Logistic Regression - Predict Customer clicks on FB ads - Solution.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# CODE TO PERFORM LOGISTIC REGRESSION\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PROBLEM STATEMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have been hired as a consultant to a start-up that is running a targetted marketing ads on facebook. The company wants to anaylze customer behaviour by predicting which customer clicks on the advertisement. Customer data is as follows: \n",
    "\n",
    "Inputs: \n",
    "- Name \n",
    "- e-mail \n",
    "- Country \n",
    "- Time on Facebook \n",
    "- Estimated Salary (derived from other parameters)\n",
    "\n",
    "Outputs:\n",
    "- Click (1: customer clicked on Ad, 0: Customer did not click on the Ad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #0: LIBRARIES IMPORT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #1: IMPORT DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# read the data using pandas dataframe\n",
    "training_set = pd.read_csv('Facebook_Ads_2.csv', encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Martina Avila</td>\n",
       "      <td>cubilia.Curae.Phasellus@quisaccumsanconvallis.edu</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>25.649648</td>\n",
       "      <td>55330.06006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Harlan Barnes</td>\n",
       "      <td>eu.dolor@diam.co.uk</td>\n",
       "      <td>Belize</td>\n",
       "      <td>32.456107</td>\n",
       "      <td>79049.07674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naomi Rodriquez</td>\n",
       "      <td>vulputate.mauris.sagittis@ametconsectetueradip...</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>20.945978</td>\n",
       "      <td>41098.60826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jade Cunningham</td>\n",
       "      <td>malesuada@dignissim.com</td>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>54.039325</td>\n",
       "      <td>37143.35536</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cedric Leach</td>\n",
       "      <td>felis.ullamcorper.viverra@egetmollislectus.net</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>34.249729</td>\n",
       "      <td>37355.11276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             Names                                             emails  \\\n",
       "0    Martina Avila  cubilia.Curae.Phasellus@quisaccumsanconvallis.edu   \n",
       "1    Harlan Barnes                                eu.dolor@diam.co.uk   \n",
       "2  Naomi Rodriquez  vulputate.mauris.sagittis@ametconsectetueradip...   \n",
       "3  Jade Cunningham                            malesuada@dignissim.com   \n",
       "4     Cedric Leach     felis.ullamcorper.viverra@egetmollislectus.net   \n",
       "\n",
       "        Country  Time Spent on Site       Salary  Clicked  \n",
       "0      Bulgaria           25.649648  55330.06006        0  \n",
       "1        Belize           32.456107  79049.07674        1  \n",
       "2       Algeria           20.945978  41098.60826        0  \n",
       "3  Cook Islands           54.039325  37143.35536        1  \n",
       "4        Brazil           34.249729  37355.11276        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the data head!\n",
    "training_set.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Rigel</td>\n",
       "      <td>egestas.blandit.Nam@semvitaealiquam.com</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>19.222746</td>\n",
       "      <td>44969.13495</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Walter</td>\n",
       "      <td>ligula@Cumsociis.ca</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>22.665662</td>\n",
       "      <td>41686.20425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Vanna</td>\n",
       "      <td>Cum.sociis.natoque@Sedmolestie.edu</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>35.320239</td>\n",
       "      <td>23989.80864</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>Pearl</td>\n",
       "      <td>penatibus.et@massanonante.com</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>26.539170</td>\n",
       "      <td>31708.57054</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Nell</td>\n",
       "      <td>Quisque.varius@arcuVivamussit.net</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>32.386148</td>\n",
       "      <td>74331.35442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      Names                                   emails                Country  \\\n",
       "494   Rigel  egestas.blandit.Nam@semvitaealiquam.com  Sao Tome and Principe   \n",
       "495  Walter                      ligula@Cumsociis.ca                  Nepal   \n",
       "496   Vanna       Cum.sociis.natoque@Sedmolestie.edu               Zimbabwe   \n",
       "497   Pearl            penatibus.et@massanonante.com            Philippines   \n",
       "498    Nell        Quisque.varius@arcuVivamussit.net               Botswana   \n",
       "\n",
       "     Time Spent on Site       Salary  Clicked  \n",
       "494           19.222746  44969.13495        0  \n",
       "495           22.665662  41686.20425        0  \n",
       "496           35.320239  23989.80864        0  \n",
       "497           26.539170  31708.57054        0  \n",
       "498           32.386148  74331.35442        1  "
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the data head!\n",
    "training_set.tail()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #2: EXPLORE/VISUALIZE DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "click    = training_set[training_set['Clicked']==1]\n",
    "no_click = training_set[training_set['Clicked']==0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total = 499\n",
      "Number of customers who clicked on Ad = 250\n",
      "Percentage Clicked = 50.1002004008016 %\n",
      "Did not Click = 249\n",
      "Percentage who did not Click = 49.899799599198396 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Total =\", len(training_set))\n",
    "\n",
    "print(\"Number of customers who clicked on Ad =\", len(click))\n",
    "print(\"Percentage Clicked =\", 1.*len(click)/len(training_set)*100.0, \"%\")\n",
    " \n",
    "print(\"Did not Click =\", len(no_click))\n",
    "print(\"Percentage who did not Click =\", 1.*len(no_click)/len(training_set)*100.0, \"%\")\n",
    " \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
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     "execution_count": 7,
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     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(training_set['Time Spent on Site'], training_set['Salary'], hue = training_set['Clicked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1454074d240>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "sns.boxplot(x='Clicked', y='Salary',data=training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14540864c88>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "sns.boxplot(x='Clicked', y='Time Spent on Site',data=training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x145409a0e10>"
      ]
     },
     "execution_count": 10,
     "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": [
    "training_set['Salary'].hist(bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14540a26fd0>"
      ]
     },
     "execution_count": 11,
     "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": [
    "training_set['Time Spent on Site'].hist(bins = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #3: PREPARE THE DATA FOR TRAINING/ DATA CLEANING "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "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>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Martina Avila</td>\n",
       "      <td>cubilia.Curae.Phasellus@quisaccumsanconvallis.edu</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>25.649648</td>\n",
       "      <td>55330.060060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Harlan Barnes</td>\n",
       "      <td>eu.dolor@diam.co.uk</td>\n",
       "      <td>Belize</td>\n",
       "      <td>32.456107</td>\n",
       "      <td>79049.076740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Naomi Rodriquez</td>\n",
       "      <td>vulputate.mauris.sagittis@ametconsectetueradip...</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>20.945978</td>\n",
       "      <td>41098.608260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jade Cunningham</td>\n",
       "      <td>malesuada@dignissim.com</td>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>54.039325</td>\n",
       "      <td>37143.355360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cedric Leach</td>\n",
       "      <td>felis.ullamcorper.viverra@egetmollislectus.net</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>34.249729</td>\n",
       "      <td>37355.112760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Carla Hester</td>\n",
       "      <td>mi@Aliquamerat.edu</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>52.009946</td>\n",
       "      <td>80642.652220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Griffin Rivera</td>\n",
       "      <td>vehicula@at.co.uk</td>\n",
       "      <td>Syria</td>\n",
       "      <td>42.881370</td>\n",
       "      <td>61908.398150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Orli Casey</td>\n",
       "      <td>nunc.est.mollis@Suspendissetristiqueneque.co.uk</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>25.676920</td>\n",
       "      <td>29851.411530</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Marny Obrien</td>\n",
       "      <td>Phasellus@sedsemegestas.org</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>40.698233</td>\n",
       "      <td>34281.118780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rhonda Chavez</td>\n",
       "      <td>nec@nuncest.com</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>29.687362</td>\n",
       "      <td>41098.878810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jerome Rowe</td>\n",
       "      <td>ipsum.cursus@dui.org</td>\n",
       "      <td>Sint Maarten</td>\n",
       "      <td>33.272572</td>\n",
       "      <td>54127.017150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Akeem Gibson</td>\n",
       "      <td>turpis.egestas.Fusce@purus.edu</td>\n",
       "      <td>Greenland</td>\n",
       "      <td>34.530898</td>\n",
       "      <td>30221.937140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Quin Smith</td>\n",
       "      <td>nulla@ipsum.edu</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>25.313695</td>\n",
       "      <td>43763.997580</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tatum Moon</td>\n",
       "      <td>Cras.sed.leo@Seddiamlorem.ca</td>\n",
       "      <td>Palestine, State of</td>\n",
       "      <td>46.540708</td>\n",
       "      <td>48663.494680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sharon Sharpe</td>\n",
       "      <td>eget.metus@aaliquetvel.co.uk</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>37.716582</td>\n",
       "      <td>64695.977000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thomas Williams</td>\n",
       "      <td>aliquet.molestie@ut.org</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>25.056782</td>\n",
       "      <td>17759.933850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Blaine Bender</td>\n",
       "      <td>ultrices.posuere.cubilia@pedenonummyut.net</td>\n",
       "      <td>Tokelau</td>\n",
       "      <td>41.095960</td>\n",
       "      <td>49463.381300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Stephen Lindsey</td>\n",
       "      <td>erat.eget.ipsum@tinciduntpede.org</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>24.761563</td>\n",
       "      <td>38484.423180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sloane Mann</td>\n",
       "      <td>at.augue@augue.net</td>\n",
       "      <td>Chad</td>\n",
       "      <td>48.870175</td>\n",
       "      <td>34774.444070</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Athena Wolf</td>\n",
       "      <td>volutpat.Nulla.facilisis@primis.ca</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>27.833832</td>\n",
       "      <td>51644.493300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Blythe Romero</td>\n",
       "      <td>Sed.eu@risusNuncac.co.uk</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>17.103576</td>\n",
       "      <td>61661.765190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Zelenia Byers</td>\n",
       "      <td>auctor.non@sapien.co.uk</td>\n",
       "      <td>Angola</td>\n",
       "      <td>25.597796</td>\n",
       "      <td>53630.763770</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Nola Wiggins</td>\n",
       "      <td>Aliquam@augue.edu</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>42.249190</td>\n",
       "      <td>32215.099500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Micah Wheeler</td>\n",
       "      <td>arcu.eu@tincidunt.org</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>37.135922</td>\n",
       "      <td>51733.657220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Caryn Hendrix</td>\n",
       "      <td>condimentum.Donec@duiCum.com</td>\n",
       "      <td>Macedonia</td>\n",
       "      <td>32.443111</td>\n",
       "      <td>51677.699040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Hedda Miranda</td>\n",
       "      <td>scelerisque@magnased.com</td>\n",
       "      <td>Oman</td>\n",
       "      <td>19.936674</td>\n",
       "      <td>34263.788530</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Ulric Lynn</td>\n",
       "      <td>sociis@vulputateveliteu.com</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>39.820539</td>\n",
       "      <td>70503.313200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Alma Pope</td>\n",
       "      <td>Nunc.mauris.Morbi@turpis.org</td>\n",
       "      <td>Namibia</td>\n",
       "      <td>30.728855</td>\n",
       "      <td>67022.273020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Gemma Hendrix</td>\n",
       "      <td>lobortis@non.co.uk</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>24.043583</td>\n",
       "      <td>36474.709700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Castor Wood</td>\n",
       "      <td>velit.Pellentesque@Nulla.net</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>39.056036</td>\n",
       "      <td>29178.361010</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>Igor</td>\n",
       "      <td>Proin.eget@aliquetnecimperdiet.org</td>\n",
       "      <td>Grenada</td>\n",
       "      <td>30.434035</td>\n",
       "      <td>40446.847990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>Diana</td>\n",
       "      <td>purus.ac.tellus@parturientmontesnascetur.org</td>\n",
       "      <td>Guadeloupe</td>\n",
       "      <td>28.274339</td>\n",
       "      <td>82395.636880</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>Todd</td>\n",
       "      <td>egestas.a@vel.ca</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>40.736279</td>\n",
       "      <td>35323.201010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Zephania</td>\n",
       "      <td>montes@sedsem.ca</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>23.556676</td>\n",
       "      <td>43072.567540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Brock</td>\n",
       "      <td>Vivamus.nisi@elitpharetraut.ca</td>\n",
       "      <td>United States Minor Outlying Islands</td>\n",
       "      <td>37.843073</td>\n",
       "      <td>57841.359800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>Avye</td>\n",
       "      <td>Cras@eros.net</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>44.893467</td>\n",
       "      <td>71840.404550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>Serena</td>\n",
       "      <td>odio.Nam@estac.ca</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>23.277282</td>\n",
       "      <td>40765.043790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>Barclay</td>\n",
       "      <td>diam.Proin@ut.edu</td>\n",
       "      <td>Saint Helena, Ascension and Tristan da Cunha</td>\n",
       "      <td>37.014895</td>\n",
       "      <td>73057.244510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>Isabella</td>\n",
       "      <td>et.magnis@necmetus.ca</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>25.087617</td>\n",
       "      <td>61653.383110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>Mia</td>\n",
       "      <td>sed.est@afelisullamcorper.edu</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>20.706101</td>\n",
       "      <td>7147.723544</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>Chester</td>\n",
       "      <td>Integer@odioauctor.co.uk</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>42.190279</td>\n",
       "      <td>48020.567990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>pretium.aliquet.metus@pretiumneque.com</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>42.885297</td>\n",
       "      <td>58162.284990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>Heather</td>\n",
       "      <td>erat.Etiam@elementum.org</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>22.439488</td>\n",
       "      <td>35917.848190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>Justine</td>\n",
       "      <td>orci.adipiscing@penatibuset.com</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>37.674058</td>\n",
       "      <td>62034.486020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>Bert</td>\n",
       "      <td>sem.Pellentesque@quis.net</td>\n",
       "      <td>Israel</td>\n",
       "      <td>20.420892</td>\n",
       "      <td>40114.156210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>Gil</td>\n",
       "      <td>Phasellus.fermentum@dictumplacerataugue.net</td>\n",
       "      <td>France</td>\n",
       "      <td>36.902378</td>\n",
       "      <td>32583.024180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>Dolan</td>\n",
       "      <td>ipsum.Phasellus@egestasblanditNam.edu</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>33.349037</td>\n",
       "      <td>52020.916460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>Cheryl</td>\n",
       "      <td>iaculis.enim@nislelementum.edu</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>41.544304</td>\n",
       "      <td>55792.698510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Courtney</td>\n",
       "      <td>porttitor.tellus@elitelit.org</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>43.281586</td>\n",
       "      <td>54123.646500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Neville</td>\n",
       "      <td>felis.Nulla@mi.ca</td>\n",
       "      <td>Mauritania</td>\n",
       "      <td>24.331054</td>\n",
       "      <td>59873.517000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Gloria</td>\n",
       "      <td>Curabitur.egestas.nunc@fermentumfermentum.ca</td>\n",
       "      <td>Gambia</td>\n",
       "      <td>23.865371</td>\n",
       "      <td>23439.867820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Jonah</td>\n",
       "      <td>augue@risusNuncac.co.uk</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>34.828303</td>\n",
       "      <td>50151.326220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Merrill</td>\n",
       "      <td>dolor.sit@turpisIn.com</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>37.173216</td>\n",
       "      <td>63750.415580</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Nolan</td>\n",
       "      <td>Donec.at@neccursus.co.uk</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>47329.565840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Winter</td>\n",
       "      <td>egestas.urna.justo@maurissagittis.edu</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>24.615228</td>\n",
       "      <td>34721.748620</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>Rigel</td>\n",
       "      <td>egestas.blandit.Nam@semvitaealiquam.com</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>19.222746</td>\n",
       "      <td>44969.134950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>Walter</td>\n",
       "      <td>ligula@Cumsociis.ca</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>22.665662</td>\n",
       "      <td>41686.204250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Vanna</td>\n",
       "      <td>Cum.sociis.natoque@Sedmolestie.edu</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>35.320239</td>\n",
       "      <td>23989.808640</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>Pearl</td>\n",
       "      <td>penatibus.et@massanonante.com</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>26.539170</td>\n",
       "      <td>31708.570540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Nell</td>\n",
       "      <td>Quisque.varius@arcuVivamussit.net</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>32.386148</td>\n",
       "      <td>74331.354420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Names                                             emails  \\\n",
       "0      Martina Avila  cubilia.Curae.Phasellus@quisaccumsanconvallis.edu   \n",
       "1      Harlan Barnes                                eu.dolor@diam.co.uk   \n",
       "2    Naomi Rodriquez  vulputate.mauris.sagittis@ametconsectetueradip...   \n",
       "3    Jade Cunningham                            malesuada@dignissim.com   \n",
       "4       Cedric Leach     felis.ullamcorper.viverra@egetmollislectus.net   \n",
       "5       Carla Hester                                 mi@Aliquamerat.edu   \n",
       "6     Griffin Rivera                                  vehicula@at.co.uk   \n",
       "7         Orli Casey    nunc.est.mollis@Suspendissetristiqueneque.co.uk   \n",
       "8       Marny Obrien                        Phasellus@sedsemegestas.org   \n",
       "9      Rhonda Chavez                                    nec@nuncest.com   \n",
       "10       Jerome Rowe                               ipsum.cursus@dui.org   \n",
       "11      Akeem Gibson                     turpis.egestas.Fusce@purus.edu   \n",
       "12        Quin Smith                                    nulla@ipsum.edu   \n",
       "13        Tatum Moon                       Cras.sed.leo@Seddiamlorem.ca   \n",
       "14     Sharon Sharpe                       eget.metus@aaliquetvel.co.uk   \n",
       "15   Thomas Williams                            aliquet.molestie@ut.org   \n",
       "16     Blaine Bender         ultrices.posuere.cubilia@pedenonummyut.net   \n",
       "17   Stephen Lindsey                  erat.eget.ipsum@tinciduntpede.org   \n",
       "18       Sloane Mann                                 at.augue@augue.net   \n",
       "19       Athena Wolf                 volutpat.Nulla.facilisis@primis.ca   \n",
       "20     Blythe Romero                           Sed.eu@risusNuncac.co.uk   \n",
       "21     Zelenia Byers                            auctor.non@sapien.co.uk   \n",
       "22      Nola Wiggins                                  Aliquam@augue.edu   \n",
       "23     Micah Wheeler                              arcu.eu@tincidunt.org   \n",
       "24     Caryn Hendrix                       condimentum.Donec@duiCum.com   \n",
       "25     Hedda Miranda                           scelerisque@magnased.com   \n",
       "26        Ulric Lynn                        sociis@vulputateveliteu.com   \n",
       "27         Alma Pope                       Nunc.mauris.Morbi@turpis.org   \n",
       "28     Gemma Hendrix                                 lobortis@non.co.uk   \n",
       "29       Castor Wood                       velit.Pellentesque@Nulla.net   \n",
       "..               ...                                                ...   \n",
       "469             Igor                 Proin.eget@aliquetnecimperdiet.org   \n",
       "470            Diana       purus.ac.tellus@parturientmontesnascetur.org   \n",
       "471             Todd                                   egestas.a@vel.ca   \n",
       "472         Zephania                                   montes@sedsem.ca   \n",
       "473            Brock                     Vivamus.nisi@elitpharetraut.ca   \n",
       "474             Avye                                      Cras@eros.net   \n",
       "475           Serena                                  odio.Nam@estac.ca   \n",
       "476          Barclay                                  diam.Proin@ut.edu   \n",
       "477         Isabella                              et.magnis@necmetus.ca   \n",
       "478              Mia                      sed.est@afelisullamcorper.edu   \n",
       "479          Chester                           Integer@odioauctor.co.uk   \n",
       "480            Isaac             pretium.aliquet.metus@pretiumneque.com   \n",
       "481          Heather                           erat.Etiam@elementum.org   \n",
       "482          Justine                    orci.adipiscing@penatibuset.com   \n",
       "483             Bert                          sem.Pellentesque@quis.net   \n",
       "484              Gil        Phasellus.fermentum@dictumplacerataugue.net   \n",
       "485            Dolan              ipsum.Phasellus@egestasblanditNam.edu   \n",
       "486           Cheryl                     iaculis.enim@nislelementum.edu   \n",
       "487         Courtney                      porttitor.tellus@elitelit.org   \n",
       "488          Neville                                  felis.Nulla@mi.ca   \n",
       "489           Gloria       Curabitur.egestas.nunc@fermentumfermentum.ca   \n",
       "490            Jonah                            augue@risusNuncac.co.uk   \n",
       "491          Merrill                             dolor.sit@turpisIn.com   \n",
       "492            Nolan                           Donec.at@neccursus.co.uk   \n",
       "493           Winter              egestas.urna.justo@maurissagittis.edu   \n",
       "494            Rigel            egestas.blandit.Nam@semvitaealiquam.com   \n",
       "495           Walter                                ligula@Cumsociis.ca   \n",
       "496            Vanna                 Cum.sociis.natoque@Sedmolestie.edu   \n",
       "497            Pearl                      penatibus.et@massanonante.com   \n",
       "498             Nell                  Quisque.varius@arcuVivamussit.net   \n",
       "\n",
       "                                          Country  Time Spent on Site  \\\n",
       "0                                        Bulgaria           25.649648   \n",
       "1                                          Belize           32.456107   \n",
       "2                                         Algeria           20.945978   \n",
       "3                                    Cook Islands           54.039325   \n",
       "4                                          Brazil           34.249729   \n",
       "5                                         Liberia           52.009946   \n",
       "6                                           Syria           42.881370   \n",
       "7                                  Czech Republic           25.676920   \n",
       "8                                         Armenia           40.698233   \n",
       "9                                         Somalia           29.687362   \n",
       "10                                   Sint Maarten           33.272572   \n",
       "11                                      Greenland           34.530898   \n",
       "12                                      Nicaragua           25.313695   \n",
       "13                            Palestine, State of           46.540708   \n",
       "14                           United Arab Emirates           37.716582   \n",
       "15                                          Gabon           25.056782   \n",
       "16                                        Tokelau           41.095960   \n",
       "17                                       Portugal           24.761563   \n",
       "18                                           Chad           48.870175   \n",
       "19                                           Iraq           27.833832   \n",
       "20                                          Sudan           17.103576   \n",
       "21                                         Angola           25.597796   \n",
       "22                                        Nigeria           42.249190   \n",
       "23                                     Madagascar           37.135922   \n",
       "24                                      Macedonia           32.443111   \n",
       "25                                           Oman           19.936674   \n",
       "26                                       Colombia           39.820539   \n",
       "27                                        Namibia           30.728855   \n",
       "28                                        Denmark           24.043583   \n",
       "29                             Dominican Republic           39.056036   \n",
       "..                                            ...                 ...   \n",
       "469                                       Grenada           30.434035   \n",
       "470                                    Guadeloupe           28.274339   \n",
       "471                                   New Zealand           40.736279   \n",
       "472                                        Malawi           23.556676   \n",
       "473          United States Minor Outlying Islands           37.843073   \n",
       "474                                       Armenia           44.893467   \n",
       "475                      Northern Mariana Islands           23.277282   \n",
       "476  Saint Helena, Ascension and Tristan da Cunha           37.014895   \n",
       "477                                        Turkey           25.087617   \n",
       "478                                  Turkmenistan           20.706101   \n",
       "479                                       Jamaica           42.190279   \n",
       "480                         Saint Kitts and Nevis           42.885297   \n",
       "481                                    Madagascar           22.439488   \n",
       "482                                     Venezuela           37.674058   \n",
       "483                                        Israel           20.420892   \n",
       "484                                        France           36.902378   \n",
       "485                                         Yemen           33.349037   \n",
       "486                                     Indonesia           41.544304   \n",
       "487                                      Malaysia           43.281586   \n",
       "488                                    Mauritania           24.331054   \n",
       "489                                        Gambia           23.865371   \n",
       "490                                       Myanmar           34.828303   \n",
       "491                                         Egypt           37.173216   \n",
       "492                                        Latvia            5.000000   \n",
       "493                             Wallis and Futuna           24.615228   \n",
       "494                         Sao Tome and Principe           19.222746   \n",
       "495                                         Nepal           22.665662   \n",
       "496                                      Zimbabwe           35.320239   \n",
       "497                                   Philippines           26.539170   \n",
       "498                                      Botswana           32.386148   \n",
       "\n",
       "           Salary  Clicked  \n",
       "0    55330.060060        0  \n",
       "1    79049.076740        1  \n",
       "2    41098.608260        0  \n",
       "3    37143.355360        1  \n",
       "4    37355.112760        0  \n",
       "5    80642.652220        1  \n",
       "6    61908.398150        1  \n",
       "7    29851.411530        0  \n",
       "8    34281.118780        1  \n",
       "9    41098.878810        0  \n",
       "10   54127.017150        0  \n",
       "11   30221.937140        0  \n",
       "12   43763.997580        0  \n",
       "13   48663.494680        1  \n",
       "14   64695.977000        1  \n",
       "15   17759.933850        0  \n",
       "16   49463.381300        1  \n",
       "17   38484.423180        0  \n",
       "18   34774.444070        1  \n",
       "19   51644.493300        0  \n",
       "20   61661.765190        0  \n",
       "21   53630.763770        0  \n",
       "22   32215.099500        1  \n",
       "23   51733.657220        1  \n",
       "24   51677.699040        0  \n",
       "25   34263.788530        0  \n",
       "26   70503.313200        1  \n",
       "27   67022.273020        1  \n",
       "28   36474.709700        1  \n",
       "29   29178.361010        0  \n",
       "..            ...      ...  \n",
       "469  40446.847990        0  \n",
       "470  82395.636880        1  \n",
       "471  35323.201010        1  \n",
       "472  43072.567540        0  \n",
       "473  57841.359800        0  \n",
       "474  71840.404550        1  \n",
       "475  40765.043790        0  \n",
       "476  73057.244510        1  \n",
       "477  61653.383110        0  \n",
       "478   7147.723544        0  \n",
       "479  48020.567990        1  \n",
       "480  58162.284990        1  \n",
       "481  35917.848190        0  \n",
       "482  62034.486020        1  \n",
       "483  40114.156210        0  \n",
       "484  32583.024180        0  \n",
       "485  52020.916460        1  \n",
       "486  55792.698510        1  \n",
       "487  54123.646500        1  \n",
       "488  59873.517000        0  \n",
       "489  23439.867820        0  \n",
       "490  50151.326220        1  \n",
       "491  63750.415580        1  \n",
       "492  47329.565840        0  \n",
       "493  34721.748620        0  \n",
       "494  44969.134950        0  \n",
       "495  41686.204250        0  \n",
       "496  23989.808640        0  \n",
       "497  31708.570540        0  \n",
       "498  74331.354420        1  \n",
       "\n",
       "[499 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's drop the emails, country and names (we can make use of the country later!)\n",
    "training_set.drop(['Names', 'emails', 'Country'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.649648</td>\n",
       "      <td>55330.060060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.456107</td>\n",
       "      <td>79049.076740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.945978</td>\n",
       "      <td>41098.608260</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.039325</td>\n",
       "      <td>37143.355360</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.249729</td>\n",
       "      <td>37355.112760</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>52.009946</td>\n",
       "      <td>80642.652220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>42.881370</td>\n",
       "      <td>61908.398150</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25.676920</td>\n",
       "      <td>29851.411530</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40.698233</td>\n",
       "      <td>34281.118780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>29.687362</td>\n",
       "      <td>41098.878810</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33.272572</td>\n",
       "      <td>54127.017150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>34.530898</td>\n",
       "      <td>30221.937140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25.313695</td>\n",
       "      <td>43763.997580</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46.540708</td>\n",
       "      <td>48663.494680</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>37.716582</td>\n",
       "      <td>64695.977000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25.056782</td>\n",
       "      <td>17759.933850</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>41.095960</td>\n",
       "      <td>49463.381300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24.761563</td>\n",
       "      <td>38484.423180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>48.870175</td>\n",
       "      <td>34774.444070</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>27.833832</td>\n",
       "      <td>51644.493300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>17.103576</td>\n",
       "      <td>61661.765190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25.597796</td>\n",
       "      <td>53630.763770</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>42.249190</td>\n",
       "      <td>32215.099500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>37.135922</td>\n",
       "      <td>51733.657220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>32.443111</td>\n",
       "      <td>51677.699040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19.936674</td>\n",
       "      <td>34263.788530</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>39.820539</td>\n",
       "      <td>70503.313200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30.728855</td>\n",
       "      <td>67022.273020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>24.043583</td>\n",
       "      <td>36474.709700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>39.056036</td>\n",
       "      <td>29178.361010</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>30.434035</td>\n",
       "      <td>40446.847990</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>28.274339</td>\n",
       "      <td>82395.636880</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>40.736279</td>\n",
       "      <td>35323.201010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>23.556676</td>\n",
       "      <td>43072.567540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>37.843073</td>\n",
       "      <td>57841.359800</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>44.893467</td>\n",
       "      <td>71840.404550</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>23.277282</td>\n",
       "      <td>40765.043790</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>37.014895</td>\n",
       "      <td>73057.244510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>25.087617</td>\n",
       "      <td>61653.383110</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>20.706101</td>\n",
       "      <td>7147.723544</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>42.190279</td>\n",
       "      <td>48020.567990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>42.885297</td>\n",
       "      <td>58162.284990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>22.439488</td>\n",
       "      <td>35917.848190</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>37.674058</td>\n",
       "      <td>62034.486020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>20.420892</td>\n",
       "      <td>40114.156210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>36.902378</td>\n",
       "      <td>32583.024180</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>33.349037</td>\n",
       "      <td>52020.916460</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>41.544304</td>\n",
       "      <td>55792.698510</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>43.281586</td>\n",
       "      <td>54123.646500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>24.331054</td>\n",
       "      <td>59873.517000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>23.865371</td>\n",
       "      <td>23439.867820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>34.828303</td>\n",
       "      <td>50151.326220</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>37.173216</td>\n",
       "      <td>63750.415580</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>47329.565840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>24.615228</td>\n",
       "      <td>34721.748620</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>19.222746</td>\n",
       "      <td>44969.134950</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>22.665662</td>\n",
       "      <td>41686.204250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>35.320239</td>\n",
       "      <td>23989.808640</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>26.539170</td>\n",
       "      <td>31708.570540</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>32.386148</td>\n",
       "      <td>74331.354420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>499 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Time Spent on Site        Salary  Clicked\n",
       "0             25.649648  55330.060060        0\n",
       "1             32.456107  79049.076740        1\n",
       "2             20.945978  41098.608260        0\n",
       "3             54.039325  37143.355360        1\n",
       "4             34.249729  37355.112760        0\n",
       "5             52.009946  80642.652220        1\n",
       "6             42.881370  61908.398150        1\n",
       "7             25.676920  29851.411530        0\n",
       "8             40.698233  34281.118780        1\n",
       "9             29.687362  41098.878810        0\n",
       "10            33.272572  54127.017150        0\n",
       "11            34.530898  30221.937140        0\n",
       "12            25.313695  43763.997580        0\n",
       "13            46.540708  48663.494680        1\n",
       "14            37.716582  64695.977000        1\n",
       "15            25.056782  17759.933850        0\n",
       "16            41.095960  49463.381300        1\n",
       "17            24.761563  38484.423180        0\n",
       "18            48.870175  34774.444070        1\n",
       "19            27.833832  51644.493300        0\n",
       "20            17.103576  61661.765190        0\n",
       "21            25.597796  53630.763770        0\n",
       "22            42.249190  32215.099500        1\n",
       "23            37.135922  51733.657220        1\n",
       "24            32.443111  51677.699040        0\n",
       "25            19.936674  34263.788530        0\n",
       "26            39.820539  70503.313200        1\n",
       "27            30.728855  67022.273020        1\n",
       "28            24.043583  36474.709700        1\n",
       "29            39.056036  29178.361010        0\n",
       "..                  ...           ...      ...\n",
       "469           30.434035  40446.847990        0\n",
       "470           28.274339  82395.636880        1\n",
       "471           40.736279  35323.201010        1\n",
       "472           23.556676  43072.567540        0\n",
       "473           37.843073  57841.359800        0\n",
       "474           44.893467  71840.404550        1\n",
       "475           23.277282  40765.043790        0\n",
       "476           37.014895  73057.244510        1\n",
       "477           25.087617  61653.383110        0\n",
       "478           20.706101   7147.723544        0\n",
       "479           42.190279  48020.567990        1\n",
       "480           42.885297  58162.284990        1\n",
       "481           22.439488  35917.848190        0\n",
       "482           37.674058  62034.486020        1\n",
       "483           20.420892  40114.156210        0\n",
       "484           36.902378  32583.024180        0\n",
       "485           33.349037  52020.916460        1\n",
       "486           41.544304  55792.698510        1\n",
       "487           43.281586  54123.646500        1\n",
       "488           24.331054  59873.517000        0\n",
       "489           23.865371  23439.867820        0\n",
       "490           34.828303  50151.326220        1\n",
       "491           37.173216  63750.415580        1\n",
       "492            5.000000  47329.565840        0\n",
       "493           24.615228  34721.748620        0\n",
       "494           19.222746  44969.134950        0\n",
       "495           22.665662  41686.204250        0\n",
       "496           35.320239  23989.808640        0\n",
       "497           26.539170  31708.570540        0\n",
       "498           32.386148  74331.354420        1\n",
       "\n",
       "[499 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's drop the target coloumn before we do train test split\n",
    "X = training_set.drop('Clicked',axis=1).values\n",
    "y = training_set['Clicked'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP#4: MODEL TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [ 1.38267785e+00,  7.78269562e-01],\n",
       "       [-2.16905779e+00, -1.51110615e+00],\n",
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       "       [ 3.30774731e-03, -5.14590296e-01],\n",
       "       [-5.55227833e-01, -1.54686351e+00],\n",
       "       [-1.14994093e+00, -2.41675560e+00],\n",
       "       [ 6.68147688e-02,  4.60377823e-01],\n",
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       "       [-5.78443863e-01, -1.74139851e+00],\n",
       "       [ 1.18473700e-01, -7.70880173e-01],\n",
       "       [-9.22546096e-01, -4.36985573e-02],\n",
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       "       [ 1.01503492e+00, -1.50611667e+00]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Logistic Regression to the Training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression(random_state = 0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP#5: MODEL TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_train = classifier.predict(X_train)\n",
    "y_predict_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
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       "       0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14541187780>"
      ]
     },
     "execution_count": 24,
     "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": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "cm = confusion_matrix(y_train, y_predict_train)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_test = classifier.predict(X_test)\n",
    "y_predict_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14541221358>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD8CAYAAAA2Y2wxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAECJJREFUeJzt3XuwXWV5x/Hvk4CSgJVoAkMukKSi1EurM8KgVkHkkkJBHG+IMAwNPQ6ORcFirIgZHKiAFSq1OB6FIgMEhaCoI6hjTcNFEkLQSLg0FAXDxYBiKwFDzj5P/zg7zDHJOXufZL977bPy/WTWzNl77b3OM0z48fCsd60VmYkkqZwJVRcgSXVn0EpSYQatJBVm0EpSYQatJBVm0EpSYQatJBVm0EpSYQatJBW2U+lfsPGph7z0TFuYNP2tVZegHjTw/KOxvccYS+bsPHXudv++dtjRSlJhxTtaSeqqwUbVFWzBoJVUL42BqivYgkErqVYyB6suYQsGraR6GTRoJaksO1pJKsyTYZJUmB2tJJWVrjqQpMI8GSZJhTk6kKTCPBkmSYX1YEfrTWUk1UtjoP2tDRExMSLujojvNV/PiYhlEbEmIr4RES9qdQyDVlK9DA62v7Xno8B9w15fAFycmfsCTwPzWx3AoJVUK5mNtrdWImImcBTwtebrAA4Brm9+5OvAsa2O44xWUr10dkb7r8AngJc0X78c+H1mbpo7rAVmtDqIHa2kehnD6CAi+iJixbCtb9NhIuJvgXWZedewo2/tiQwtn+hgRyupXsbQ0WZmP9A/wu63AMdExJHALsCfMdTh7h4ROzW72pnAY61+jx2tpHppbGx/G0Vm/lNmzszM2cBxwH9m5geBnwDvaX7sJODGViUZtJLqpfOrDja3ADgjIh5kaGZ7WasvODqQVC8FLljIzCXAkubPDwEHjOX7Bq2kevGmMpJUmEErSWVli5NcVTBoJdVLD95UxqCVVC+ODiSpMDtaSSrMjlaSCrOjlaTCBnwKriSVZUcrSYU5o5WkwuxoJakwO1pJKsyOVpIKc9WBJBWWLR/h1XUGraR6cUYrSYUZtJJUmCfDJKmwRqPqCrZg0EqqF0cHklSYQStJhTmjlaSyctB1tJJUlqMDSSrMVQeSVJgd7Y6l0Wjw/vmnsce0qVz6+XM4+3MXs/r+NWQms2fN4LyzPs7kyZOqLlMVevC/7+APzzxDozHIwMAAB77pyKpLGv8M2h3LVdfdyNzZe/PM+mcBWHBaH7vtuisAF17SzzWLv8spJ76vyhLVAw497L389rdPV11GffTgTWUmtPpAROwXEQsi4pKI+GLz57/oRnHj2RPrnmTp7ct599FHvPDeppDNTP64YQMRVVUn1djgYPtbl4watBGxALgWCGA5cGfz50UR8cny5Y1fF3zxK5zx4flE/Ok/4k+fdxEHHX08v3x4Lce/55iKqlOvyExu+v4ilt1xE6fM/2DV5dTDYLa/dUmr0cF84DWZuXH4mxFxEbAaOL9UYePZktuW8bIpu/Oa/fZl+cpVf7Lv3LPOoNFo8M8Xf5mbf7yUdx11eEVVqhe87eBjefzx3zBt2su5+aZreeCBB7nl1mVVlzW+9eCqg1ajg0Fg+lbe36u5b6sioi8iVkTEiq9duWh76huX7l51L0tuvYPD330SZy48n+V3/ZwF51z4wv6JEycy7x1v40dLbquwSvWCxx//DQBPPvlbbrzxJvbf//UVVzT+5eBg21u3tOpoPwb8OCLWAL9uvrc38ArgIyN9KTP7gX6AjU891HuT6cJOP/VkTj/1ZACWr1zFFYsWc/5nzuSRtY+x98zpZCZLblvGnH1mVlypqjR58iQmTJjAM8+sZ/LkSRx26EGce97FVZc1/o23K8My8+aIeCVwADCDofnsWuDOzOy9/ryHZSafOvcLrF//LJnJq14xh7PPHPG/VdoB7LnnNK6/7jIAdtppItde+21+8MMl1RZVBz14r4PIwkshdsSOVq1Nmv7WqktQDxp4/tHtXouz/rMfbDtzdv3M1V1Z++M6Wkn1MtB7/7Nt0Eqqlw6NDiJiF2Ap8GKGsvL6zFwYEVcDbwQ2MrTs9UObr8zaXMsLFiRpXOncOtoNwCGZ+VfA64F5EXEgcDWwH/A6YBJwSqsD2dFKqpVOLdvKoRNYzzRf7tzcMjO/v+kzEbEcaLl8yI5WUr2MoaMdvua/ufUNP1RETIyInwHrgB9l5rJh+3YGTgRublWSHa2kehnDOtrha/5H2N8AXh8RuwPfiojXZuY9zd2XAksz85ZWv8eglVQvBS7BzczfR8QSYB5wT0QsBKYBH2rn+44OJNVKDmbb22giYlqzkyUiJgGHAvdHxCnAEcAHMttb4mBHK6leOncJ7l7A1yNiIkNN6Tcz83sRMQA8DPw0hu51ekNmfna0Axm0kuqlc6sOVgFv2Mr7Y85Ng1ZSvYy3m8pI0rhj0EpSWdnovbt3GbSS6sWOVpLKarVsqwoGraR6MWglqbDeG9EatJLqJQd6L2kNWkn10ns5a9BKqhdPhklSaXa0klSWHa0klWZHK0ll5UDVFWzJoJVUKx162nhHGbSS6sWglaSy7GglqTCDVpIKy0ZUXcIWDFpJtWJHK0mF5aAdrSQVZUcrSYVl2tFKUlF2tJJU2KCrDiSpLE+GSVJhBq0kFZa9dztag1ZSvdjRSlJhLu+SpMIarjqQpLLsaCWpMGe0klSYqw4kqTA7WkkqrDE4oeoSttB7FUnSdshsfxtNRMyKiJ9ExH0RsToiPrrZ/n+MiIyIqa1qsqOVVCuDnVt1MAB8PDNXRsRLgLsi4keZeW9EzAIOAx5p50B2tJJqJTPa3kY/Tj6emSubP/8BuA+Y0dx9MfAJoK1Tb3a0kmqlxKqDiJgNvAFYFhHHAI9m5s8j2uueiwft6179/tK/QuPQc4/dUnUJqqmxjA4iog/oG/ZWf2b2b/aZ3YDFwMcYGiecBRw+lprsaCXVylhWHTRDtX+k/RGxM0Mhe3Vm3hARrwPmAJu62ZnAyog4IDOfGOk4Bq2kWunU5CCGkvQy4L7MvAggM38B7DHsM78C3piZT412LE+GSaqVwYy2txbeApwIHBIRP2tuR25LTXa0kmqlUzeVycxbgVEPlpmz2zmWQSupVnrwIbgGraR6ydGb0EoYtJJqZcD70UpSWXa0klSYM1pJKsyOVpIKs6OVpMIadrSSVFYPPsnGoJVUL4N2tJJUVg8+BNeglVQvngyTpMIG23zqQTcZtJJqpVF1AVth0EqqFVcdSFJhrjqQpMJcdSBJhTk6kKTCXN4lSYU17GglqSw7WkkqzKCVpMJ68JFhBq2kerGjlaTCvARXkgpzHa0kFeboQJIKM2glqTDvdSBJhTmjlaTCXHUgSYUN9uDwwKCVVCueDJOkwnqvnzVoJdWMHa0kFTYQvdfTGrSSaqX3YhYmVF2AJHXS4Bi2ViLi8ohYFxH3bPb+P0TEAxGxOiIubHUcO1pJtdLh5V1XAF8Crtz0RkS8HXgn8JeZuSEi9mh1EDtaSbWSY9haHitzKfC7zd4+FTg/Mzc0P7Ou1XEMWkm10snRwQheCbw1IpZFxH9FxP6tvuDoQFKtNMYwOoiIPqBv2Fv9mdnf4ms7AVOAA4H9gW9GxNzMHPEXG7SSamUsnWozVFsF6+bWAjc0g3V5RAwCU4EnR/qCowNJtZJj+LONvg0cAhARrwReBDw12hfsaCXVSievDIuIRcDBwNSIWAssBC4HLm8u+XoeOGm0sQEYtF1z4t8fx3tPOJaI4Lqrvs2V/YuqLkkVaTQavH/+aewxbSqXfv4czv7cxay+fw2ZyexZMzjvrI8zefKkqssctzq5vCszPzDCrhPGchxHB12w735/zntPOJb3zTuJY99+PAcf/tfsM2dW1WWpIldddyNzZ+/9wusFp/Vxw9cv5VtXfpm99tyDaxZ/t8Lqxr9OLu/qFIO2C+buO5uf3/UL/vjcBhqNBnfevpJDjzq46rJUgSfWPcnS25fz7qOPeOG93XbdFYDM5I8bNhA9+ISA8WSAbHvrlm0O2og4uZOF1Nma+/+H/d/0Bnaf8lJ2mfRiDjr0zew1fc+qy1IFLvjiVzjjw/OJ+NN/9T593kUcdPTx/PLhtRz/nmMqqq4eunAybMy2p6M9Z6QdEdEXESsiYsXvnxtxxcMO46E1v+Kr/3Yll133Jb567SXcv3oNAwO9+MANlbTktmW8bMruvGa/fbfYd+5ZZ/CTG69i7uxZ3PzjpRVUVx9duGBhzEY9GRYRq0baBYzYkg1fm7bfHvv34s10um7xNd9h8TXfAeD0T32YJx5vedWeaubuVfey5NY7uOWnd7Lh+Y2sX/8sC865kAsWfgKAiRMnMu8db+M/rlnMu446vOJqx69udqrtarXqYE/gCODpzd4P4PYiFdXUy6ZO4XdPPc1eM/bksKPeznFH/l3VJanLTj/1ZE4/dWjitnzlKq5YtJjzP3Mmj6x9jL1nTiczWXLbMubsM7PiSse38Xjj7+8Bu2XmzzbfERFLilRUU5dcfgG7T3kpAwMDfPaTF/J///uHqktSD8hMPnXuF1i//lkyk1e9Yg5nn/mRqssa1xqjL2mtRLRYZ7vdHB1oa35x7zeqLkE9aOepc7d7zcXx+7yr7cy55uFvdWWNhxcsSKqV8TijlaRxZTzOaCVpXOnwExY6wqCVVCuODiSpsF5cdWDQSqoVRweSVJgnwySpMGe0klSYowNJKqz01a7bwqCVVCtjedx4txi0kmrF0YEkFeboQJIKs6OVpMJc3iVJhXkJriQV5uhAkgozaCWpMFcdSFJhdrSSVJirDiSpsEb23o0SDVpJteKMVpIKc0YrSYU5o5WkwgYdHUhSWXa0klSYqw4kqbBeHB1MqLoASeqkHMOfViLi9IhYHRH3RMSiiNhlW2oyaCXVymBm29toImIGcBrwxsx8LTAROG5banJ0IKlWOnwybCdgUkRsBCYDj23rQSSpNhrZ6MhxMvPRiPgX4BHgOeCHmfnDbTmWowNJtZKZbW8R0RcRK4ZtfZuOExFTgHcCc4DpwK4RccK21GRHK6lWxnIJbmb2A/0j7D4U+GVmPgkQETcAbwauGmtNBq2kWungTWUeAQ6MiMkMjQ7eAazYlgMZtJJqpVPraDNzWURcD6wEBoC7Gbn7HZVBK6lWOrnqIDMXAgu39zgGraRa8RJcSSrMG39LUmG9eK8Dg1ZSrdjRSlJhPspGkgqzo5Wkwlx1IEmFeTJMkgpzdCBJhflwRkkqzI5WkgrrxRlt9GL611VE9DXvfym9wL8X9ecTFrqrr/VHtAPy70XNGbSSVJhBK0mFGbTd5RxOW+Pfi5rzZJgkFWZHK0mFGbRdEhHzIuKBiHgwIj5ZdT2qXkRcHhHrIuKeqmtRWQZtF0TERODfgb8BXg18ICJeXW1V6gFXAPOqLkLlGbTdcQDwYGY+lJnPA9cC76y4JlUsM5cCv6u6DpVn0HbHDODXw16vbb4naQdg0HZHbOU9l3tIOwiDtjvWArOGvZ4JPFZRLZK6zKDtjjuBfSNiTkS8CDgO+E7FNUnqEoO2CzJzAPgI8APgPuCbmbm62qpUtYhYBPwUeFVErI2I+VXXpDK8MkySCrOjlaTCDFpJKsyglaTCDFpJKsyglaTCDFpJKsyglaTCDFpJKuz/AToN9w2a5d2hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_predict_test)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.83      0.90      0.86        48\n",
      "          1       0.90      0.83      0.86        52\n",
      "\n",
      "avg / total       0.86      0.86      0.86       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_predict_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# STEP #6: VISUALIZING TRAINING AND TESTING DATASETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "\n",
    "# Create a meshgrid ranging from the minimum to maximum value for both features\n",
    "\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(399, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(715, 740)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.787379255660188, 3.3526207443396596)"
      ]
     },
     "execution_count": 32,
     "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": [
    "# plot the boundary using the trained classifier\n",
    "# Run the classifier to predict the outcome on all pixels with resolution of 0.01\n",
    "# Colouring the pixels with 0 or 1\n",
    "# If classified as 0 it will be magenta, and if it is classified as 1 it will be shown in blue \n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('magenta', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all the actual training points\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('magenta', 'blue'))(i), label = j)\n",
    "    \n",
    "plt.title('Facebook Ad: Customer Click Prediction (Training set)')\n",
    "plt.xlabel('Time Spent on Site')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('magenta', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('magenta', 'blue'))(i), label = j)\n",
    "plt.title('Facebook Ad: Customer Click Prediction (Training set)')\n",
    "plt.xlabel('Time Spent on Site')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "from matplotlib.colors import ListedColormap\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('magenta', 'blue')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('magenta', 'blue'))(i), label = j)\n",
    "plt.title('Facebook Ad: Customer Click Prediction (Testing set)')\n",
    "plt.xlabel('Time Spent on Site')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXCELLENT JOB! NOW YOU BECOME EXPERT IN LOGISTIC REGRESSION"
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}