Detecting_Customers_Subscription.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DETECTING CUSTOMERS SUBSCRIPTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. DATA IMPORT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "from dateutil import parser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('input/appdata10.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>user</th>\n",
       "      <th>first_open</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>screen_list</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>enrolled_date</th>\n",
       "      <th>liked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>235136</td>\n",
       "      <td>2012-12-27 02:14:51.273</td>\n",
       "      <td>3</td>\n",
       "      <td>02:00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>idscreen,joinscreen,Cycle,product_review,ScanP...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>333588</td>\n",
       "      <td>2012-12-02 01:16:00.905</td>\n",
       "      <td>6</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>24</td>\n",
       "      <td>joinscreen,product_review,product_review2,Scan...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>254414</td>\n",
       "      <td>2013-03-19 19:19:09.157</td>\n",
       "      <td>1</td>\n",
       "      <td>19:00:00</td>\n",
       "      <td>23</td>\n",
       "      <td>Splash,Cycle,Loan</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>234192</td>\n",
       "      <td>2013-07-05 16:08:46.354</td>\n",
       "      <td>4</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>28</td>\n",
       "      <td>product_review,Home,product_review,Loan3,Finan...</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-05 16:11:49.513</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>51549</td>\n",
       "      <td>2013-02-26 18:50:48.661</td>\n",
       "      <td>1</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>31</td>\n",
       "      <td>idscreen,joinscreen,Cycle,Credit3Container,Sca...</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-02-26 18:56:37.841</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user               first_open  dayofweek       hour  age  \\\n",
       "0  235136  2012-12-27 02:14:51.273          3   02:00:00   23   \n",
       "1  333588  2012-12-02 01:16:00.905          6   01:00:00   24   \n",
       "2  254414  2013-03-19 19:19:09.157          1   19:00:00   23   \n",
       "3  234192  2013-07-05 16:08:46.354          4   16:00:00   28   \n",
       "4   51549  2013-02-26 18:50:48.661          1   18:00:00   31   \n",
       "\n",
       "                                         screen_list  numscreens  minigame  \\\n",
       "0  idscreen,joinscreen,Cycle,product_review,ScanP...          15         0   \n",
       "1  joinscreen,product_review,product_review2,Scan...          13         0   \n",
       "2                                  Splash,Cycle,Loan           3         0   \n",
       "3  product_review,Home,product_review,Loan3,Finan...          40         0   \n",
       "4  idscreen,joinscreen,Cycle,Credit3Container,Sca...          32         0   \n",
       "\n",
       "   used_premium_feature  enrolled            enrolled_date  liked  \n",
       "0                     0         0                      NaN      0  \n",
       "1                     0         0                      NaN      0  \n",
       "2                     1         0                      NaN      1  \n",
       "3                     0         1  2013-07-05 16:11:49.513      0  \n",
       "4                     0         1  2013-02-26 18:56:37.841      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>age</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>liked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>186889.729900</td>\n",
       "      <td>3.029860</td>\n",
       "      <td>31.72436</td>\n",
       "      <td>21.095900</td>\n",
       "      <td>0.107820</td>\n",
       "      <td>0.172020</td>\n",
       "      <td>0.621480</td>\n",
       "      <td>0.165000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>107768.520361</td>\n",
       "      <td>2.031997</td>\n",
       "      <td>10.80331</td>\n",
       "      <td>15.728812</td>\n",
       "      <td>0.310156</td>\n",
       "      <td>0.377402</td>\n",
       "      <td>0.485023</td>\n",
       "      <td>0.371184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>93526.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>187193.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>279984.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>373662.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                user     dayofweek          age    numscreens      minigame  \\\n",
       "count   50000.000000  50000.000000  50000.00000  50000.000000  50000.000000   \n",
       "mean   186889.729900      3.029860     31.72436     21.095900      0.107820   \n",
       "std    107768.520361      2.031997     10.80331     15.728812      0.310156   \n",
       "min        13.000000      0.000000     16.00000      1.000000      0.000000   \n",
       "25%     93526.750000      1.000000     24.00000     10.000000      0.000000   \n",
       "50%    187193.500000      3.000000     29.00000     18.000000      0.000000   \n",
       "75%    279984.250000      5.000000     37.00000     28.000000      0.000000   \n",
       "max    373662.000000      6.000000    101.00000    325.000000      1.000000   \n",
       "\n",
       "       used_premium_feature      enrolled         liked  \n",
       "count          50000.000000  50000.000000  50000.000000  \n",
       "mean               0.172020      0.621480      0.165000  \n",
       "std                0.377402      0.485023      0.371184  \n",
       "min                0.000000      0.000000      0.000000  \n",
       "25%                0.000000      0.000000      0.000000  \n",
       "50%                0.000000      1.000000      0.000000  \n",
       "75%                0.000000      1.000000      0.000000  \n",
       "max                1.000000      1.000000      1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. DATA CLEANING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert column type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert 'hour' column from 'str' to 'int' type. By using slice, we extract 1st and 2nd number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['hour'] = dataset['hour'].str.slice(1, 3).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>first_open</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>screen_list</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>enrolled_date</th>\n",
       "      <th>liked</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>42011</td>\n",
       "      <td>367346</td>\n",
       "      <td>2013-02-05 21:06:07.738</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>67</td>\n",
       "      <td>joinscreen,Cycle,product_review,Credit3Contain...</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-02-05 21:25:40.609</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45172</td>\n",
       "      <td>316826</td>\n",
       "      <td>2013-04-30 04:49:34.152</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>idscreen,Cycle,Home,ReferralContainer,Credit3C...</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-04-30 04:51:18.978</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29230</td>\n",
       "      <td>226829</td>\n",
       "      <td>2013-04-05 17:41:51.109</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>idscreen,Cycle,Home,Loan2,product_review,produ...</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-04-05 17:43:37.698</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15746</td>\n",
       "      <td>322333</td>\n",
       "      <td>2013-02-10 19:04:07.206</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>Splash,Home,Loan2,CC1,TransactionList,Alerts,R...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15134</td>\n",
       "      <td>142729</td>\n",
       "      <td>2013-01-29 00:18:28.933</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>joinscreen,Cycle,Credit3Container,ScanPreview,...</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-05-18 18:45:14.071</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user               first_open  dayofweek  hour  age  \\\n",
       "42011  367346  2013-02-05 21:06:07.738          1    21   67   \n",
       "45172  316826  2013-04-30 04:49:34.152          1     4   20   \n",
       "29230  226829  2013-04-05 17:41:51.109          4    17   32   \n",
       "15746  322333  2013-02-10 19:04:07.206          6    19   28   \n",
       "15134  142729  2013-01-29 00:18:28.933          1     0   29   \n",
       "\n",
       "                                             screen_list  numscreens  \\\n",
       "42011  joinscreen,Cycle,product_review,Credit3Contain...          45   \n",
       "45172  idscreen,Cycle,Home,ReferralContainer,Credit3C...          85   \n",
       "29230  idscreen,Cycle,Home,Loan2,product_review,produ...          16   \n",
       "15746  Splash,Home,Loan2,CC1,TransactionList,Alerts,R...          15   \n",
       "15134  joinscreen,Cycle,Credit3Container,ScanPreview,...          14   \n",
       "\n",
       "       minigame  used_premium_feature  enrolled            enrolled_date  \\\n",
       "42011         0                     0         1  2013-02-05 21:25:40.609   \n",
       "45172         1                     0         1  2013-04-30 04:51:18.978   \n",
       "29230         0                     0         1  2013-04-05 17:43:37.698   \n",
       "15746         0                     1         0                      NaN   \n",
       "15134         0                     0         1  2013-05-18 18:45:14.071   \n",
       "\n",
       "       liked  \n",
       "42011      0  \n",
       "45172      0  \n",
       "29230      0  \n",
       "15746      1  \n",
       "15134      1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove unnecessary columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We remove unnecessary columns so that we are left only with numerical columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>liked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dayofweek  hour  age  numscreens  minigame  used_premium_feature  liked\n",
       "0          3     2   23          15         0                     0      0\n",
       "1          6     1   24          13         0                     0      0\n",
       "2          1    19   23           3         0                     1      1\n",
       "3          4    16   28          40         0                     0      0\n",
       "4          1    18   31          32         0                     0      1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2 = dataset.copy().drop(columns = ['user', 'screen_list', 'enrolled_date', 'first_open', 'enrolled'])\n",
    "dataset2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. DATA VISUALIZING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to plot every image on one picture, we are going to use a loop. We are going to hav 7 images so we will plot them in 3 rows and 3 columns. Then we are going to specify bins number by vals variable. We will use 'unique' function to have exactly the same number of bins as the number of unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.suptitle('Histograms of Numerical Columns', fontsize = 20, y = 1.1)\n",
    "\n",
    "for i in range(1, dataset2.shape[1]+1):\n",
    "    plt.subplot(3, 3, i)\n",
    "    f = plt.gca()\n",
    "    f.set_title(dataset2.columns.values[i - 1])\n",
    "    \n",
    "    vals = np.size(dataset2.iloc[:, i - 1].unique())\n",
    "    \n",
    "    plt.hist(dataset2.iloc[:, i - 1], bins = vals)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarks:\n",
    "- day of week has pretty even distribution, no particular day of week seem to have influence on if User install the app\n",
    "- we have a substantial drop around 10 but note that those hours are in UTC format and the data are from US time zone so those hours are in reality in the middle of the night\n",
    "- age seem to have even distribution apart from 2-3 jumps we have around 30, 40 and 52\n",
    "- number of screens seem to be also evenly distributed apart from one outlier\n",
    "- we may observe that not so many people played minigame, similar with using premium feature and liked/ not liked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. CORRELATION WITH RESPONSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use corrwith function which shows the correlation between all the fields in this dataframe with a list that we specify as an argument, in this case that would be 'enrolled':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset2.corrwith(dataset.enrolled).plot.bar(figsize = (20,10),\n",
    "                                            title = 'Correlation with Response Variable',\n",
    "                                            fontsize = 15, rot = 45,\n",
    "                                            grid = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarks:\n",
    "- day of the week seems to be correlated with the response variable meaning that the later the day of the week, the more possible to enroll but this relation is very small\n",
    "- the earlier it is, the smaller the age is, the more likely to enroll\n",
    "- the more screens we saw, the more likely it is to enroll\n",
    "- if we are likely to play a minigame, the more likely it is to enroll\n",
    "- the more we used premium features, the less likely to enroll\n",
    "- liked is not that strongly correlated with response variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. CORRELATION MATRIX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to plot a correlation matrix between every individual field. Let's mask upper triangle of the matrix as it is symmetric:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"white\", font_scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = dataset2.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a mask for the upper triangle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw the heatmap with the mask and correct aspect ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.0, 0.0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(18, 15))\n",
    "f.suptitle(\"Correlation Matrix\", fontsize = 40)\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "ax = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "                square=True, linewidths=.5, cbar_kws={\"shrink\": 0.5});\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remarks:\n",
    "- if colour between specific features is (almost) white, there is very little or no correlation between those features\n",
    "- there is a positive correlation between those who played minigame and those who used premium features\n",
    "- age and number of screens seem to be strongly correlated in a negative sense, i.e. the older the person is, the less number of screens he sees\n",
    "- it doesn't seem there is a linear dependence between the features so we can conclude that the features are independent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering is the process of selecting and transforming variables when creating a predictive model using machine learning or statistical modeling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting date columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check first data types we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user                     int64\n",
       "first_open              object\n",
       "dayofweek                int64\n",
       "hour                     int32\n",
       "age                      int64\n",
       "screen_list             object\n",
       "numscreens               int64\n",
       "minigame                 int64\n",
       "used_premium_feature     int64\n",
       "enrolled                 int64\n",
       "enrolled_date           object\n",
       "liked                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>user</th>\n",
       "      <th>first_open</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>screen_list</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>enrolled_date</th>\n",
       "      <th>liked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>235136</td>\n",
       "      <td>2012-12-27 02:14:51.273</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>idscreen,joinscreen,Cycle,product_review,ScanP...</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>333588</td>\n",
       "      <td>2012-12-02 01:16:00.905</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>joinscreen,product_review,product_review2,Scan...</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>254414</td>\n",
       "      <td>2013-03-19 19:19:09.157</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>Splash,Cycle,Loan</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>234192</td>\n",
       "      <td>2013-07-05 16:08:46.354</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>product_review,Home,product_review,Loan3,Finan...</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-07-05 16:11:49.513</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>51549</td>\n",
       "      <td>2013-02-26 18:50:48.661</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>idscreen,joinscreen,Cycle,Credit3Container,Sca...</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-02-26 18:56:37.841</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user               first_open  dayofweek  hour  age  \\\n",
       "0  235136  2012-12-27 02:14:51.273          3     2   23   \n",
       "1  333588  2012-12-02 01:16:00.905          6     1   24   \n",
       "2  254414  2013-03-19 19:19:09.157          1    19   23   \n",
       "3  234192  2013-07-05 16:08:46.354          4    16   28   \n",
       "4   51549  2013-02-26 18:50:48.661          1    18   31   \n",
       "\n",
       "                                         screen_list  numscreens  minigame  \\\n",
       "0  idscreen,joinscreen,Cycle,product_review,ScanP...          15         0   \n",
       "1  joinscreen,product_review,product_review2,Scan...          13         0   \n",
       "2                                  Splash,Cycle,Loan           3         0   \n",
       "3  product_review,Home,product_review,Loan3,Finan...          40         0   \n",
       "4  idscreen,joinscreen,Cycle,Credit3Container,Sca...          32         0   \n",
       "\n",
       "   used_premium_feature  enrolled            enrolled_date  liked  \n",
       "0                     0         0                      NaN      0  \n",
       "1                     0         0                      NaN      0  \n",
       "2                     1         0                      NaN      1  \n",
       "3                     0         1  2013-07-05 16:11:49.513      0  \n",
       "4                     0         1  2013-02-26 18:56:37.841      1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['first_open'] = [parser.parse(row_data) for row_data in dataset['first_open']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['enrolled_date'] = [parser.parse(row_data) if isinstance(row_data, str) else row_data for row_data in dataset['enrolled_date']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now observe that our objects are now datetime objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user                             int64\n",
       "first_open              datetime64[ns]\n",
       "dayofweek                        int64\n",
       "hour                             int32\n",
       "age                              int64\n",
       "screen_list                     object\n",
       "numscreens                       int64\n",
       "minigame                         int64\n",
       "used_premium_feature             int64\n",
       "enrolled                         int64\n",
       "enrolled_date           datetime64[ns]\n",
       "liked                            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting time for response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will need to calculate the difference between those days and set the type to be displayed in hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['difference'] = (dataset['enrolled_date'] - dataset['first_open']).astype('timedelta64[h]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dataset['difference'].dropna(), color = '#3F5D7D')\n",
    "plt.title('Distribution of Time-Since-Enrolled')\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(dataset['difference'].dropna(), color = '#3F5D7D', range = [0, 100])\n",
    "plt.title('Distribution of Time-Since-Enrolled')\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's set the limit for enrollments to be up to 48 hours:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.loc[dataset.difference > 48, 'enrolled'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop the columns we don't need anymore:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns=['difference', 'enrolled_date', 'first_open'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load top screens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now read screens that were most popular among the Users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_screens = pd.read_csv('input/top_screens.csv').top_screens.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Loan2', 'location', 'Institutions', 'Credit3Container',\n",
       "       'VerifyPhone', 'BankVerification', 'VerifyDateOfBirth',\n",
       "       'ProfilePage', 'VerifyCountry', 'Cycle', 'idscreen',\n",
       "       'Credit3Dashboard', 'Loan3', 'CC1Category', 'Splash', 'Loan',\n",
       "       'CC1', 'RewardsContainer', 'Credit3', 'Credit1', 'EditProfile',\n",
       "       'Credit2', 'Finances', 'CC3', 'Saving9', 'Saving1', 'Alerts',\n",
       "       'Saving8', 'Saving10', 'Leaderboard', 'Saving4', 'VerifyMobile',\n",
       "       'VerifyHousing', 'RewardDetail', 'VerifyHousingAmount',\n",
       "       'ProfileMaritalStatus', 'ProfileChildren ', 'ProfileEducation',\n",
       "       'Saving7', 'ProfileEducationMajor', 'Rewards', 'AccountView',\n",
       "       'VerifyAnnualIncome', 'VerifyIncomeType', 'Saving2', 'Saving6',\n",
       "       'Saving2Amount', 'Saving5', 'ProfileJobTitle', 'Login',\n",
       "       'ProfileEmploymentLength', 'WebView', 'SecurityModal', 'Loan4',\n",
       "       'ResendToken', 'TransactionList', 'NetworkFailure', 'ListPicker'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_screens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping screens to fields"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add ',' at the end of every screen_list column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['screen_list'] = dataset.screen_list.astype(str) + ','"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to iterate over top_screens. The loop creates columns with top_screen names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sc in top_screens:\n",
    "    dataset[sc] = dataset.screen_list.str.contains(sc).astype(int) #convert to integer, otherwise we will get True/False\n",
    "    dataset['screen_list'] = dataset.screen_list.str.replace(sc+',','') #remove used top_screen from screen_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we are left with other screen types also, we are going to add all of those into \"Other\" column. We will indicate how many other screens we have. As we have added ',' at the end, it is enough if we calculate number of commas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Other'] = dataset.screen_list.str.count(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns = 'screen_list')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funnels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to work with funnels. Funnels are a group of screens that belong to the same set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "savings_screens = [\n",
    "    'Saving1',\n",
    "    'Saving2',\n",
    "    'Saving2Amount',\n",
    "    'Saving4',\n",
    "    'Saving5',\n",
    "    'Saving6',\n",
    "    'Saving7',\n",
    "    'Saving8',\n",
    "    'Saving9',\n",
    "    'Saving10'\n",
    "]\n",
    "dataset['SavingsCount'] = dataset[savings_screens].sum(axis = 1)\n",
    "dataset = dataset.drop(columns = savings_screens)\n",
    "\n",
    "cm_screens = [\n",
    "    'Credit1',\n",
    "    'Credit2',\n",
    "    'Credit3',\n",
    "    'Credit3Container',\n",
    "    'Credit3Dashboard'\n",
    "]\n",
    "dataset['CMCount'] = dataset[cm_screens].sum(axis=1)\n",
    "dataset = dataset.drop(columns=cm_screens)\n",
    "\n",
    "cc_screens = [\n",
    "    'CC1',\n",
    "    'CC1Category',\n",
    "    'CC3'\n",
    "]\n",
    "dataset['CCCount'] = dataset[cc_screens].sum(axis=1)\n",
    "dataset = dataset.drop(columns=cc_screens)\n",
    "\n",
    "loan_screens = [\n",
    "    'Loan',\n",
    "    'Loan2',\n",
    "    'Loan3',\n",
    "    'Loan4'\n",
    "]\n",
    "dataset['LoansCount'] = dataset[loan_screens].sum(axis=1)\n",
    "dataset = dataset.drop(columns=loan_screens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>liked</th>\n",
       "      <th>location</th>\n",
       "      <th>...</th>\n",
       "      <th>SecurityModal</th>\n",
       "      <th>ResendToken</th>\n",
       "      <th>TransactionList</th>\n",
       "      <th>NetworkFailure</th>\n",
       "      <th>ListPicker</th>\n",
       "      <th>Other</th>\n",
       "      <th>SavingsCount</th>\n",
       "      <th>CMCount</th>\n",
       "      <th>CCCount</th>\n",
       "      <th>LoansCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>235136</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>333588</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>254414</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>234192</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>51549</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     user  dayofweek  hour  age  numscreens  minigame  used_premium_feature  \\\n",
       "0  235136          3     2   23          15         0                     0   \n",
       "1  333588          6     1   24          13         0                     0   \n",
       "2  254414          1    19   23           3         0                     1   \n",
       "3  234192          4    16   28          40         0                     0   \n",
       "4   51549          1    18   31          32         0                     0   \n",
       "\n",
       "   enrolled  liked  location  ...  SecurityModal  ResendToken  \\\n",
       "0         0      0         0  ...              0            0   \n",
       "1         0      0         1  ...              0            0   \n",
       "2         0      1         0  ...              0            0   \n",
       "3         1      0         1  ...              0            0   \n",
       "4         1      1         0  ...              0            0   \n",
       "\n",
       "   TransactionList  NetworkFailure  ListPicker  Other  SavingsCount  CMCount  \\\n",
       "0                0               0           0      7             0        0   \n",
       "1                0               0           0      5             0        0   \n",
       "2                0               0           0      0             0        0   \n",
       "3                0               0           0      6             0        3   \n",
       "4                0               0           0     10             0        2   \n",
       "\n",
       "   CCCount  LoansCount  \n",
       "0        0           1  \n",
       "1        0           1  \n",
       "2        0           1  \n",
       "3        0           1  \n",
       "4        0           1  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "      <th>numscreens</th>\n",
       "      <th>minigame</th>\n",
       "      <th>used_premium_feature</th>\n",
       "      <th>enrolled</th>\n",
       "      <th>liked</th>\n",
       "      <th>location</th>\n",
       "      <th>...</th>\n",
       "      <th>SecurityModal</th>\n",
       "      <th>ResendToken</th>\n",
       "      <th>TransactionList</th>\n",
       "      <th>NetworkFailure</th>\n",
       "      <th>ListPicker</th>\n",
       "      <th>Other</th>\n",
       "      <th>SavingsCount</th>\n",
       "      <th>CMCount</th>\n",
       "      <th>CCCount</th>\n",
       "      <th>LoansCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>186889.729900</td>\n",
       "      <td>3.029860</td>\n",
       "      <td>12.557220</td>\n",
       "      <td>31.72436</td>\n",
       "      <td>21.095900</td>\n",
       "      <td>0.107820</td>\n",
       "      <td>0.172020</td>\n",
       "      <td>0.497000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.517760</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014220</td>\n",
       "      <td>0.013340</td>\n",
       "      <td>0.013400</td>\n",
       "      <td>0.008200</td>\n",
       "      <td>0.007580</td>\n",
       "      <td>6.214260</td>\n",
       "      <td>0.365020</td>\n",
       "      <td>0.92776</td>\n",
       "      <td>0.176860</td>\n",
       "      <td>0.788400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>107768.520361</td>\n",
       "      <td>2.031997</td>\n",
       "      <td>7.438072</td>\n",
       "      <td>10.80331</td>\n",
       "      <td>15.728812</td>\n",
       "      <td>0.310156</td>\n",
       "      <td>0.377402</td>\n",
       "      <td>0.499996</td>\n",
       "      <td>0.371184</td>\n",
       "      <td>0.499689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118398</td>\n",
       "      <td>0.114727</td>\n",
       "      <td>0.114981</td>\n",
       "      <td>0.090183</td>\n",
       "      <td>0.086733</td>\n",
       "      <td>3.672561</td>\n",
       "      <td>1.405511</td>\n",
       "      <td>1.21751</td>\n",
       "      <td>0.612787</td>\n",
       "      <td>0.677462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>93526.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>187193.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>279984.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>373662.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>101.00000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                user     dayofweek          hour          age    numscreens  \\\n",
       "count   50000.000000  50000.000000  50000.000000  50000.00000  50000.000000   \n",
       "mean   186889.729900      3.029860     12.557220     31.72436     21.095900   \n",
       "std    107768.520361      2.031997      7.438072     10.80331     15.728812   \n",
       "min        13.000000      0.000000      0.000000     16.00000      1.000000   \n",
       "25%     93526.750000      1.000000      5.000000     24.00000     10.000000   \n",
       "50%    187193.500000      3.000000     14.000000     29.00000     18.000000   \n",
       "75%    279984.250000      5.000000     19.000000     37.00000     28.000000   \n",
       "max    373662.000000      6.000000     23.000000    101.00000    325.000000   \n",
       "\n",
       "           minigame  used_premium_feature      enrolled         liked  \\\n",
       "count  50000.000000          50000.000000  50000.000000  50000.000000   \n",
       "mean       0.107820              0.172020      0.497000      0.165000   \n",
       "std        0.310156              0.377402      0.499996      0.371184   \n",
       "min        0.000000              0.000000      0.000000      0.000000   \n",
       "25%        0.000000              0.000000      0.000000      0.000000   \n",
       "50%        0.000000              0.000000      0.000000      0.000000   \n",
       "75%        0.000000              0.000000      1.000000      0.000000   \n",
       "max        1.000000              1.000000      1.000000      1.000000   \n",
       "\n",
       "           location  ...  SecurityModal   ResendToken  TransactionList  \\\n",
       "count  50000.000000  ...   50000.000000  50000.000000     50000.000000   \n",
       "mean       0.517760  ...       0.014220      0.013340         0.013400   \n",
       "std        0.499689  ...       0.118398      0.114727         0.114981   \n",
       "min        0.000000  ...       0.000000      0.000000         0.000000   \n",
       "25%        0.000000  ...       0.000000      0.000000         0.000000   \n",
       "50%        1.000000  ...       0.000000      0.000000         0.000000   \n",
       "75%        1.000000  ...       0.000000      0.000000         0.000000   \n",
       "max        1.000000  ...       1.000000      1.000000         1.000000   \n",
       "\n",
       "       NetworkFailure    ListPicker         Other  SavingsCount      CMCount  \\\n",
       "count    50000.000000  50000.000000  50000.000000  50000.000000  50000.00000   \n",
       "mean         0.008200      0.007580      6.214260      0.365020      0.92776   \n",
       "std          0.090183      0.086733      3.672561      1.405511      1.21751   \n",
       "min          0.000000      0.000000      0.000000      0.000000      0.00000   \n",
       "25%          0.000000      0.000000      3.000000      0.000000      0.00000   \n",
       "50%          0.000000      0.000000      6.000000      0.000000      0.00000   \n",
       "75%          0.000000      0.000000      8.000000      0.000000      1.00000   \n",
       "max          1.000000      1.000000     35.000000     10.000000      5.00000   \n",
       "\n",
       "            CCCount    LoansCount  \n",
       "count  50000.000000  50000.000000  \n",
       "mean       0.176860      0.788400  \n",
       "std        0.612787      0.677462  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.000000      0.000000  \n",
       "50%        0.000000      1.000000  \n",
       "75%        0.000000      1.000000  \n",
       "max        3.000000      3.000000  \n",
       "\n",
       "[8 rows x 50 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user', 'dayofweek', 'hour', 'age', 'numscreens', 'minigame',\n",
       "       'used_premium_feature', 'enrolled', 'liked', 'location', 'Institutions',\n",
       "       'VerifyPhone', 'BankVerification', 'VerifyDateOfBirth', 'ProfilePage',\n",
       "       'VerifyCountry', 'Cycle', 'idscreen', 'Splash', 'RewardsContainer',\n",
       "       'EditProfile', 'Finances', 'Alerts', 'Leaderboard', 'VerifyMobile',\n",
       "       'VerifyHousing', 'RewardDetail', 'VerifyHousingAmount',\n",
       "       'ProfileMaritalStatus', 'ProfileChildren ', 'ProfileEducation',\n",
       "       'ProfileEducationMajor', 'Rewards', 'AccountView', 'VerifyAnnualIncome',\n",
       "       'VerifyIncomeType', 'ProfileJobTitle', 'Login',\n",
       "       'ProfileEmploymentLength', 'WebView', 'SecurityModal', 'ResendToken',\n",
       "       'TransactionList', 'NetworkFailure', 'ListPicker', 'Other',\n",
       "       'SavingsCount', 'CMCount', 'CCCount', 'LoansCount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv('new_appdata10.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('input/new_appdata10.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting independent and response variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split response variable from independent features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop(columns = 'enrolled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dataset['enrolled']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the dataset into the training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing identifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training and testing, user id is not needed in our model, but at the end we are going to associate the prediction to the user it came from, so let's save it away before removing it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_identifier = X_train['user']\n",
    "X_train = X_train.drop(columns = 'user')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_identifier = X_test['user']\n",
    "X_test = X_test.drop(columns = 'user')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step will be feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_X = StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "StandardScaler returns numpy array. The problem with that is that it looses the column names and index. Index is identifying each set of fields to the user and columns build our models. So we are going to save scaled part into a different dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n_train,v_train in X_train.items():\n",
    "    if v_train.dtype == \"object\":\n",
    "        X_train[n_train] = v_train.factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n_test,v_test in X_test.items():\n",
    "    if v_test.dtype == \"object\":\n",
    "        X_test[n_test] = v_test.factorize()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2 = pd.DataFrame(sc_X.fit_transform(X_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going only to transform scaled X cause it was already fitted to the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test2 = pd.DataFrame(sc_X.transform(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2.columns = X_train.columns.values\n",
    "X_test2.columns = X_test.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2.index = X_train.index.values\n",
    "X_test2.index = X_test.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train2\n",
    "X_test = X_test2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. MODEL BUILDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting model to the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Penalty l1 is going to change the model from regular logistic regression model to l1 regularization. We do it because, as mentioned above, screens can be correlated one to another. Maybe one screen is next to the other and maybe if you click on one screen, then you will click on the other. We address a lot of this correlations while creating the funnel features that incorporate all the screens that belong to the same set but there might be other correlations that exist even though the screens does not belong to the same set. What l1 penalty does, it penalizes those cases in which one particular feature gets a very big coefficient. This is important in the model when we are working with app screens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = LogisticRegression(random_state = 0, penalty = 'l1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:929: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l1',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's evaluate if our predictions were accurate or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, precision_score, recall_score, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3741   38]\n",
      " [   1 6220]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, 0.0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(cm, annot=True, fmt='d')\n",
    "bottom, top = ax.get_ylim()\n",
    "ax.set_ylim(bottom + 0.5, top - 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9997    0.9899    0.9948      3779\n",
      "           1     0.9939    0.9998    0.9969      6221\n",
      "\n",
      "    accuracy                         0.9961     10000\n",
      "   macro avg     0.9968    0.9949    0.9958     10000\n",
      "weighted avg     0.9961    0.9961    0.9961     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, digits=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We managed to improve a bit our model - now only 39 samples are misclassified. The good point is that only 1 out of 39 elements that are misclassified are an error type 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data Accuracy: 0.9961\n"
     ]
    }
   ],
   "source": [
    "print('Test Data Accuracy: %0.4f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\annak\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Accuracy: 0.996 (+/- 0.022)\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Accuracy: %0.3f (+/- %0.3f)' % (accuracies.mean(), accuracies.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formatting final results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_results = pd.concat([y_test, test_identifier], axis = 1).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_results['predicted_results'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>user</th>\n",
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       "      <th>predicted_results</th>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user  enrolled  predicted_results\n",
       "0     239786         1                  1\n",
       "1     279644         1                  1\n",
       "2      98290         0                  0\n",
       "3     170150         1                  1\n",
       "4     237568         1                  1\n",
       "...      ...       ...                ...\n",
       "9995  143036         1                  1\n",
       "9996   91158         1                  1\n",
       "9997  248318         0                  0\n",
       "9998  142418         1                  1\n",
       "9999  279355         1                  1\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_results[['user', 'enrolled', 'predicted_results']].reset_index(drop=True)"
   ]
  }
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