Artificial Neural Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>15574012</td>\n",
       "      <td>Chu</td>\n",
       "      <td>645</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>8</td>\n",
       "      <td>113755.78</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>149756.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>15592531</td>\n",
       "      <td>Bartlett</td>\n",
       "      <td>822</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10062.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>15656148</td>\n",
       "      <td>Obinna</td>\n",
       "      <td>376</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>115046.74</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119346.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>15792365</td>\n",
       "      <td>He</td>\n",
       "      <td>501</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>142051.07</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>15592389</td>\n",
       "      <td>H?</td>\n",
       "      <td>684</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>134603.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>71725.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>15767821</td>\n",
       "      <td>Bearce</td>\n",
       "      <td>528</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>6</td>\n",
       "      <td>102016.72</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80181.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15737173</td>\n",
       "      <td>Andrews</td>\n",
       "      <td>497</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76390.01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>15632264</td>\n",
       "      <td>Kay</td>\n",
       "      <td>476</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26260.98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>15691483</td>\n",
       "      <td>Chin</td>\n",
       "      <td>549</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>190857.79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15600882</td>\n",
       "      <td>Scott</td>\n",
       "      <td>635</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>65951.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>15643966</td>\n",
       "      <td>Goforth</td>\n",
       "      <td>616</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>143129.41</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>64327.26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>15737452</td>\n",
       "      <td>Romeo</td>\n",
       "      <td>653</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>132602.88</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5097.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>15788218</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>549</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14406.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>15661507</td>\n",
       "      <td>Muldrow</td>\n",
       "      <td>587</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158684.81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>15568982</td>\n",
       "      <td>Hao</td>\n",
       "      <td>726</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>54724.03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>15577657</td>\n",
       "      <td>McDonald</td>\n",
       "      <td>732</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>170886.17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>15597945</td>\n",
       "      <td>Dellucci</td>\n",
       "      <td>636</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>138555.46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>15699309</td>\n",
       "      <td>Gerasimov</td>\n",
       "      <td>510</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>118913.53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>15725737</td>\n",
       "      <td>Mosman</td>\n",
       "      <td>669</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8487.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>15625047</td>\n",
       "      <td>Yen</td>\n",
       "      <td>846</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>187616.16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>15738191</td>\n",
       "      <td>Maclean</td>\n",
       "      <td>577</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>124508.29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>15736816</td>\n",
       "      <td>Young</td>\n",
       "      <td>756</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>136815.64</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>170041.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>15700772</td>\n",
       "      <td>Nebechi</td>\n",
       "      <td>571</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38433.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>15728693</td>\n",
       "      <td>McWilliams</td>\n",
       "      <td>574</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>141349.43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>100187.43</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>15656300</td>\n",
       "      <td>Lucciano</td>\n",
       "      <td>411</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>59697.17</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53483.21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>9971</td>\n",
       "      <td>15587133</td>\n",
       "      <td>Thompson</td>\n",
       "      <td>518</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>151027.05</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>119377.36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>9972</td>\n",
       "      <td>15721377</td>\n",
       "      <td>Chou</td>\n",
       "      <td>833</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>144751.81</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166472.81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>9973</td>\n",
       "      <td>15747927</td>\n",
       "      <td>Ch'in</td>\n",
       "      <td>758</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>155739.76</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>171552.02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>9974</td>\n",
       "      <td>15806455</td>\n",
       "      <td>Miller</td>\n",
       "      <td>611</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157474.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>9975</td>\n",
       "      <td>15695474</td>\n",
       "      <td>Barker</td>\n",
       "      <td>583</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>122531.86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13549.24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>9976</td>\n",
       "      <td>15666295</td>\n",
       "      <td>Smith</td>\n",
       "      <td>610</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>113957.01</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>196526.55</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>9977</td>\n",
       "      <td>15656062</td>\n",
       "      <td>Azikiwe</td>\n",
       "      <td>637</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>103377.81</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>84419.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>9978</td>\n",
       "      <td>15579969</td>\n",
       "      <td>Mancini</td>\n",
       "      <td>683</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24991.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>9979</td>\n",
       "      <td>15703563</td>\n",
       "      <td>P'eng</td>\n",
       "      <td>774</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>9</td>\n",
       "      <td>93017.47</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>191608.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>9980</td>\n",
       "      <td>15692664</td>\n",
       "      <td>Diribe</td>\n",
       "      <td>677</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>90022.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2988.28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>9981</td>\n",
       "      <td>15719276</td>\n",
       "      <td>T'ao</td>\n",
       "      <td>741</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>74371.49</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99595.67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>9982</td>\n",
       "      <td>15672754</td>\n",
       "      <td>Burbidge</td>\n",
       "      <td>498</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>152039.70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53445.17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>9983</td>\n",
       "      <td>15768163</td>\n",
       "      <td>Griffin</td>\n",
       "      <td>655</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>137145.12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115146.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>9984</td>\n",
       "      <td>15656710</td>\n",
       "      <td>Cocci</td>\n",
       "      <td>613</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>151325.24</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>9985</td>\n",
       "      <td>15696175</td>\n",
       "      <td>Echezonachukwu</td>\n",
       "      <td>602</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>90602.42</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>51695.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>9986</td>\n",
       "      <td>15586914</td>\n",
       "      <td>Nepean</td>\n",
       "      <td>659</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>123841.49</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96833.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>9987</td>\n",
       "      <td>15581736</td>\n",
       "      <td>Bartlett</td>\n",
       "      <td>673</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>183579.54</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34047.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>9988</td>\n",
       "      <td>15588839</td>\n",
       "      <td>Mancini</td>\n",
       "      <td>606</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>180307.73</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1914.41</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>9989</td>\n",
       "      <td>15589329</td>\n",
       "      <td>Pirozzi</td>\n",
       "      <td>775</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49337.84</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>9990</td>\n",
       "      <td>15605622</td>\n",
       "      <td>McMillan</td>\n",
       "      <td>841</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>179436.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>9991</td>\n",
       "      <td>15798964</td>\n",
       "      <td>Nkemakonam</td>\n",
       "      <td>714</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>35016.60</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53667.08</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>9992</td>\n",
       "      <td>15769959</td>\n",
       "      <td>Ajuluchukwu</td>\n",
       "      <td>597</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>88381.21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>69384.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>9993</td>\n",
       "      <td>15657105</td>\n",
       "      <td>Chukwualuka</td>\n",
       "      <td>726</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>195192.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>9994</td>\n",
       "      <td>15569266</td>\n",
       "      <td>Rahman</td>\n",
       "      <td>644</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>155060.41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29179.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>9995</td>\n",
       "      <td>15719294</td>\n",
       "      <td>Wood</td>\n",
       "      <td>800</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>167773.55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId         Surname  CreditScore Geography  Gender  \\\n",
       "0             1    15634602        Hargrave          619    France  Female   \n",
       "1             2    15647311            Hill          608     Spain  Female   \n",
       "2             3    15619304            Onio          502    France  Female   \n",
       "3             4    15701354            Boni          699    France  Female   \n",
       "4             5    15737888        Mitchell          850     Spain  Female   \n",
       "5             6    15574012             Chu          645     Spain    Male   \n",
       "6             7    15592531        Bartlett          822    France    Male   \n",
       "7             8    15656148          Obinna          376   Germany  Female   \n",
       "8             9    15792365              He          501    France    Male   \n",
       "9            10    15592389              H?          684    France    Male   \n",
       "10           11    15767821          Bearce          528    France    Male   \n",
       "11           12    15737173         Andrews          497     Spain    Male   \n",
       "12           13    15632264             Kay          476    France  Female   \n",
       "13           14    15691483            Chin          549    France  Female   \n",
       "14           15    15600882           Scott          635     Spain  Female   \n",
       "15           16    15643966         Goforth          616   Germany    Male   \n",
       "16           17    15737452           Romeo          653   Germany    Male   \n",
       "17           18    15788218       Henderson          549     Spain  Female   \n",
       "18           19    15661507         Muldrow          587     Spain    Male   \n",
       "19           20    15568982             Hao          726    France  Female   \n",
       "20           21    15577657        McDonald          732    France    Male   \n",
       "21           22    15597945        Dellucci          636     Spain  Female   \n",
       "22           23    15699309       Gerasimov          510     Spain  Female   \n",
       "23           24    15725737          Mosman          669    France    Male   \n",
       "24           25    15625047             Yen          846    France  Female   \n",
       "25           26    15738191         Maclean          577    France    Male   \n",
       "26           27    15736816           Young          756   Germany    Male   \n",
       "27           28    15700772         Nebechi          571    France    Male   \n",
       "28           29    15728693      McWilliams          574   Germany  Female   \n",
       "29           30    15656300        Lucciano          411    France    Male   \n",
       "...         ...         ...             ...          ...       ...     ...   \n",
       "9970       9971    15587133        Thompson          518    France    Male   \n",
       "9971       9972    15721377            Chou          833    France  Female   \n",
       "9972       9973    15747927           Ch'in          758    France    Male   \n",
       "9973       9974    15806455          Miller          611    France    Male   \n",
       "9974       9975    15695474          Barker          583    France    Male   \n",
       "9975       9976    15666295           Smith          610   Germany    Male   \n",
       "9976       9977    15656062         Azikiwe          637    France  Female   \n",
       "9977       9978    15579969         Mancini          683    France  Female   \n",
       "9978       9979    15703563           P'eng          774    France    Male   \n",
       "9979       9980    15692664          Diribe          677    France  Female   \n",
       "9980       9981    15719276            T'ao          741     Spain    Male   \n",
       "9981       9982    15672754        Burbidge          498   Germany    Male   \n",
       "9982       9983    15768163         Griffin          655   Germany  Female   \n",
       "9983       9984    15656710           Cocci          613    France    Male   \n",
       "9984       9985    15696175  Echezonachukwu          602   Germany    Male   \n",
       "9985       9986    15586914          Nepean          659    France    Male   \n",
       "9986       9987    15581736        Bartlett          673   Germany    Male   \n",
       "9987       9988    15588839         Mancini          606     Spain    Male   \n",
       "9988       9989    15589329         Pirozzi          775    France    Male   \n",
       "9989       9990    15605622        McMillan          841     Spain    Male   \n",
       "9990       9991    15798964      Nkemakonam          714   Germany    Male   \n",
       "9991       9992    15769959     Ajuluchukwu          597    France  Female   \n",
       "9992       9993    15657105     Chukwualuka          726     Spain    Male   \n",
       "9993       9994    15569266          Rahman          644    France    Male   \n",
       "9994       9995    15719294            Wood          800    France  Female   \n",
       "9995       9996    15606229        Obijiaku          771    France    Male   \n",
       "9996       9997    15569892       Johnstone          516    France    Male   \n",
       "9997       9998    15584532             Liu          709    France  Female   \n",
       "9998       9999    15682355       Sabbatini          772   Germany    Male   \n",
       "9999      10000    15628319          Walker          792    France  Female   \n",
       "\n",
       "      Age  Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0      42       2       0.00              1          1               1   \n",
       "1      41       1   83807.86              1          0               1   \n",
       "2      42       8  159660.80              3          1               0   \n",
       "3      39       1       0.00              2          0               0   \n",
       "4      43       2  125510.82              1          1               1   \n",
       "5      44       8  113755.78              2          1               0   \n",
       "6      50       7       0.00              2          1               1   \n",
       "7      29       4  115046.74              4          1               0   \n",
       "8      44       4  142051.07              2          0               1   \n",
       "9      27       2  134603.88              1          1               1   \n",
       "10     31       6  102016.72              2          0               0   \n",
       "11     24       3       0.00              2          1               0   \n",
       "12     34      10       0.00              2          1               0   \n",
       "13     25       5       0.00              2          0               0   \n",
       "14     35       7       0.00              2          1               1   \n",
       "15     45       3  143129.41              2          0               1   \n",
       "16     58       1  132602.88              1          1               0   \n",
       "17     24       9       0.00              2          1               1   \n",
       "18     45       6       0.00              1          0               0   \n",
       "19     24       6       0.00              2          1               1   \n",
       "20     41       8       0.00              2          1               1   \n",
       "21     32       8       0.00              2          1               0   \n",
       "22     38       4       0.00              1          1               0   \n",
       "23     46       3       0.00              2          0               1   \n",
       "24     38       5       0.00              1          1               1   \n",
       "25     25       3       0.00              2          0               1   \n",
       "26     36       2  136815.64              1          1               1   \n",
       "27     44       9       0.00              2          0               0   \n",
       "28     43       3  141349.43              1          1               1   \n",
       "29     29       0   59697.17              2          1               1   \n",
       "...   ...     ...        ...            ...        ...             ...   \n",
       "9970   42       7  151027.05              2          1               0   \n",
       "9971   34       3  144751.81              1          0               0   \n",
       "9972   26       4  155739.76              1          1               0   \n",
       "9973   27       7       0.00              2          1               1   \n",
       "9974   33       7  122531.86              1          1               0   \n",
       "9975   50       1  113957.01              2          1               0   \n",
       "9976   33       7  103377.81              1          1               0   \n",
       "9977   32       9       0.00              2          1               1   \n",
       "9978   40       9   93017.47              2          1               0   \n",
       "9979   58       1   90022.85              1          0               1   \n",
       "9980   35       6   74371.49              1          0               0   \n",
       "9981   42       3  152039.70              1          1               1   \n",
       "9982   46       7  137145.12              1          1               0   \n",
       "9983   40       4       0.00              1          0               0   \n",
       "9984   35       7   90602.42              2          1               1   \n",
       "9985   36       6  123841.49              2          1               0   \n",
       "9986   47       1  183579.54              2          0               1   \n",
       "9987   30       8  180307.73              2          1               1   \n",
       "9988   30       4       0.00              2          1               0   \n",
       "9989   28       4       0.00              2          1               1   \n",
       "9990   33       3   35016.60              1          1               0   \n",
       "9991   53       4   88381.21              1          1               0   \n",
       "9992   36       2       0.00              1          1               0   \n",
       "9993   28       7  155060.41              1          1               0   \n",
       "9994   29       2       0.00              2          0               0   \n",
       "9995   39       5       0.00              2          1               0   \n",
       "9996   35      10   57369.61              1          1               1   \n",
       "9997   36       7       0.00              1          0               1   \n",
       "9998   42       3   75075.31              2          1               0   \n",
       "9999   28       4  130142.79              1          1               0   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "0           101348.88       1  \n",
       "1           112542.58       0  \n",
       "2           113931.57       1  \n",
       "3            93826.63       0  \n",
       "4            79084.10       0  \n",
       "5           149756.71       1  \n",
       "6            10062.80       0  \n",
       "7           119346.88       1  \n",
       "8            74940.50       0  \n",
       "9            71725.73       0  \n",
       "10           80181.12       0  \n",
       "11           76390.01       0  \n",
       "12           26260.98       0  \n",
       "13          190857.79       0  \n",
       "14           65951.65       0  \n",
       "15           64327.26       0  \n",
       "16            5097.67       1  \n",
       "17           14406.41       0  \n",
       "18          158684.81       0  \n",
       "19           54724.03       0  \n",
       "20          170886.17       0  \n",
       "21          138555.46       0  \n",
       "22          118913.53       1  \n",
       "23            8487.75       0  \n",
       "24          187616.16       0  \n",
       "25          124508.29       0  \n",
       "26          170041.95       0  \n",
       "27           38433.35       0  \n",
       "28          100187.43       0  \n",
       "29           53483.21       0  \n",
       "...               ...     ...  \n",
       "9970        119377.36       0  \n",
       "9971        166472.81       0  \n",
       "9972        171552.02       0  \n",
       "9973        157474.10       0  \n",
       "9974         13549.24       0  \n",
       "9975        196526.55       1  \n",
       "9976         84419.78       0  \n",
       "9977         24991.92       0  \n",
       "9978        191608.97       0  \n",
       "9979          2988.28       0  \n",
       "9980         99595.67       0  \n",
       "9981         53445.17       1  \n",
       "9982        115146.40       1  \n",
       "9983        151325.24       0  \n",
       "9984         51695.41       0  \n",
       "9985         96833.00       0  \n",
       "9986         34047.54       0  \n",
       "9987          1914.41       0  \n",
       "9988         49337.84       0  \n",
       "9989        179436.60       0  \n",
       "9990         53667.08       0  \n",
       "9991         69384.71       1  \n",
       "9992        195192.40       0  \n",
       "9993         29179.52       0  \n",
       "9994        167773.55       0  \n",
       "9995         96270.64       0  \n",
       "9996        101699.77       0  \n",
       "9997         42085.58       1  \n",
       "9998         92888.52       1  \n",
       "9999         38190.78       0  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, 3:13].values\n",
    "y = dataset.iloc[:, 13].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labelencoder_X_1 = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labelencoder_X_2 = LabelEncoder()\n",
    "X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "onehotencoder = OneHotEncoder(categorical_features = [1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = onehotencoder.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X[:, 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the Dataset into the Training Set and Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": "markdown",
   "metadata": {},
   "source": [
    "# Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.5698444 ,  1.74309049,  0.16958176, ...,  0.64259497,\n",
       "        -1.03227043,  1.10643166],\n",
       "       [ 1.75486502, -0.57369368, -2.30455945, ...,  0.64259497,\n",
       "         0.9687384 , -0.74866447],\n",
       "       [-0.5698444 , -0.57369368, -1.19119591, ...,  0.64259497,\n",
       "        -1.03227043,  1.48533467],\n",
       "       ..., \n",
       "       [-0.5698444 , -0.57369368,  0.9015152 , ...,  0.64259497,\n",
       "        -1.03227043,  1.41231994],\n",
       "       [-0.5698444 ,  1.74309049, -0.62420521, ...,  0.64259497,\n",
       "         0.9687384 ,  0.84432121],\n",
       "       [ 1.75486502, -0.57369368, -0.28401079, ...,  0.64259497,\n",
       "        -1.03227043,  0.32472465]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = sc.fit_transform(X_train)\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.62776996, -0.57427105, -0.56129438, ...,  0.66011376,\n",
       "         0.97628121,  1.62185911],\n",
       "       [-0.61433742, -0.57427105, -1.33847768, ...,  0.66011376,\n",
       "        -1.02429504,  0.504204  ],\n",
       "       [-0.61433742,  1.74133801,  0.58347561, ...,  0.66011376,\n",
       "         0.97628121, -0.41865644],\n",
       "       ..., \n",
       "       [-0.61433742,  1.74133801, -0.76084144, ...,  0.66011376,\n",
       "        -1.02429504,  0.72775202],\n",
       "       [ 1.62776996, -0.57427105, -0.0046631 , ...,  0.66011376,\n",
       "         0.97628121, -1.54162886],\n",
       "       [ 1.62776996, -0.57427105, -0.81335383, ...,  0.66011376,\n",
       "        -1.02429504,  1.62356528]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = sc.fit_transform(X_test)\n",
    "X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Now let's make the ANN!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Keras Libraries and Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialising the ANN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding the Input Layer and the First Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/atse/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=11, units=6, kernel_initializer=\"uniform\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 11))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Second Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/atse/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=6, kernel_initializer=\"uniform\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Output Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/atse/miniconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"uniform\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting the ANN to the Training Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/atse/miniconda3/lib/python3.6/site-packages/keras/models.py:837: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
      "  warnings.warn('The `nb_epoch` argument in `fit` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4899 - acc: 0.7949     \n",
      "Epoch 2/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4279 - acc: 0.7960     \n",
      "Epoch 3/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4220 - acc: 0.7960     \n",
      "Epoch 4/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4178 - acc: 0.8224     \n",
      "Epoch 5/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4158 - acc: 0.8274     \n",
      "Epoch 6/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4139 - acc: 0.8292     \n",
      "Epoch 7/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4127 - acc: 0.8312     \n",
      "Epoch 8/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4110 - acc: 0.8331     \n",
      "Epoch 9/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4100 - acc: 0.8327     \n",
      "Epoch 10/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4087 - acc: 0.8330     \n",
      "Epoch 11/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4082 - acc: 0.8342     \n",
      "Epoch 12/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4075 - acc: 0.8339     \n",
      "Epoch 13/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4063 - acc: 0.8334     \n",
      "Epoch 14/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4061 - acc: 0.8347     \n",
      "Epoch 15/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4052 - acc: 0.8340     \n",
      "Epoch 16/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4051 - acc: 0.8356     \n",
      "Epoch 17/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4051 - acc: 0.8341     \n",
      "Epoch 18/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4041 - acc: 0.8346     \n",
      "Epoch 19/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4041 - acc: 0.8344     \n",
      "Epoch 20/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4034 - acc: 0.8345     \n",
      "Epoch 21/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4037 - acc: 0.8336     \n",
      "Epoch 22/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4034 - acc: 0.8346     \n",
      "Epoch 23/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4030 - acc: 0.8334     \n",
      "Epoch 24/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4023 - acc: 0.8355     \n",
      "Epoch 25/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4026 - acc: 0.8344     \n",
      "Epoch 26/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4019 - acc: 0.8339     \n",
      "Epoch 27/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4022 - acc: 0.8336     \n",
      "Epoch 28/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4021 - acc: 0.8335     \n",
      "Epoch 29/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4018 - acc: 0.8351     \n",
      "Epoch 30/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4017 - acc: 0.8361     \n",
      "Epoch 31/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4016 - acc: 0.8342     \n",
      "Epoch 32/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4011 - acc: 0.8347     \n",
      "Epoch 33/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4013 - acc: 0.8347     \n",
      "Epoch 34/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4011 - acc: 0.8356     \n",
      "Epoch 35/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4011 - acc: 0.8354     \n",
      "Epoch 36/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4010 - acc: 0.8361     \n",
      "Epoch 37/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4007 - acc: 0.8345     \n",
      "Epoch 38/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4009 - acc: 0.8349     \n",
      "Epoch 39/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4009 - acc: 0.8339     \n",
      "Epoch 40/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4006 - acc: 0.8361     \n",
      "Epoch 41/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4009 - acc: 0.8342     \n",
      "Epoch 42/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4000 - acc: 0.8355     \n",
      "Epoch 43/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4009 - acc: 0.8366     \n",
      "Epoch 44/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4006 - acc: 0.8342     \n",
      "Epoch 45/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4002 - acc: 0.8341     \n",
      "Epoch 46/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4000 - acc: 0.8361     \n",
      "Epoch 47/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4003 - acc: 0.8367     \n",
      "Epoch 48/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4001 - acc: 0.8337     \n",
      "Epoch 49/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3999 - acc: 0.8346     \n",
      "Epoch 50/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4003 - acc: 0.8354     \n",
      "Epoch 51/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3997 - acc: 0.8352     \n",
      "Epoch 52/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.4001 - acc: 0.8365     \n",
      "Epoch 53/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3998 - acc: 0.8362     \n",
      "Epoch 54/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3999 - acc: 0.8356     \n",
      "Epoch 55/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3998 - acc: 0.8337     \n",
      "Epoch 56/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3997 - acc: 0.8366     \n",
      "Epoch 57/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3995 - acc: 0.8356     \n",
      "Epoch 58/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3993 - acc: 0.8364     \n",
      "Epoch 59/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3995 - acc: 0.8360     - ETA: 0s - loss: 0.3992 - acc: 0.836\n",
      "Epoch 60/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3996 - acc: 0.8356     \n",
      "Epoch 61/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3999 - acc: 0.8372     \n",
      "Epoch 62/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3993 - acc: 0.8364     \n",
      "Epoch 63/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3998 - acc: 0.8349     \n",
      "Epoch 64/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3993 - acc: 0.8357     \n",
      "Epoch 65/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3996 - acc: 0.8359     \n",
      "Epoch 66/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3993 - acc: 0.8352     \n",
      "Epoch 67/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3991 - acc: 0.8367     \n",
      "Epoch 68/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3990 - acc: 0.8369     \n",
      "Epoch 69/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3993 - acc: 0.8351     \n",
      "Epoch 70/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3991 - acc: 0.8365     \n",
      "Epoch 71/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3989 - acc: 0.8362     \n",
      "Epoch 72/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3992 - acc: 0.8370     \n",
      "Epoch 73/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3993 - acc: 0.8349     \n",
      "Epoch 74/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3988 - acc: 0.8370     \n",
      "Epoch 75/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3991 - acc: 0.8345     \n",
      "Epoch 76/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3985 - acc: 0.8371     \n",
      "Epoch 77/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3988 - acc: 0.8371     \n",
      "Epoch 78/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3988 - acc: 0.8361     \n",
      "Epoch 79/100\n",
      "8000/8000 [==============================] - 0s - loss: 0.3988 - acc: 0.8362     \n",
      "Epoch 80/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3987 - acc: 0.8357     \n",
      "Epoch 81/100\n",
      "8000/8000 [==============================] - 0s - loss: 0.3986 - acc: 0.8359     \n",
      "Epoch 82/100\n",
      "8000/8000 [==============================] - 0s - loss: 0.3985 - acc: 0.8364     \n",
      "Epoch 83/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3984 - acc: 0.8370     \n",
      "Epoch 84/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3983 - acc: 0.8357     \n",
      "Epoch 85/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3983 - acc: 0.8359     \n",
      "Epoch 86/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8000/8000 [==============================] - 1s - loss: 0.3981 - acc: 0.8365     \n",
      "Epoch 87/100\n",
      "8000/8000 [==============================] - 0s - loss: 0.3977 - acc: 0.8356     \n",
      "Epoch 88/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3970 - acc: 0.8356     \n",
      "Epoch 89/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3958 - acc: 0.8361     \n",
      "Epoch 90/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3949 - acc: 0.8365     \n",
      "Epoch 91/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3927 - acc: 0.8365     \n",
      "Epoch 92/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3887 - acc: 0.8386     \n",
      "Epoch 93/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3825 - acc: 0.8430     \n",
      "Epoch 94/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3750 - acc: 0.8457     \n",
      "Epoch 95/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3683 - acc: 0.8499     \n",
      "Epoch 96/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3610 - acc: 0.8527     \n",
      "Epoch 97/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3559 - acc: 0.8540     \n",
      "Epoch 98/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3515 - acc: 0.8572     \n",
      "Epoch 99/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3486 - acc: 0.8555     \n",
      "Epoch 100/100\n",
      "8000/8000 [==============================] - 1s - loss: 0.3473 - acc: 0.8572     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f689cac3940>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3 - Making the Predictions and Evaluating the Model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the Test Set Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.20027186],\n",
       "       [ 0.38046303],\n",
       "       [ 0.19971733],\n",
       "       ..., \n",
       "       [ 0.20171742],\n",
       "       [ 0.15267174],\n",
       "       [ 0.16234307]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ..., \n",
       "       [False],\n",
       "       [False],\n",
       "       [False]], dtype=bool)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = (y_pred > 0.5)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1541,   54],\n",
       "       [ 219,  186]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8635"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1541+186)/2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}