer id': 'offer_id'},inplace=True)\n",
    "viewed_df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_and_viewed = viewed_df.merge(completed_df,how='left',on=['person','offer_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_and_viewed.rename(columns={'event_x': 'offer_viewed', 'event_y': 'offer_completed'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_and_viewed['offer_viewed']= completed_and_viewed.offer_viewed.apply(lambda x: 1 if x == 'offer_viewed' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_and_viewed['offer_completed']= completed_and_viewed.offer_completed.apply(lambda x: 1 if x == 'offer_completed' else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49135 entries, 0 to 49134\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   person           49135 non-null  object\n",
      " 1   offer_viewed     49135 non-null  int64 \n",
      " 2   offer_id         49135 non-null  object\n",
      " 3   offer_completed  49135 non-null  int64 \n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "completed_and_viewed.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_response(df):\n",
    "    if (df.offer_completed == 1) & (df.offer_viewed == 1):\n",
    "        response = 1\n",
    "    else:\n",
    "        response = 0\n",
    "    return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_and_viewed['response'] = completed_and_viewed.apply(return_response,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "completed_and_viewed.drop(['offer_viewed','offer_completed'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49135, 3)"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "completed_and_viewed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "profile_df.reset_index(inplace=True)\n",
    "profile_df = profile_df.rename(columns={'id':'person'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = completed_and_viewed.merge(profile_df,how='left',on='person')\n",
    "final_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>membership_days</th>\n",
       "      <th>income</th>\n",
       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>345f9431137a49b3963221a55567c4a8</td>\n",
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       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1147.0</td>\n",
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       "      <td>50's</td>\n",
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       "      <td>102000.0</td>\n",
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       "      <td>ec1761735b034ae8874bc979f0cd5862</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1391.0</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>50's</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1076.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>49133</th>\n",
       "      <td>abc4359eb34e4e2ca2349da2ddf771b6</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
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       "      <td>51.0</td>\n",
       "      <td>2122.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>50's</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38129 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 person                          offer_id  \\\n",
       "0      389bc3fa690240e798340f5a15918d5c  f19421c1d4aa40978ebb69ca19b0e20d   \n",
       "1      d1ede868e29245ea91818a903fec04c6  5a8bc65990b245e5a138643cd4eb9837   \n",
       "2      102e9454054946fda62242d2e176fdce  4d5c57ea9a6940dd891ad53e9dbe8da0   \n",
       "4      be8a5d1981a2458d90b255ddc7e0d174  5a8bc65990b245e5a138643cd4eb9837   \n",
       "5      9fa9ae8f57894cc9a3b8a9bbe0fc1b2f  2906b810c7d4411798c6938adc9daaa5   \n",
       "...                                 ...                               ...   \n",
       "49126  345f9431137a49b3963221a55567c4a8  0b1e1539f2cc45b7b9fa7c272da2e1d7   \n",
       "49127  5a3f7fcb450a4cb693ab5267b4d49b40  5a8bc65990b245e5a138643cd4eb9837   \n",
       "49129  ec1761735b034ae8874bc979f0cd5862  5a8bc65990b245e5a138643cd4eb9837   \n",
       "49132  84fb57a7fe8045a8bf6236738ee73a0f  5a8bc65990b245e5a138643cd4eb9837   \n",
       "49133  abc4359eb34e4e2ca2349da2ddf771b6  3f207df678b143eea3cee63160fa8bed   \n",
       "\n",
       "       response   age  membership_days    income   Age    F    M    O  \n",
       "0             1  65.0            837.0   53000.0  60's  0.0  1.0  0.0  \n",
       "1             0  53.0            983.0   52000.0  50's  0.0  0.0  1.0  \n",
       "2             1  69.0           1381.0   57000.0  60's  1.0  0.0  0.0  \n",
       "4             0  39.0           2191.0   51000.0  30's  0.0  1.0  0.0  \n",
       "5             1  42.0           1591.0   96000.0  40's  0.0  1.0  0.0  \n",
       "...         ...   ...              ...       ...   ...  ...  ...  ...  \n",
       "49126         1  53.0           1147.0   77000.0  50's  0.0  1.0  0.0  \n",
       "49127         0  59.0            951.0  102000.0  50's  1.0  0.0  0.0  \n",
       "49129         0  56.0           1391.0  103000.0  50's  1.0  0.0  0.0  \n",
       "49132         0  64.0           1076.0  113000.0  60's  1.0  0.0  0.0  \n",
       "49133         0  51.0           2122.0   68000.0  50's  0.0  1.0  0.0  \n",
       "\n",
       "[38129 rows x 10 columns]"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df[(final_df.offer_id != '3f207df678b143eea3cee63160fa8bed') & (final_df.offer_id != '5a8bc65990b245e5a138643cd4eb9837')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "offer_dummies =pd.get_dummies(portfolio_df['offer_type'])\n",
    "portfolio_df = pd.concat([portfolio_df,offer_dummies],axis=1)\n",
    "portfolio_df = portfolio_df.drop(columns=['offer_type'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>duration</th>\n",
       "      <th>offer_id</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
       "      <th>informational</th>\n",
       "      <th>email</th>\n",
       "      <th>mobile</th>\n",
       "      <th>social</th>\n",
       "      <th>web</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>ae264e3637204a6fb9bb56bc8210ddfd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4d5c57ea9a6940dd891ad53e9dbe8da0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3f207df678b143eea3cee63160fa8bed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>5</td>\n",
       "      <td>7</td>\n",
       "      <td>9b98b8c7a33c4b65b9aebfe6a799e6d9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>0b1e1539f2cc45b7b9fa7c272da2e1d7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2298d6c36e964ae4a3e7e9706d1fb8c2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>fafdcd668e3743c1bb461111dcafc2a4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5a8bc65990b245e5a138643cd4eb9837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>f19421c1d4aa40978ebb69ca19b0e20d</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>2906b810c7d4411798c6938adc9daaa5</td>\n",
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       "      <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": [
       "   reward  difficulty  duration                          offer_id  bogo  \\\n",
       "0      10          10         7  ae264e3637204a6fb9bb56bc8210ddfd     1   \n",
       "1      10          10         5  4d5c57ea9a6940dd891ad53e9dbe8da0     1   \n",
       "2       0           0         4  3f207df678b143eea3cee63160fa8bed     0   \n",
       "3       5           5         7  9b98b8c7a33c4b65b9aebfe6a799e6d9     1   \n",
       "4       5          20        10  0b1e1539f2cc45b7b9fa7c272da2e1d7     0   \n",
       "5       3           7         7  2298d6c36e964ae4a3e7e9706d1fb8c2     0   \n",
       "6       2          10        10  fafdcd668e3743c1bb461111dcafc2a4     0   \n",
       "7       0           0         3  5a8bc65990b245e5a138643cd4eb9837     0   \n",
       "8       5           5         5  f19421c1d4aa40978ebb69ca19b0e20d     1   \n",
       "9       2          10         7  2906b810c7d4411798c6938adc9daaa5     0   \n",
       "\n",
       "   discount  informational  email  mobile  social  web  \n",
       "0         0              0      1       1       1    0  \n",
       "1         0              0      1       1       1    1  \n",
       "2         0              1      1       1       0    1  \n",
       "3         0              0      1       1       0    1  \n",
       "4         1              0      1       0       0    1  \n",
       "5         1              0      1       1       1    1  \n",
       "6         1              0      1       1       1    1  \n",
       "7         0              1      1       1       1    0  \n",
       "8         0              0      1       1       1    1  \n",
       "9         1              0      1       1       0    1  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Encoding channel featurees\"\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "encoder = MultiLabelBinarizer()\n",
    "\n",
    "cat_encoded = encoder.fit(portfolio['channels'])\n",
    "channels_df = pd.DataFrame(cat_encoded.transform(portfolio['channels']),columns=cat_encoded.classes_)\n",
    "portfolio_df = portfolio_df.drop(columns=['channels'],axis=1)\n",
    "portfolio_df = portfolio_df.rename(columns={'id':'offer_id'})\n",
    "portfolio_df = pd.concat([portfolio_df,channels_df],axis=1)\n",
    "portfolio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [],
   "source": [
    "portfolio_df = portfolio_df.drop(['reward','difficulty','duration','informational'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_id</th>\n",
       "      <th>bogo</th>\n",
       "      <th>discount</th>\n",
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       "      <th>mobile</th>\n",
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       "      <th>8</th>\n",
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      "text/plain": [
       "                           offer_id  bogo  discount  email  mobile  social  \\\n",
       "0  ae264e3637204a6fb9bb56bc8210ddfd     1         0      1       1       1   \n",
       "1  4d5c57ea9a6940dd891ad53e9dbe8da0     1         0      1       1       1   \n",
       "2  3f207df678b143eea3cee63160fa8bed     0         0      1       1       0   \n",
       "3  9b98b8c7a33c4b65b9aebfe6a799e6d9     1         0      1       1       0   \n",
       "4  0b1e1539f2cc45b7b9fa7c272da2e1d7     0         1      1       0       0   \n",
       "5  2298d6c36e964ae4a3e7e9706d1fb8c2     0         1      1       1       1   \n",
       "6  fafdcd668e3743c1bb461111dcafc2a4     0         1      1       1       1   \n",
       "7  5a8bc65990b245e5a138643cd4eb9837     0         0      1       1       1   \n",
       "8  f19421c1d4aa40978ebb69ca19b0e20d     1         0      1       1       1   \n",
       "9  2906b810c7d4411798c6938adc9daaa5     0         1      1       1       0   \n",
       "\n",
       "   web  \n",
       "0    0  \n",
       "1    1  \n",
       "2    1  \n",
       "3    1  \n",
       "4    1  \n",
       "5    1  \n",
       "6    1  \n",
       "7    0  \n",
       "8    1  \n",
       "9    1  "
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = final_df.merge(portfolio_df,how='left', on='offer_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['offer_id'] = final_df['offer_id'].map({'f19421c1d4aa40978ebb69ca19b0e20d':'offer_1',\n",
    "                          '5a8bc65990b245e5a138643cd4eb9837':'offer_2',\n",
    "                          '4d5c57ea9a6940dd891ad53e9dbe8da0':'offer_3',\n",
    "                          '2906b810c7d4411798c6938adc9daaa5':'offer_4',\n",
    "                          '2298d6c36e964ae4a3e7e9706d1fb8c2':'offer_5',\n",
    "                          'ae264e3637204a6fb9bb56bc8210ddfd':'offer_6',\n",
    "                          '9b98b8c7a33c4b65b9aebfe6a799e6d9':'offer_7',\n",
    "                          'fafdcd668e3743c1bb461111dcafc2a4':'offer_8',\n",
    "                          '3f207df678b143eea3cee63160fa8bed':'offer_9',\n",
    "                          '0b1e1539f2cc45b7b9fa7c272da2e1d7':'offer_10'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['offer_1', 'offer_3', 'offer_4', 'offer_5', 'offer_6', 'offer_7',\n",
       "       'offer_8', 'offer_10'], dtype=object)"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['offer_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "offer_dummies =pd.get_dummies(final_df['offer_id'])\n",
    "final_df = pd.concat([final_df,offer_dummies],axis=1)\n",
    "final_df.drop(['offer_id','person','Age'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>42.0</td>\n",
       "      <td>1591.0</td>\n",
       "      <td>96000.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>36000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30921</th>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>59000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30922</th>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>893.0</td>\n",
       "      <td>113000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30923</th>\n",
       "      <td>0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>823.0</td>\n",
       "      <td>43000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30924</th>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1485.0</td>\n",
       "      <td>110000.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30925</th>\n",
       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1147.0</td>\n",
       "      <td>77000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30926 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       response   age  membership_days    income    F    M    O  bogo  \\\n",
       "0             1  65.0            837.0   53000.0  0.0  1.0  0.0     1   \n",
       "1             1  69.0           1381.0   57000.0  1.0  0.0  0.0     1   \n",
       "2             1  42.0           1591.0   96000.0  0.0  1.0  0.0     0   \n",
       "3             1  53.0           1420.0   72000.0  1.0  0.0  0.0     0   \n",
       "4             0  46.0            697.0   36000.0  1.0  0.0  0.0     0   \n",
       "...         ...   ...              ...       ...  ...  ...  ...   ...   \n",
       "30921         0  68.0           1645.0   59000.0  1.0  0.0  0.0     0   \n",
       "30922         1  55.0            893.0  113000.0  1.0  0.0  0.0     1   \n",
       "30923         0  51.0            823.0   43000.0  0.0  1.0  0.0     1   \n",
       "30924         1  58.0           1485.0  110000.0  0.0  1.0  0.0     0   \n",
       "30925         1  53.0           1147.0   77000.0  0.0  1.0  0.0     0   \n",
       "\n",
       "       discount  email  ...  social  web  offer_1  offer_10  offer_3  offer_4  \\\n",
       "0             0      1  ...       1    1        1         0        0        0   \n",
       "1             0      1  ...       1    1        0         0        1        0   \n",
       "2             1      1  ...       0    1        0         0        0        1   \n",
       "3             1      1  ...       1    1        0         0        0        0   \n",
       "4             1      1  ...       1    1        0         0        0        0   \n",
       "...         ...    ...  ...     ...  ...      ...       ...      ...      ...   \n",
       "30921         1      1  ...       1    1        0         0        0        0   \n",
       "30922         0      1  ...       1    1        1         0        0        0   \n",
       "30923         0      1  ...       1    1        0         0        1        0   \n",
       "30924         1      1  ...       0    1        0         0        0        1   \n",
       "30925         1      1  ...       0    1        0         1        0        0   \n",
       "\n",
       "       offer_5  offer_6  offer_7  offer_8  \n",
       "0            0        0        0        0  \n",
       "1            0        0        0        0  \n",
       "2            0        0        0        0  \n",
       "3            1        0        0        0  \n",
       "4            1        0        0        0  \n",
       "...        ...      ...      ...      ...  \n",
       "30921        1        0        0        0  \n",
       "30922        0        0        0        0  \n",
       "30923        0        0        0        0  \n",
       "30924        0        0        0        0  \n",
       "30925        0        0        0        0  \n",
       "\n",
       "[30926 rows x 21 columns]"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "norm = MinMaxScaler()\n",
    "\n",
    "income_scal = norm.fit_transform(final_df[['income']])\n",
    "member_scal = norm.fit_transform(final_df[['membership_days']])\n",
    "age = norm.fit_transform(final_df[['age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df['income'] = income_scal\n",
    "final_df['membership_days'] = member_scal\n",
    "final_df['age'] = age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.drop('email',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
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       "            background-color:  #f6bea4;\n",
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       "            background-color:  #b1cbfc;\n",
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       "            background-color:  #e6d7cf;\n",
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       "            background-color:  #3b4cc0;\n",
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       "            background-color:  #ecd3c5;\n",
       "            color:  #000000;\n",
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       "            background-color:  #7699f6;\n",
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       "            background-color:  #d3dbe7;\n",
       "            color:  #000000;\n",
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       "            background-color:  #7699f6;\n",
       "            color:  #000000;\n",
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       "            background-color:  #9dbdff;\n",
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       "            background-color:  #84a7fc;\n",
       "            color:  #000000;\n",
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       "            background-color:  #cbd8ee;\n",
       "            color:  #000000;\n",
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       "            background-color:  #b40426;\n",
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       "            background-color:  #85a8fc;\n",
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       "            background-color:  #5f7fe8;\n",
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       "            background-color:  #3f53c6;\n",
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       "            background-color:  #6c8ff1;\n",
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       "            background-color:  #dadce0;\n",
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       "            background-color:  #dcdddd;\n",
       "            color:  #000000;\n",
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       "            background-color:  #5e7de7;\n",
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       "            background-color:  #92b4fe;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f59f80;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ead5c9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #e0dbd8;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f0cdbb;\n",
       "            color:  #000000;\n",
       "        }    #T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col12 {\n",
       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
       "        }    #T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col13 {\n",
       "            background-color:  #cedaeb;\n",
       "            color:  #000000;\n",
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       "            background-color:  #6a8bef;\n",
       "            color:  #000000;\n",
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       "            background-color:  #94b6ff;\n",
       "            color:  #000000;\n",
       "        }    #T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col16 {\n",
       "            background-color:  #779af7;\n",
       "            color:  #000000;\n",
       "        }    #T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col17 {\n",
       "            background-color:  #c4d5f3;\n",
       "            color:  #000000;\n",
       "        }    #T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col18 {\n",
       "            background-color:  #96b7ff;\n",
       "            color:  #000000;\n",
       "        }    #T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col19 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >response</th>        <th class=\"col_heading level0 col1\" >age</th>        <th class=\"col_heading level0 col2\" >membership_days</th>        <th class=\"col_heading level0 col3\" >income</th>        <th class=\"col_heading level0 col4\" >F</th>        <th class=\"col_heading level0 col5\" >M</th>        <th class=\"col_heading level0 col6\" >O</th>        <th class=\"col_heading level0 col7\" >bogo</th>        <th class=\"col_heading level0 col8\" >discount</th>        <th class=\"col_heading level0 col9\" >mobile</th>        <th class=\"col_heading level0 col10\" >social</th>        <th class=\"col_heading level0 col11\" >web</th>        <th class=\"col_heading level0 col12\" >offer_1</th>        <th class=\"col_heading level0 col13\" >offer_10</th>        <th class=\"col_heading level0 col14\" >offer_3</th>        <th class=\"col_heading level0 col15\" >offer_4</th>        <th class=\"col_heading level0 col16\" >offer_5</th>        <th class=\"col_heading level0 col17\" >offer_6</th>        <th class=\"col_heading level0 col18\" >offer_7</th>        <th class=\"col_heading level0 col19\" >offer_8</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row0\" class=\"row_heading level0 row0\" >response</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col1\" class=\"data row0 col1\" >0.095173</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col2\" class=\"data row0 col2\" >0.222237</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col3\" class=\"data row0 col3\" >0.214953</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col4\" class=\"data row0 col4\" >0.153331</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col5\" class=\"data row0 col5\" >-0.157958</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col6\" class=\"data row0 col6\" >0.021752</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col7\" class=\"data row0 col7\" >-0.156665</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col8\" class=\"data row0 col8\" >0.156665</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col9\" class=\"data row0 col9\" >-0.022591</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col10\" class=\"data row0 col10\" >-0.091999</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col11\" class=\"data row0 col11\" >0.101371</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col12\" class=\"data row0 col12\" >-0.025570</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col13\" class=\"data row0 col13\" >0.022591</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col14\" class=\"data row0 col14\" >-0.142339</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col15\" class=\"data row0 col15\" >0.054675</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col16\" class=\"data row0 col16\" >0.076517</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col17\" class=\"data row0 col17\" >-0.101371</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col18\" class=\"data row0 col18\" >0.063843</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row0_col19\" class=\"data row0 col19\" >0.081133</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row1\" class=\"row_heading level0 row1\" >age</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col0\" class=\"data row1 col0\" >0.095173</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col2\" class=\"data row1 col2\" >0.011804</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col3\" class=\"data row1 col3\" >0.297050</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col4\" class=\"data row1 col4\" >0.140342</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col5\" class=\"data row1 col5\" >-0.142050</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col6\" class=\"data row1 col6\" >0.009615</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col7\" class=\"data row1 col7\" >-0.016496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col8\" class=\"data row1 col8\" >0.016496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col9\" class=\"data row1 col9\" >-0.029549</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col10\" class=\"data row1 col10\" >-0.044721</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col11\" class=\"data row1 col11\" >0.021483</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col12\" class=\"data row1 col12\" >-0.005516</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col13\" class=\"data row1 col13\" >0.029549</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col14\" class=\"data row1 col14\" >-0.011331</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col15\" class=\"data row1 col15\" >0.023522</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col16\" class=\"data row1 col16\" >-0.008445</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col17\" class=\"data row1 col17\" >-0.021483</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col18\" class=\"data row1 col18\" >0.018949</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row1_col19\" class=\"data row1 col19\" >-0.006347</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row2\" class=\"row_heading level0 row2\" >membership_days</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col0\" class=\"data row2 col0\" >0.222237</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col1\" class=\"data row2 col1\" >0.011804</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col3\" class=\"data row2 col3\" >0.023893</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col4\" class=\"data row2 col4\" >-0.023604</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col5\" class=\"data row2 col5\" >0.025236</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col6\" class=\"data row2 col6\" >-0.007099</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col7\" class=\"data row2 col7\" >0.002640</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col8\" class=\"data row2 col8\" >-0.002640</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col9\" class=\"data row2 col9\" >0.004721</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col10\" class=\"data row2 col10\" >-0.022959</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col11\" class=\"data row2 col11\" >0.000188</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col12\" class=\"data row2 col12\" >-0.007340</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col13\" class=\"data row2 col13\" >-0.004721</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col14\" class=\"data row2 col14\" >-0.002254</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col15\" class=\"data row2 col15\" >0.021163</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col16\" class=\"data row2 col16\" >-0.011555</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col17\" class=\"data row2 col17\" >-0.000188</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col18\" class=\"data row2 col18\" >0.016884</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row2_col19\" class=\"data row2 col19\" >-0.005671</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row3\" class=\"row_heading level0 row3\" >income</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col0\" class=\"data row3 col0\" >0.214953</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col1\" class=\"data row3 col1\" >0.297050</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col2\" class=\"data row3 col2\" >0.023893</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col4\" class=\"data row3 col4\" >0.205966</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col5\" class=\"data row3 col5\" >-0.203794</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col6\" class=\"data row3 col6\" >-0.004954</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col7\" class=\"data row3 col7\" >-0.024681</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col8\" class=\"data row3 col8\" >0.024681</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col9\" class=\"data row3 col9\" >-0.044430</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col10\" class=\"data row3 col10\" >-0.064606</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col11\" class=\"data row3 col11\" >0.034824</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col12\" class=\"data row3 col12\" >-0.005979</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col13\" class=\"data row3 col13\" >0.044430</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col14\" class=\"data row3 col14\" >-0.012614</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col15\" class=\"data row3 col15\" >0.036631</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col16\" class=\"data row3 col16\" >-0.010428</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col17\" class=\"data row3 col17\" >-0.034824</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col18\" class=\"data row3 col18\" >0.023316</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row3_col19\" class=\"data row3 col19\" >-0.012950</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col0\" class=\"data row4 col0\" >0.153331</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col1\" class=\"data row4 col1\" >0.140342</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col2\" class=\"data row4 col2\" >-0.023604</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col3\" class=\"data row4 col3\" >0.205966</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col5\" class=\"data row4 col5\" >-0.969753</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col6\" class=\"data row4 col6\" >-0.104330</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col7\" class=\"data row4 col7\" >-0.008342</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col8\" class=\"data row4 col8\" >0.008342</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col9\" class=\"data row4 col9\" >-0.015569</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col10\" class=\"data row4 col10\" >-0.031316</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col11\" class=\"data row4 col11\" >0.015312</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col12\" class=\"data row4 col12\" >-0.003655</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col13\" class=\"data row4 col13\" >0.015569</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col14\" class=\"data row4 col14\" >-0.006710</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col15\" class=\"data row4 col15\" >0.016531</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col16\" class=\"data row4 col16\" >-0.004684</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col17\" class=\"data row4 col17\" >-0.015312</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col18\" class=\"data row4 col18\" >0.017383</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row4_col19\" class=\"data row4 col19\" >-0.006825</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row5\" class=\"row_heading level0 row5\" >M</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col0\" class=\"data row5 col0\" >-0.157958</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col1\" class=\"data row5 col1\" >-0.142050</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col2\" class=\"data row5 col2\" >0.025236</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col3\" class=\"data row5 col3\" >-0.203794</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col4\" class=\"data row5 col4\" >-0.969753</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col6\" class=\"data row5 col6\" >-0.141584</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col7\" class=\"data row5 col7\" >0.009094</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col8\" class=\"data row5 col8\" >-0.009094</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col9\" class=\"data row5 col9\" >0.018372</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col10\" class=\"data row5 col10\" >0.037197</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col11\" class=\"data row5 col11\" >-0.016036</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col12\" class=\"data row5 col12\" >0.004240</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col13\" class=\"data row5 col13\" >-0.018372</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col14\" class=\"data row5 col14\" >0.009703</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col15\" class=\"data row5 col15\" >-0.018898</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col16\" class=\"data row5 col16\" >0.005344</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col17\" class=\"data row5 col17\" >0.016036</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col18\" class=\"data row5 col18\" >-0.021481</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row5_col19\" class=\"data row5 col19\" >0.008785</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row6\" class=\"row_heading level0 row6\" >O</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col0\" class=\"data row6 col0\" >0.021752</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col1\" class=\"data row6 col1\" >0.009615</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col2\" class=\"data row6 col2\" >-0.007099</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col3\" class=\"data row6 col3\" >-0.004954</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col4\" class=\"data row6 col4\" >-0.104330</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col5\" class=\"data row6 col5\" >-0.141584</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col7\" class=\"data row6 col7\" >-0.003224</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col8\" class=\"data row6 col8\" >0.003224</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col9\" class=\"data row6 col9\" >-0.011715</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col10\" class=\"data row6 col10\" >-0.024556</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col11\" class=\"data row6 col11\" >0.003241</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col12\" class=\"data row6 col12\" >-0.002453</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col13\" class=\"data row6 col13\" >0.011715</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col14\" class=\"data row6 col14\" >-0.012321</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col15\" class=\"data row6 col15\" >0.009958</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col16\" class=\"data row6 col16\" >-0.002778</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col17\" class=\"data row6 col17\" >-0.003241</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col18\" class=\"data row6 col18\" >0.017025</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row6_col19\" class=\"data row6 col19\" >-0.008113</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row7\" class=\"row_heading level0 row7\" >bogo</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col0\" class=\"data row7 col0\" >-0.156665</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col1\" class=\"data row7 col1\" >-0.016496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col2\" class=\"data row7 col2\" >0.002640</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col3\" class=\"data row7 col3\" >-0.024681</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col4\" class=\"data row7 col4\" >-0.008342</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col5\" class=\"data row7 col5\" >0.009094</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col6\" class=\"data row7 col6\" >-0.003224</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col8\" class=\"data row7 col8\" >-1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col9\" class=\"data row7 col9\" >0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col10\" class=\"data row7 col10\" >0.180053</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col11\" class=\"data row7 col11\" >-0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col12\" class=\"data row7 col12\" >0.389222</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col13\" class=\"data row7 col13\" >-0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col14\" class=\"data row7 col14\" >0.393910</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col15\" class=\"data row7 col15\" >-0.339532</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col16\" class=\"data row7 col16\" >-0.464459</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col17\" class=\"data row7 col17\" >0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col18\" class=\"data row7 col18\" >0.289405</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row7_col19\" class=\"data row7 col19\" >-0.469560</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row8\" class=\"row_heading level0 row8\" >discount</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col0\" class=\"data row8 col0\" >0.156665</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col1\" class=\"data row8 col1\" >0.016496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col2\" class=\"data row8 col2\" >-0.002640</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col3\" class=\"data row8 col3\" >0.024681</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col4\" class=\"data row8 col4\" >0.008342</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col5\" class=\"data row8 col5\" >-0.009094</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col6\" class=\"data row8 col6\" >0.003224</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col7\" class=\"data row8 col7\" >-1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col9\" class=\"data row8 col9\" >-0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col10\" class=\"data row8 col10\" >-0.180053</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col11\" class=\"data row8 col11\" >0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col12\" class=\"data row8 col12\" >-0.389222</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col13\" class=\"data row8 col13\" >0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col14\" class=\"data row8 col14\" >-0.393910</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col15\" class=\"data row8 col15\" >0.339532</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col16\" class=\"data row8 col16\" >0.464459</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col17\" class=\"data row8 col17\" >-0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col18\" class=\"data row8 col18\" >-0.289405</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row8_col19\" class=\"data row8 col19\" >0.469560</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row9\" class=\"row_heading level0 row9\" >mobile</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col0\" class=\"data row9 col0\" >-0.022591</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col1\" class=\"data row9 col1\" >-0.029549</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col2\" class=\"data row9 col2\" >0.004721</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col3\" class=\"data row9 col3\" >-0.044430</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col4\" class=\"data row9 col4\" >-0.015569</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col5\" class=\"data row9 col5\" >0.018372</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col6\" class=\"data row9 col6\" >-0.011715</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col7\" class=\"data row9 col7\" >0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col8\" class=\"data row9 col8\" >-0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col10\" class=\"data row9 col10\" >0.444212</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col11\" class=\"data row9 col11\" >-0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col12\" class=\"data row9 col12\" >0.104496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col13\" class=\"data row9 col13\" >-1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col14\" class=\"data row9 col14\" >0.105754</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col15\" class=\"data row9 col15\" >0.077328</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col16\" class=\"data row9 col16\" >0.105780</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col17\" class=\"data row9 col17\" >0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col18\" class=\"data row9 col18\" >0.077698</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row9_col19\" class=\"data row9 col19\" >0.106942</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row10\" class=\"row_heading level0 row10\" >social</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col0\" class=\"data row10 col0\" >-0.091999</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col1\" class=\"data row10 col1\" >-0.044721</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col2\" class=\"data row10 col2\" >-0.022959</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col3\" class=\"data row10 col3\" >-0.064606</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col4\" class=\"data row10 col4\" >-0.031316</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col5\" class=\"data row10 col5\" >0.037197</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col6\" class=\"data row10 col6\" >-0.024556</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col7\" class=\"data row10 col7\" >0.180053</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col8\" class=\"data row10 col8\" >-0.180053</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col9\" class=\"data row10 col9\" >0.444212</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col11\" class=\"data row10 col11\" >-0.229262</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col12\" class=\"data row10 col12\" >0.235238</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col13\" class=\"data row10 col13\" >-0.444212</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col14\" class=\"data row10 col14\" >0.238072</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col15\" class=\"data row10 col15\" >-0.561786</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col16\" class=\"data row10 col16\" >0.238131</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col17\" class=\"data row10 col17\" >0.229262</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col18\" class=\"data row10 col18\" >-0.564468</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row10_col19\" class=\"data row10 col19\" >0.240746</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row11\" class=\"row_heading level0 row11\" >web</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col0\" class=\"data row11 col0\" >0.101371</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col1\" class=\"data row11 col1\" >0.021483</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col2\" class=\"data row11 col2\" >0.000188</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col3\" class=\"data row11 col3\" >0.034824</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col4\" class=\"data row11 col4\" >0.015312</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col5\" class=\"data row11 col5\" >-0.016036</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col6\" class=\"data row11 col6\" >0.003241</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col7\" class=\"data row11 col7\" >-0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col8\" class=\"data row11 col8\" >0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col9\" class=\"data row11 col9\" >-0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col10\" class=\"data row11 col10\" >-0.229262</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col12\" class=\"data row11 col12\" >0.174045</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col13\" class=\"data row11 col13\" >0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col14\" class=\"data row11 col14\" >0.176141</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col15\" class=\"data row11 col15\" >0.128796</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col16\" class=\"data row11 col16\" >0.176185</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col17\" class=\"data row11 col17\" >-1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col18\" class=\"data row11 col18\" >0.129411</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row11_col19\" class=\"data row11 col19\" >0.178120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row12\" class=\"row_heading level0 row12\" >offer_1</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col0\" class=\"data row12 col0\" >-0.025570</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col1\" class=\"data row12 col1\" >-0.005516</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col2\" class=\"data row12 col2\" >-0.007340</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col3\" class=\"data row12 col3\" >-0.005979</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col4\" class=\"data row12 col4\" >-0.003655</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col5\" class=\"data row12 col5\" >0.004240</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col6\" class=\"data row12 col6\" >-0.002453</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col7\" class=\"data row12 col7\" >0.389222</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col8\" class=\"data row12 col8\" >-0.389222</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col9\" class=\"data row12 col9\" >0.104496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col10\" class=\"data row12 col10\" >0.235238</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col11\" class=\"data row12 col11\" >0.174045</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col13\" class=\"data row12 col13\" >-0.104496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col14\" class=\"data row12 col14\" >-0.180733</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col15\" class=\"data row12 col15\" >-0.132154</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col16\" class=\"data row12 col16\" >-0.180778</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col17\" class=\"data row12 col17\" >-0.174045</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col18\" class=\"data row12 col18\" >-0.132785</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row12_col19\" class=\"data row12 col19\" >-0.182763</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row13\" class=\"row_heading level0 row13\" >offer_10</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col0\" class=\"data row13 col0\" >0.022591</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col1\" class=\"data row13 col1\" >0.029549</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col2\" class=\"data row13 col2\" >-0.004721</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col3\" class=\"data row13 col3\" >0.044430</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col4\" class=\"data row13 col4\" >0.015569</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col5\" class=\"data row13 col5\" >-0.018372</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col6\" class=\"data row13 col6\" >0.011715</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col7\" class=\"data row13 col7\" >-0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col8\" class=\"data row13 col8\" >0.268473</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col9\" class=\"data row13 col9\" >-1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col10\" class=\"data row13 col10\" >-0.444212</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col11\" class=\"data row13 col11\" >0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col12\" class=\"data row13 col12\" >-0.104496</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col14\" class=\"data row13 col14\" >-0.105754</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col15\" class=\"data row13 col15\" >-0.077328</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col16\" class=\"data row13 col16\" >-0.105780</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col17\" class=\"data row13 col17\" >-0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col18\" class=\"data row13 col18\" >-0.077698</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row13_col19\" class=\"data row13 col19\" >-0.106942</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row14\" class=\"row_heading level0 row14\" >offer_3</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col0\" class=\"data row14 col0\" >-0.142339</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col1\" class=\"data row14 col1\" >-0.011331</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col2\" class=\"data row14 col2\" >-0.002254</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col3\" class=\"data row14 col3\" >-0.012614</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col4\" class=\"data row14 col4\" >-0.006710</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col5\" class=\"data row14 col5\" >0.009703</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col6\" class=\"data row14 col6\" >-0.012321</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col7\" class=\"data row14 col7\" >0.393910</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col8\" class=\"data row14 col8\" >-0.393910</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col9\" class=\"data row14 col9\" >0.105754</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col10\" class=\"data row14 col10\" >0.238072</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col11\" class=\"data row14 col11\" >0.176141</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col12\" class=\"data row14 col12\" >-0.180733</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col13\" class=\"data row14 col13\" >-0.105754</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col15\" class=\"data row14 col15\" >-0.133745</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col16\" class=\"data row14 col16\" >-0.182955</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col17\" class=\"data row14 col17\" >-0.176141</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col18\" class=\"data row14 col18\" >-0.134384</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row14_col19\" class=\"data row14 col19\" >-0.184964</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row15\" class=\"row_heading level0 row15\" >offer_4</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col0\" class=\"data row15 col0\" >0.054675</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col1\" class=\"data row15 col1\" >0.023522</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col2\" class=\"data row15 col2\" >0.021163</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col3\" class=\"data row15 col3\" >0.036631</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col4\" class=\"data row15 col4\" >0.016531</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col5\" class=\"data row15 col5\" >-0.018898</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col6\" class=\"data row15 col6\" >0.009958</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col7\" class=\"data row15 col7\" >-0.339532</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col8\" class=\"data row15 col8\" >0.339532</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col9\" class=\"data row15 col9\" >0.077328</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col10\" class=\"data row15 col10\" >-0.561786</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col11\" class=\"data row15 col11\" >0.128796</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col12\" class=\"data row15 col12\" >-0.132154</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col13\" class=\"data row15 col13\" >-0.077328</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col14\" class=\"data row15 col14\" >-0.133745</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col16\" class=\"data row15 col16\" >-0.133778</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col17\" class=\"data row15 col17\" >-0.128796</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col18\" class=\"data row15 col18\" >-0.098263</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row15_col19\" class=\"data row15 col19\" >-0.135247</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row16\" class=\"row_heading level0 row16\" >offer_5</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col0\" class=\"data row16 col0\" >0.076517</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col1\" class=\"data row16 col1\" >-0.008445</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col2\" class=\"data row16 col2\" >-0.011555</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col3\" class=\"data row16 col3\" >-0.010428</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col4\" class=\"data row16 col4\" >-0.004684</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col5\" class=\"data row16 col5\" >0.005344</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col6\" class=\"data row16 col6\" >-0.002778</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col7\" class=\"data row16 col7\" >-0.464459</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col8\" class=\"data row16 col8\" >0.464459</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col9\" class=\"data row16 col9\" >0.105780</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col10\" class=\"data row16 col10\" >0.238131</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col11\" class=\"data row16 col11\" >0.176185</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col12\" class=\"data row16 col12\" >-0.180778</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col13\" class=\"data row16 col13\" >-0.105780</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col14\" class=\"data row16 col14\" >-0.182955</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col15\" class=\"data row16 col15\" >-0.133778</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col17\" class=\"data row16 col17\" >-0.176185</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col18\" class=\"data row16 col18\" >-0.134417</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row16_col19\" class=\"data row16 col19\" >-0.185010</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row17\" class=\"row_heading level0 row17\" >offer_6</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col0\" class=\"data row17 col0\" >-0.101371</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col1\" class=\"data row17 col1\" >-0.021483</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col2\" class=\"data row17 col2\" >-0.000188</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col3\" class=\"data row17 col3\" >-0.034824</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col4\" class=\"data row17 col4\" >-0.015312</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col5\" class=\"data row17 col5\" >0.016036</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col6\" class=\"data row17 col6\" >-0.003241</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col7\" class=\"data row17 col7\" >0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col8\" class=\"data row17 col8\" >-0.379333</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col9\" class=\"data row17 col9\" >0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col10\" class=\"data row17 col10\" >0.229262</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col11\" class=\"data row17 col11\" >-1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col12\" class=\"data row17 col12\" >-0.174045</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col13\" class=\"data row17 col13\" >-0.101841</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col14\" class=\"data row17 col14\" >-0.176141</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col15\" class=\"data row17 col15\" >-0.128796</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col16\" class=\"data row17 col16\" >-0.176185</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col18\" class=\"data row17 col18\" >-0.129411</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row17_col19\" class=\"data row17 col19\" >-0.178120</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row18\" class=\"row_heading level0 row18\" >offer_7</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col0\" class=\"data row18 col0\" >0.063843</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col1\" class=\"data row18 col1\" >0.018949</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col2\" class=\"data row18 col2\" >0.016884</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col3\" class=\"data row18 col3\" >0.023316</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col4\" class=\"data row18 col4\" >0.017383</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col5\" class=\"data row18 col5\" >-0.021481</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col6\" class=\"data row18 col6\" >0.017025</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col7\" class=\"data row18 col7\" >0.289405</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col8\" class=\"data row18 col8\" >-0.289405</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col9\" class=\"data row18 col9\" >0.077698</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col10\" class=\"data row18 col10\" >-0.564468</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col11\" class=\"data row18 col11\" >0.129411</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col12\" class=\"data row18 col12\" >-0.132785</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col13\" class=\"data row18 col13\" >-0.077698</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col14\" class=\"data row18 col14\" >-0.134384</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col15\" class=\"data row18 col15\" >-0.098263</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col16\" class=\"data row18 col16\" >-0.134417</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col17\" class=\"data row18 col17\" >-0.129411</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row18_col19\" class=\"data row18 col19\" >-0.135893</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39level0_row19\" class=\"row_heading level0 row19\" >offer_8</th>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col0\" class=\"data row19 col0\" >0.081133</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col1\" class=\"data row19 col1\" >-0.006347</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col2\" class=\"data row19 col2\" >-0.005671</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col3\" class=\"data row19 col3\" >-0.012950</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col4\" class=\"data row19 col4\" >-0.006825</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col5\" class=\"data row19 col5\" >0.008785</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col6\" class=\"data row19 col6\" >-0.008113</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col7\" class=\"data row19 col7\" >-0.469560</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col8\" class=\"data row19 col8\" >0.469560</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col9\" class=\"data row19 col9\" >0.106942</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col10\" class=\"data row19 col10\" >0.240746</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col11\" class=\"data row19 col11\" >0.178120</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col12\" class=\"data row19 col12\" >-0.182763</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col13\" class=\"data row19 col13\" >-0.106942</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col14\" class=\"data row19 col14\" >-0.184964</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col15\" class=\"data row19 col15\" >-0.135247</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col16\" class=\"data row19 col16\" >-0.185010</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col17\" class=\"data row19 col17\" >-0.178120</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col18\" class=\"data row19 col18\" >-0.135893</td>\n",
       "                        <td id=\"T_175b47f0_9f78_11ea_b781_507b9d8a1f39row19_col19\" class=\"data row19 col19\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29e46a13470>"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = final_df.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv('dataset.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df_x = final_df.iloc[:,1:]\n",
    "final_df_y = final_df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Specs       Score\n",
      "13          offer_3  529.687654\n",
      "3                 F  424.055021\n",
      "7          discount  410.668433\n",
      "6              bogo  348.376030\n",
      "4                 M  333.165195\n",
      "1   membership_days  279.556458\n",
      "16          offer_6  271.711472\n",
      "2            income  198.202269\n",
      "18          offer_8  171.497334\n",
      "15          offer_5  153.055678\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "bestfeatures = SelectKBest(score_func=chi2, k=10)\n",
    "fit = bestfeatures.fit(final_df_x,final_df_y)\n",
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(final_df_x.columns)\n",
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Specs','Score']\n",
    "print(featureScores.nlargest(10,'Score'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(final_df_x,final_df_y, test_size=0.1, random_state=0)\n",
    "clf = GaussianNB()\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      0.60      0.52       967\n",
      "           1       0.79      0.67      0.73      2126\n",
      "\n",
      "   micro avg       0.65      0.65      0.65      3093\n",
      "   macro avg       0.62      0.64      0.62      3093\n",
      "weighted avg       0.68      0.65      0.66      3093\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['response', 'age', 'membership_days', 'income', 'F', 'M', 'O', 'bogo',\n",
       "       'discount', 'mobile', 'social', 'web', 'offer_1', 'offer_10', 'offer_3',\n",
       "       'offer_4', 'offer_5', 'offer_6', 'offer_7', 'offer_8'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "img = plt.matshow(cm, cmap=plt.cm.autumn)\n",
    "plt.colorbar(img, fraction=0.045)\n",
    "for x in range(cm.shape[0]):\n",
    "    for y in range(cm.shape[1]):\n",
    "        plt.text(x,y, \"%0.2f\" %cm[x,y], size=12, color='black', ha='center', va='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6356451517188577"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "log_clf = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "log_clf.fit(X_train, y_train)\n",
    "y_pred = log_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)\n",
    "img = plt.matshow(cm, cmap=plt.cm.autumn)\n",
    "plt.colorbar(img, fraction=0.045)\n",
    "for x in range(cm.shape[0]):\n",
    "    for y in range(cm.shape[1]):\n",
    "        plt.text(x,y, \"%0.2f\" %cm[x,y], size=12, color='black', ha='center', va='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      0.37      0.47       967\n",
      "           1       0.76      0.91      0.83      2126\n",
      "\n",
      "   micro avg       0.74      0.74      0.74      3093\n",
      "   macro avg       0.70      0.64      0.65      3093\n",
      "weighted avg       0.72      0.74      0.72      3093\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6396230838751227"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "model = xgb.XGBClassifier(silent=False, \n",
    "                      scale_pos_weight=1,\n",
    "                      learning_rate=0.01,  \n",
    "                      colsample_bytree = 0.4,\n",
    "                      subsample = 0.8,\n",
    "                      objective='binary:logistic', \n",
    "                      n_estimators=1000, \n",
    "                      reg_alpha = 0.3,\n",
    "                      max_depth=10, \n",
    "                      gamma=10)\n",
    "#model = xgb.XGBClassifier(**xgb_params)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "predictions = [round(value) for value in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.45      0.53       967\n",
      "           1       0.78      0.89      0.83      2126\n",
      "\n",
      "   micro avg       0.75      0.75      0.75      3093\n",
      "   macro avg       0.72      0.67      0.68      3093\n",
      "weighted avg       0.74      0.75      0.74      3093\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,y_pred)\n",
    "img = plt.matshow(cm, cmap=plt.cm.autumn)\n",
    "plt.colorbar(img, fraction=0.045)\n",
    "for x in range(cm.shape[0]):\n",
    "    for y in range(cm.shape[1]):\n",
    "        plt.text(x,y, \"%0.2f\" %cm[x,y], size=12, color='black', ha='center', va='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6689971797443578"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27833, 19)"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install hyperopt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from hyperopt import hp, fmin, tpe, STATUS_OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "class BinaryClassifier(nn.Module):\n",
    "\n",
    "    def __init__(self, input_features, hidden_dim, output_dim):\n",
    "        super(BinaryClassifier, self).__init__()\n",
    "        \n",
    "        self.fc1 = nn.Linear(input_features,hidden_dim)\n",
    "        self.fc2 = nn.Linear(hidden_dim,16)\n",
    "        self.fc3 = nn.Linear(16,output_dim)\n",
    "        #self.drop = nn.Dropout(0.5)\n",
    "        self.sig = nn.Sigmoid()\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.fc1(x))\n",
    "        out = F.relu(self.fc2(out))\n",
    "        #out = self.drop(out)\n",
    "        out = self.fc3(out)\n",
    "        return self.sig(out)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, train_loader, epochs, criterion, optimizer, device):\n",
    "    for epoch in range(1, epochs + 1):\n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        \n",
    "        for batch in train_loader:\n",
    "            \n",
    "            batch_x, batch_y = batch\n",
    "            batch_x = batch_x.to(device)\n",
    "            batch_y = batch_y.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            # get predictions from model\n",
    "            y_pred = model(batch_x)           \n",
    "            # perform backprop\n",
    "            loss = criterion(y_pred, batch_y)\n",
    "            loss.backward()\n",
    "            optimizer.step()           \n",
    "            total_loss += loss.data.item()\n",
    "\n",
    "        print(\"Epoch: {}, Loss: {}\".format(epoch, total_loss / len(train_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.astype(np.float32)\n",
    "X_test = X_test.astype(np.float32)\n",
    "y_train = y_train.astype(np.float32)\n",
    "y_test = y_test.astype(np.float32)\n",
    "X_train = X_train.to_numpy()\n",
    "y_train = y_train.to_numpy()\n",
    "X_test = X_test.to_numpy()\n",
    "y_test = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n",
      "Epoch: 1, Loss: 0.5545066479978891\n",
      "Epoch: 2, Loss: 0.5222563841219606\n",
      "Epoch: 3, Loss: 0.510549224630512\n",
      "Epoch: 4, Loss: 0.5045401756105752\n",
      "Epoch: 5, Loss: 0.5014443823232733\n",
      "Epoch: 6, Loss: 0.4995549771463734\n",
      "Epoch: 7, Loss: 0.49845532543700316\n",
      "Epoch: 8, Loss: 0.4976054076405092\n",
      "Epoch: 9, Loss: 0.49714939766402905\n",
      "Epoch: 10, Loss: 0.4967203777668805\n",
      "Epoch: 11, Loss: 0.4964551339889395\n",
      "Epoch: 12, Loss: 0.49619166452823016\n",
      "Epoch: 13, Loss: 0.49594962184114016\n",
      "Epoch: 14, Loss: 0.4956963921329756\n",
      "Epoch: 15, Loss: 0.4954845595120013\n",
      "Epoch: 16, Loss: 0.49534131497487255\n",
      "Epoch: 17, Loss: 0.4950770553501173\n",
      "Epoch: 18, Loss: 0.49492545034522295\n",
      "Epoch: 19, Loss: 0.494805507653061\n",
      "Epoch: 20, Loss: 0.49452298084894813\n",
      "Epoch: 21, Loss: 0.49433937560039004\n",
      "Epoch: 22, Loss: 0.4942291377947248\n",
      "Epoch: 23, Loss: 0.49404590802973714\n",
      "Epoch: 24, Loss: 0.49384274478445106\n",
      "Epoch: 25, Loss: 0.49379433533069733\n",
      "Epoch: 26, Loss: 0.493662225206693\n",
      "Epoch: 27, Loss: 0.4935516708455552\n",
      "Epoch: 28, Loss: 0.49342430062677667\n",
      "Epoch: 29, Loss: 0.49322427782690387\n",
      "Epoch: 30, Loss: 0.4932503650500171\n",
      "Epoch: 31, Loss: 0.4932115387385604\n",
      "Epoch: 32, Loss: 0.4930759917559295\n",
      "Epoch: 33, Loss: 0.4929187375838044\n",
      "Epoch: 34, Loss: 0.492878575657291\n",
      "Epoch: 35, Loss: 0.4927777758189316\n",
      "Epoch: 36, Loss: 0.49255474190944915\n",
      "Epoch: 37, Loss: 0.49259586388181\n",
      "Epoch: 38, Loss: 0.4924312406401525\n",
      "Epoch: 39, Loss: 0.4923844393385553\n",
      "Epoch: 40, Loss: 0.49224922918211456\n",
      "Epoch: 41, Loss: 0.49218051216040537\n",
      "Epoch: 42, Loss: 0.4920398634055565\n",
      "Epoch: 43, Loss: 0.49186881466329785\n",
      "Epoch: 44, Loss: 0.4919220159022972\n",
      "Epoch: 45, Loss: 0.49184637815616594\n",
      "Epoch: 46, Loss: 0.491745813190937\n",
      "Epoch: 47, Loss: 0.49179280954359594\n",
      "Epoch: 48, Loss: 0.491827676273968\n",
      "Epoch: 49, Loss: 0.49160389363251883\n",
      "Epoch: 50, Loss: 0.4915010111170939\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import DataLoader, Dataset\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = BinaryClassifier(19, 24, 1).to(device)\n",
    "\n",
    "print(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "\n",
    "features_train = torch.from_numpy(X_train)\n",
    "targets_train = torch.from_numpy(y_train)\n",
    "\n",
    "features_Test = torch.from_numpy(X_test)\n",
    "targets_Test = torch.from_numpy(y_test)\n",
    "\n",
    "batch_size = 16\n",
    "#n_iters = 10000\n",
    "#num_epochs = n_iters / (len(features_train) / batch_size)\n",
    "num_epochs = 50\n",
    "\n",
    "\n",
    "train = torch.utils.data.TensorDataset(features_train,targets_train)\n",
    "test = torch.utils.data.TensorDataset(features_Test,targets_Test)\n",
    "\n",
    "train_loader = DataLoader(train, batch_size = batch_size, shuffle = False)\n",
    "test_loader = DataLoader(test, batch_size = batch_size, shuffle = False)\n",
    "\n",
    "\n",
    "for epoch in range(1, num_epochs + 1):\n",
    "        \n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        \n",
    "        for i, batch in enumerate(train_loader):\n",
    "            \n",
    "            batch_x, batch_y = batch\n",
    "            batch_x = batch_x.to(device)\n",
    "            batch_y = batch_y.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            # get predictions from model\n",
    "            y_pred = model(batch_x)           \n",
    "            # perform backprop\n",
    "            loss = criterion(y_pred, batch_y)\n",
    "            loss.backward()\n",
    "            optimizer.step()           \n",
    "            total_loss += loss.data.item()\n",
    "\n",
    "        print(\"Epoch: {}, Loss: {}\".format(epoch, total_loss / len(train_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_fn(input_data, model):\n",
    "    print('Predicting class labels for the input data...')\n",
    "\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    \n",
    "    # Process input_data so that it is ready to be sent to our model.\n",
    "    data = torch.from_numpy(input_data.astype('float32'))\n",
    "    data = data.to(device)\n",
    "\n",
    "    # Put the model into evaluation mode\n",
    "    model.eval()\n",
    "\n",
    "    # Compute the result of applying the model to the input data\n",
    "    # The variable `out_label` should be a rounded value, either 1 or 0\n",
    "    out = model(data)\n",
    "    out_np = out.cpu().detach().numpy()\n",
    "    out_label = out_np.round()\n",
    "    print(out_label)\n",
    "    return out_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting class labels for the input data...\n",
      "[[1.]\n",
      " [1.]\n",
      " [1.]\n",
      " ...\n",
      " [1.]\n",
      " [1.]\n",
      " [0.]]\n"
     ]
    }
   ],
   "source": [
    "predictions = predict_fn(X_test,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.62      0.48      0.54       967\n",
      "         1.0       0.79      0.87      0.83      2126\n",
      "\n",
      "   micro avg       0.75      0.75      0.75      3093\n",
      "   macro avg       0.70      0.67      0.68      3093\n",
      "weighted avg       0.73      0.75      0.74      3093\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,predictions)\n",
    "img = plt.matshow(cm, cmap=plt.cm.autumn)\n",
    "plt.colorbar(img, fraction=0.045)\n",
    "for x in range(cm.shape[0]):\n",
    "    for y in range(cm.shape[1]):\n",
    "        plt.text(x,y, \"%0.2f\" %cm[x,y], size=12, color='black', ha='center', va='center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6732202669271277"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}