11. ANN Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn Modelling \n",
    "\n",
    "tinyurl.com/ann-classify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Libraries for Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Step2- Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('Churn_Modelling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>619</td>\n",
       "      <td>France</td>\n",
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       "      <td>42</td>\n",
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       "      <td>4</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>9995</td>\n",
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       "      <td>France</td>\n",
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       "      <td>9996</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <td>9997</td>\n",
       "      <td>9998</td>\n",
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       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
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       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
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       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
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       "<p>10000 rows × 14 columns</p>\n",
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      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  Age  \\\n",
       "0             1    15634602   Hargrave          619    France  Female   42   \n",
       "1             2    15647311       Hill          608     Spain  Female   41   \n",
       "2             3    15619304       Onio          502    France  Female   42   \n",
       "3             4    15701354       Boni          699    France  Female   39   \n",
       "4             5    15737888   Mitchell          850     Spain  Female   43   \n",
       "...         ...         ...        ...          ...       ...     ...  ...   \n",
       "9995       9996    15606229   Obijiaku          771    France    Male   39   \n",
       "9996       9997    15569892  Johnstone          516    France    Male   35   \n",
       "9997       9998    15584532        Liu          709    France  Female   36   \n",
       "9998       9999    15682355  Sabbatini          772   Germany    Male   42   \n",
       "9999      10000    15628319     Walker          792    France  Female   28   \n",
       "\n",
       "      Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0          2       0.00              1          1               1   \n",
       "1          1   83807.86              1          0               1   \n",
       "2          8  159660.80              3          1               0   \n",
       "3          1       0.00              2          0               0   \n",
       "4          2  125510.82              1          1               1   \n",
       "...      ...        ...            ...        ...             ...   \n",
       "9995       5       0.00              2          1               0   \n",
       "9996      10   57369.61              1          1               1   \n",
       "9997       7       0.00              1          0               1   \n",
       "9998       3   75075.31              2          1               0   \n",
       "9999       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",
       "...               ...     ...  \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]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=dataset.iloc[:,3:13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Credit scoring is a statistical analysis performed by lenders and financial institutions to access a person's creditworthiness. Credit scoring is used by lenders to help decide on whether to extend or deny credit. A person's credit score is a number between 300 and 850, 850 being the highest credit rating possible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
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       "      <th>4</th>\n",
       "      <td>850</td>\n",
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       "      <td>43</td>\n",
       "      <td>2</td>\n",
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       "      <th>5</th>\n",
       "      <td>645</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>6</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\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",
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       "      <td>119346.88</td>\n",
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       "      <th>8</th>\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",
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       "      <td>1</td>\n",
       "      <td>74940.50</td>\n",
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       "      <th>9</th>\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",
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       "      <th>10</th>\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",
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       "      <th>11</th>\n",
       "      <td>497</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>76390.01</td>\n",
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       "      <th>12</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\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",
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       "    <tr>\n",
       "      <th>14</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0             619    France  Female   42       2       0.00              1   \n",
       "1             608     Spain  Female   41       1   83807.86              1   \n",
       "2             502    France  Female   42       8  159660.80              3   \n",
       "3             699    France  Female   39       1       0.00              2   \n",
       "4             850     Spain  Female   43       2  125510.82              1   \n",
       "5             645     Spain    Male   44       8  113755.78              2   \n",
       "6             822    France    Male   50       7       0.00              2   \n",
       "7             376   Germany  Female   29       4  115046.74              4   \n",
       "8             501    France    Male   44       4  142051.07              2   \n",
       "9             684    France    Male   27       2  134603.88              1   \n",
       "10            528    France    Male   31       6  102016.72              2   \n",
       "11            497     Spain    Male   24       3       0.00              2   \n",
       "12            476    France  Female   34      10       0.00              2   \n",
       "13            549    France  Female   25       5       0.00              2   \n",
       "14            635     Spain  Female   35       7       0.00              2   \n",
       "15            616   Germany    Male   45       3  143129.41              2   \n",
       "16            653   Germany    Male   58       1  132602.88              1   \n",
       "17            549     Spain  Female   24       9       0.00              2   \n",
       "18            587     Spain    Male   45       6       0.00              1   \n",
       "19            726    France  Female   24       6       0.00              2   \n",
       "20            732    France    Male   41       8       0.00              2   \n",
       "21            636     Spain  Female   32       8       0.00              2   \n",
       "22            510     Spain  Female   38       4       0.00              1   \n",
       "23            669    France    Male   46       3       0.00              2   \n",
       "24            846    France  Female   38       5       0.00              1   \n",
       "25            577    France    Male   25       3       0.00              2   \n",
       "26            756   Germany    Male   36       2  136815.64              1   \n",
       "27            571    France    Male   44       9       0.00              2   \n",
       "28            574   Germany  Female   43       3  141349.43              1   \n",
       "29            411    France    Male   29       0   59697.17              2   \n",
       "...           ...       ...     ...  ...     ...        ...            ...   \n",
       "9970          518    France    Male   42       7  151027.05              2   \n",
       "9971          833    France  Female   34       3  144751.81              1   \n",
       "9972          758    France    Male   26       4  155739.76              1   \n",
       "9973          611    France    Male   27       7       0.00              2   \n",
       "9974          583    France    Male   33       7  122531.86              1   \n",
       "9975          610   Germany    Male   50       1  113957.01              2   \n",
       "9976          637    France  Female   33       7  103377.81              1   \n",
       "9977          683    France  Female   32       9       0.00              2   \n",
       "9978          774    France    Male   40       9   93017.47              2   \n",
       "9979          677    France  Female   58       1   90022.85              1   \n",
       "9980          741     Spain    Male   35       6   74371.49              1   \n",
       "9981          498   Germany    Male   42       3  152039.70              1   \n",
       "9982          655   Germany  Female   46       7  137145.12              1   \n",
       "9983          613    France    Male   40       4       0.00              1   \n",
       "9984          602   Germany    Male   35       7   90602.42              2   \n",
       "9985          659    France    Male   36       6  123841.49              2   \n",
       "9986          673   Germany    Male   47       1  183579.54              2   \n",
       "9987          606     Spain    Male   30       8  180307.73              2   \n",
       "9988          775    France    Male   30       4       0.00              2   \n",
       "9989          841     Spain    Male   28       4       0.00              2   \n",
       "9990          714   Germany    Male   33       3   35016.60              1   \n",
       "9991          597    France  Female   53       4   88381.21              1   \n",
       "9992          726     Spain    Male   36       2       0.00              1   \n",
       "9993          644    France    Male   28       7  155060.41              1   \n",
       "9994          800    France  Female   29       2       0.00              2   \n",
       "9995          771    France    Male   39       5       0.00              2   \n",
       "9996          516    France    Male   35      10   57369.61              1   \n",
       "9997          709    France  Female   36       7       0.00              1   \n",
       "9998          772   Germany    Male   42       3   75075.31              2   \n",
       "9999          792    France  Female   28       4  130142.79              1   \n",
       "\n",
       "      HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0             1               1        101348.88  \n",
       "1             0               1        112542.58  \n",
       "2             1               0        113931.57  \n",
       "3             0               0         93826.63  \n",
       "4             1               1         79084.10  \n",
       "5             1               0        149756.71  \n",
       "6             1               1         10062.80  \n",
       "7             1               0        119346.88  \n",
       "8             0               1         74940.50  \n",
       "9             1               1         71725.73  \n",
       "10            0               0         80181.12  \n",
       "11            1               0         76390.01  \n",
       "12            1               0         26260.98  \n",
       "13            0               0        190857.79  \n",
       "14            1               1         65951.65  \n",
       "15            0               1         64327.26  \n",
       "16            1               0          5097.67  \n",
       "17            1               1         14406.41  \n",
       "18            0               0        158684.81  \n",
       "19            1               1         54724.03  \n",
       "20            1               1        170886.17  \n",
       "21            1               0        138555.46  \n",
       "22            1               0        118913.53  \n",
       "23            0               1          8487.75  \n",
       "24            1               1        187616.16  \n",
       "25            0               1        124508.29  \n",
       "26            1               1        170041.95  \n",
       "27            0               0         38433.35  \n",
       "28            1               1        100187.43  \n",
       "29            1               1         53483.21  \n",
       "...         ...             ...              ...  \n",
       "9970          1               0        119377.36  \n",
       "9971          0               0        166472.81  \n",
       "9972          1               0        171552.02  \n",
       "9973          1               1        157474.10  \n",
       "9974          1               0         13549.24  \n",
       "9975          1               0        196526.55  \n",
       "9976          1               0         84419.78  \n",
       "9977          1               1         24991.92  \n",
       "9978          1               0        191608.97  \n",
       "9979          0               1          2988.28  \n",
       "9980          0               0         99595.67  \n",
       "9981          1               1         53445.17  \n",
       "9982          1               0        115146.40  \n",
       "9983          0               0        151325.24  \n",
       "9984          1               1         51695.41  \n",
       "9985          1               0         96833.00  \n",
       "9986          0               1         34047.54  \n",
       "9987          1               1          1914.41  \n",
       "9988          1               0         49337.84  \n",
       "9989          1               1        179436.60  \n",
       "9990          1               0         53667.08  \n",
       "9991          1               0         69384.71  \n",
       "9992          1               0        195192.40  \n",
       "9993          1               0         29179.52  \n",
       "9994          0               0        167773.55  \n",
       "9995          1               0         96270.64  \n",
       "9996          1               1        101699.77  \n",
       "9997          0               1         42085.58  \n",
       "9998          1               0         92888.52  \n",
       "9999          1               0         38190.78  \n",
       "\n",
       "[10000 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=dataset.iloc[:,3:13].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[619, 'France', 'Female', ..., 1, 1, 101348.88],\n",
       "       [608, 'Spain', 'Female', ..., 0, 1, 112542.58],\n",
       "       [502, 'France', 'Female', ..., 1, 0, 113931.57],\n",
       "       ...,\n",
       "       [709, 'France', 'Female', ..., 0, 1, 42085.58],\n",
       "       [772, 'Germany', 'Male', ..., 1, 0, 92888.52],\n",
       "       [792, 'France', 'Female', ..., 1, 0, 38190.78]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=dataset.iloc[:,13].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId  Surname  CreditScore  Geography  Gender    Age  \\\n",
       "0         False       False    False        False      False   False  False   \n",
       "1         False       False    False        False      False   False  False   \n",
       "2         False       False    False        False      False   False  False   \n",
       "3         False       False    False        False      False   False  False   \n",
       "4         False       False    False        False      False   False  False   \n",
       "5         False       False    False        False      False   False  False   \n",
       "6         False       False    False        False      False   False  False   \n",
       "7         False       False    False        False      False   False  False   \n",
       "8         False       False    False        False      False   False  False   \n",
       "9         False       False    False        False      False   False  False   \n",
       "10        False       False    False        False      False   False  False   \n",
       "11        False       False    False        False      False   False  False   \n",
       "12        False       False    False        False      False   False  False   \n",
       "13        False       False    False        False      False   False  False   \n",
       "14        False       False    False        False      False   False  False   \n",
       "15        False       False    False        False      False   False  False   \n",
       "16        False       False    False        False      False   False  False   \n",
       "17        False       False    False        False      False   False  False   \n",
       "18        False       False    False        False      False   False  False   \n",
       "19        False       False    False        False      False   False  False   \n",
       "20        False       False    False        False      False   False  False   \n",
       "21        False       False    False        False      False   False  False   \n",
       "22        False       False    False        False      False   False  False   \n",
       "23        False       False    False        False      False   False  False   \n",
       "24        False       False    False        False      False   False  False   \n",
       "25        False       False    False        False      False   False  False   \n",
       "26        False       False    False        False      False   False  False   \n",
       "27        False       False    False        False      False   False  False   \n",
       "28        False       False    False        False      False   False  False   \n",
       "29        False       False    False        False      False   False  False   \n",
       "...         ...         ...      ...          ...        ...     ...    ...   \n",
       "9970      False       False    False        False      False   False  False   \n",
       "9971      False       False    False        False      False   False  False   \n",
       "9972      False       False    False        False      False   False  False   \n",
       "9973      False       False    False        False      False   False  False   \n",
       "9974      False       False    False        False      False   False  False   \n",
       "9975      False       False    False        False      False   False  False   \n",
       "9976      False       False    False        False      False   False  False   \n",
       "9977      False       False    False        False      False   False  False   \n",
       "9978      False       False    False        False      False   False  False   \n",
       "9979      False       False    False        False      False   False  False   \n",
       "9980      False       False    False        False      False   False  False   \n",
       "9981      False       False    False        False      False   False  False   \n",
       "9982      False       False    False        False      False   False  False   \n",
       "9983      False       False    False        False      False   False  False   \n",
       "9984      False       False    False        False      False   False  False   \n",
       "9985      False       False    False        False      False   False  False   \n",
       "9986      False       False    False        False      False   False  False   \n",
       "9987      False       False    False        False      False   False  False   \n",
       "9988      False       False    False        False      False   False  False   \n",
       "9989      False       False    False        False      False   False  False   \n",
       "9990      False       False    False        False      False   False  False   \n",
       "9991      False       False    False        False      False   False  False   \n",
       "9992      False       False    False        False      False   False  False   \n",
       "9993      False       False    False        False      False   False  False   \n",
       "9994      False       False    False        False      False   False  False   \n",
       "9995      False       False    False        False      False   False  False   \n",
       "9996      False       False    False        False      False   False  False   \n",
       "9997      False       False    False        False      False   False  False   \n",
       "9998      False       False    False        False      False   False  False   \n",
       "9999      False       False    False        False      False   False  False   \n",
       "\n",
       "      Tenure  Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0      False    False          False      False           False   \n",
       "1      False    False          False      False           False   \n",
       "2      False    False          False      False           False   \n",
       "3      False    False          False      False           False   \n",
       "4      False    False          False      False           False   \n",
       "5      False    False          False      False           False   \n",
       "6      False    False          False      False           False   \n",
       "7      False    False          False      False           False   \n",
       "8      False    False          False      False           False   \n",
       "9      False    False          False      False           False   \n",
       "10     False    False          False      False           False   \n",
       "11     False    False          False      False           False   \n",
       "12     False    False          False      False           False   \n",
       "13     False    False          False      False           False   \n",
       "14     False    False          False      False           False   \n",
       "15     False    False          False      False           False   \n",
       "16     False    False          False      False           False   \n",
       "17     False    False          False      False           False   \n",
       "18     False    False          False      False           False   \n",
       "19     False    False          False      False           False   \n",
       "20     False    False          False      False           False   \n",
       "21     False    False          False      False           False   \n",
       "22     False    False          False      False           False   \n",
       "23     False    False          False      False           False   \n",
       "24     False    False          False      False           False   \n",
       "25     False    False          False      False           False   \n",
       "26     False    False          False      False           False   \n",
       "27     False    False          False      False           False   \n",
       "28     False    False          False      False           False   \n",
       "29     False    False          False      False           False   \n",
       "...      ...      ...            ...        ...             ...   \n",
       "9970   False    False          False      False           False   \n",
       "9971   False    False          False      False           False   \n",
       "9972   False    False          False      False           False   \n",
       "9973   False    False          False      False           False   \n",
       "9974   False    False          False      False           False   \n",
       "9975   False    False          False      False           False   \n",
       "9976   False    False          False      False           False   \n",
       "9977   False    False          False      False           False   \n",
       "9978   False    False          False      False           False   \n",
       "9979   False    False          False      False           False   \n",
       "9980   False    False          False      False           False   \n",
       "9981   False    False          False      False           False   \n",
       "9982   False    False          False      False           False   \n",
       "9983   False    False          False      False           False   \n",
       "9984   False    False          False      False           False   \n",
       "9985   False    False          False      False           False   \n",
       "9986   False    False          False      False           False   \n",
       "9987   False    False          False      False           False   \n",
       "9988   False    False          False      False           False   \n",
       "9989   False    False          False      False           False   \n",
       "9990   False    False          False      False           False   \n",
       "9991   False    False          False      False           False   \n",
       "9992   False    False          False      False           False   \n",
       "9993   False    False          False      False           False   \n",
       "9994   False    False          False      False           False   \n",
       "9995   False    False          False      False           False   \n",
       "9996   False    False          False      False           False   \n",
       "9997   False    False          False      False           False   \n",
       "9998   False    False          False      False           False   \n",
       "9999   False    False          False      False           False   \n",
       "\n",
       "      EstimatedSalary  Exited  \n",
       "0               False   False  \n",
       "1               False   False  \n",
       "2               False   False  \n",
       "3               False   False  \n",
       "4               False   False  \n",
       "5               False   False  \n",
       "6               False   False  \n",
       "7               False   False  \n",
       "8               False   False  \n",
       "9               False   False  \n",
       "10              False   False  \n",
       "11              False   False  \n",
       "12              False   False  \n",
       "13              False   False  \n",
       "14              False   False  \n",
       "15              False   False  \n",
       "16              False   False  \n",
       "17              False   False  \n",
       "18              False   False  \n",
       "19              False   False  \n",
       "20              False   False  \n",
       "21              False   False  \n",
       "22              False   False  \n",
       "23              False   False  \n",
       "24              False   False  \n",
       "25              False   False  \n",
       "26              False   False  \n",
       "27              False   False  \n",
       "28              False   False  \n",
       "29              False   False  \n",
       "...               ...     ...  \n",
       "9970            False   False  \n",
       "9971            False   False  \n",
       "9972            False   False  \n",
       "9973            False   False  \n",
       "9974            False   False  \n",
       "9975            False   False  \n",
       "9976            False   False  \n",
       "9977            False   False  \n",
       "9978            False   False  \n",
       "9979            False   False  \n",
       "9980            False   False  \n",
       "9981            False   False  \n",
       "9982            False   False  \n",
       "9983            False   False  \n",
       "9984            False   False  \n",
       "9985            False   False  \n",
       "9986            False   False  \n",
       "9987            False   False  \n",
       "9988            False   False  \n",
       "9989            False   False  \n",
       "9990            False   False  \n",
       "9991            False   False  \n",
       "9992            False   False  \n",
       "9993            False   False  \n",
       "9994            False   False  \n",
       "9995            False   False  \n",
       "9996            False   False  \n",
       "9997            False   False  \n",
       "9998            False   False  \n",
       "9999            False   False  \n",
       "\n",
       "[10000 rows x 14 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding \n",
    "\n",
    "<img src=\"https://ekababisong.org/assets/seminar_IEEE/LabelEncoder.png\" width=1000 height=10500 />\n",
    "\n",
    "Example 2 \n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/WillKoehrsen/Machine-Learning-Projects/master/label_encoding.png\" width=500 height=500 />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step5-Encode categorical Text Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lb=LabelEncoder()\n",
    "lb2=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['France', 'Spain', 'France', ..., 'France', 'Germany', 'France'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x[:,1]=lb.fit_transform(x[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[619, 0, 'Female', ..., 1, 1, 101348.88],\n",
       "       [608, 2, 'Female', ..., 0, 1, 112542.58],\n",
       "       [502, 0, 'Female', ..., 1, 0, 113931.57],\n",
       "       ...,\n",
       "       [709, 0, 'Female', ..., 0, 1, 42085.58],\n",
       "       [772, 1, 'Male', ..., 1, 0, 92888.52],\n",
       "       [792, 0, 'Female', ..., 1, 0, 38190.78]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x[:,2]=lb2.fit_transform(x[:,2])#if you have more than one more that one category of categorical variable then we have to do label encoding all at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[619, 0, 0, ..., 1, 1, 101348.88],\n",
       "       [608, 2, 0, ..., 0, 1, 112542.58],\n",
       "       [502, 0, 0, ..., 1, 0, 113931.57],\n",
       "       ...,\n",
       "       [709, 0, 0, ..., 0, 1, 42085.58],\n",
       "       [772, 1, 1, ..., 1, 0, 92888.52],\n",
       "       [792, 0, 0, ..., 1, 0, 38190.78]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# one-hot encoding\n",
    "\n",
    "<img src=\"https://i.imgur.com/mtimFxh.png\" width=500 height=500 />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "oh=OneHotEncoder(categorical_features=[1])\n",
    "x=oh.fit_transform(x).toarray()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        1.0000000e+00, 1.0134888e+05],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 1.1254258e+05],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 1.1393157e+05],\n",
       "       ...,\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        1.0000000e+00, 4.2085580e+04],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 9.2888520e+04],\n",
       "       [1.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        0.0000000e+00, 3.8190780e+04]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0000000e+00, 1.0000000e+00, 6.0800000e+02, 0.0000000e+00,\n",
       "       4.1000000e+01, 1.0000000e+00, 8.3807860e+04, 1.0000000e+00,\n",
       "       0.0000000e+00, 1.0000000e+00, 1.1254258e+05])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Standard Scaling \n",
    "\n",
    "<img src=\"https://i.stack.imgur.com/PZgJ2.pngg\" width=500 height=500 />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc= StandardScaler()\n",
    "\n",
    "x_train =sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.4'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.__version__"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "while using tensorflow we have to build our own activation function and every thing need to be done by the user but when it comes to keras all the activation functions and every thing will be built in and keras uses the backend power of tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7000, 11)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(input_dim=11, activation=\"relu\", units=15, kernel_initializer=\"random_uniform\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "model.add(Dense(input_dim=11,init=\"random_uniform\",activation='relu',output_dim=15))#input layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=8, kernel_initializer=\"random_uniform\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "model.add(Dense(output_dim=8,init=\"random_uniform\",activation='relu'))#hidden layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"random_uniform\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "model.add(Dense(output_dim=1,init='random_uniform',activation='sigmoid'))#output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# since it is a binary classification WE use binary cross entropy.if it is categorical then we ue categorical entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])#adam=batch gradent descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/50\n",
      "7000/7000 [==============================] - 1s 139us/step - loss: 0.5714 - acc: 0.7951\n",
      "Epoch 2/50\n",
      "7000/7000 [==============================] - 0s 69us/step - loss: 0.4327 - acc: 0.7977\n",
      "Epoch 3/50\n",
      "7000/7000 [==============================] - 0s 63us/step - loss: 0.4261 - acc: 0.7977\n",
      "Epoch 4/50\n",
      "7000/7000 [==============================] - 1s 74us/step - loss: 0.4218 - acc: 0.7977\n",
      "Epoch 5/50\n",
      "7000/7000 [==============================] - 0s 63us/step - loss: 0.4174 - acc: 0.8166\n",
      "Epoch 6/50\n",
      "7000/7000 [==============================] - 0s 59us/step - loss: 0.4136 - acc: 0.8280\n",
      "Epoch 7/50\n",
      "7000/7000 [==============================] - 0s 60us/step - loss: 0.4102 - acc: 0.8310\n",
      "Epoch 8/50\n",
      "7000/7000 [==============================] - 0s 61us/step - loss: 0.4072 - acc: 0.8331\n",
      "Epoch 9/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.4046 - acc: 0.8339\n",
      "Epoch 10/50\n",
      "7000/7000 [==============================] - 0s 61us/step - loss: 0.4024 - acc: 0.8344\n",
      "Epoch 11/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.4008 - acc: 0.8346\n",
      "Epoch 12/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3999 - acc: 0.8351\n",
      "Epoch 13/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.3986 - acc: 0.8359\n",
      "Epoch 14/50\n",
      "7000/7000 [==============================] - 0s 61us/step - loss: 0.3980 - acc: 0.8360\n",
      "Epoch 15/50\n",
      "7000/7000 [==============================] - 1s 73us/step - loss: 0.3970 - acc: 0.8369\n",
      "Epoch 16/50\n",
      "7000/7000 [==============================] - 0s 63us/step - loss: 0.3962 - acc: 0.8349\n",
      "Epoch 17/50\n",
      "7000/7000 [==============================] - 0s 59us/step - loss: 0.3952 - acc: 0.8366\n",
      "Epoch 18/50\n",
      "7000/7000 [==============================] - ETA: 0s - loss: 0.3986 - acc: 0.832 - 0s 58us/step - loss: 0.3950 - acc: 0.8349\n",
      "Epoch 19/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3943 - acc: 0.8340: 0s - loss: 0.3977 - acc: 0.8\n",
      "Epoch 20/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3935 - acc: 0.8383\n",
      "Epoch 21/50\n",
      "7000/7000 [==============================] - 0s 57us/step - loss: 0.3928 - acc: 0.8384\n",
      "Epoch 22/50\n",
      "7000/7000 [==============================] - 0s 60us/step - loss: 0.3919 - acc: 0.8360\n",
      "Epoch 23/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3911 - acc: 0.8390\n",
      "Epoch 24/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3903 - acc: 0.8409\n",
      "Epoch 25/50\n",
      "7000/7000 [==============================] - 0s 56us/step - loss: 0.3890 - acc: 0.8414\n",
      "Epoch 26/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3863 - acc: 0.8431\n",
      "Epoch 27/50\n",
      "7000/7000 [==============================] - 0s 59us/step - loss: 0.3851 - acc: 0.8406\n",
      "Epoch 28/50\n",
      "7000/7000 [==============================] - 1s 75us/step - loss: 0.3830 - acc: 0.8440: 0s - loss: 0.3855 - acc: 0.8\n",
      "Epoch 29/50\n",
      "7000/7000 [==============================] - 0s 63us/step - loss: 0.3802 - acc: 0.8444\n",
      "Epoch 30/50\n",
      "7000/7000 [==============================] - 0s 65us/step - loss: 0.3773 - acc: 0.8446\n",
      "Epoch 31/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.3743 - acc: 0.8446\n",
      "Epoch 32/50\n",
      "7000/7000 [==============================] - 0s 57us/step - loss: 0.3716 - acc: 0.8437\n",
      "Epoch 33/50\n",
      "7000/7000 [==============================] - 0s 54us/step - loss: 0.3675 - acc: 0.8446\n",
      "Epoch 34/50\n",
      "7000/7000 [==============================] - 0s 59us/step - loss: 0.3635 - acc: 0.8457: 0s - loss: 0.3625 - acc: 0.846\n",
      "Epoch 35/50\n",
      "7000/7000 [==============================] - 0s 69us/step - loss: 0.3581 - acc: 0.8506\n",
      "Epoch 36/50\n",
      "7000/7000 [==============================] - 0s 65us/step - loss: 0.3523 - acc: 0.8550\n",
      "Epoch 37/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.3448 - acc: 0.8586\n",
      "Epoch 38/50\n",
      "7000/7000 [==============================] - 0s 57us/step - loss: 0.3400 - acc: 0.8603\n",
      "Epoch 39/50\n",
      "7000/7000 [==============================] - 0s 57us/step - loss: 0.3363 - acc: 0.8654\n",
      "Epoch 40/50\n",
      "7000/7000 [==============================] - 0s 53us/step - loss: 0.3349 - acc: 0.8647\n",
      "Epoch 41/50\n",
      "7000/7000 [==============================] - 0s 54us/step - loss: 0.3326 - acc: 0.8654\n",
      "Epoch 42/50\n",
      "7000/7000 [==============================] - 0s 58us/step - loss: 0.3312 - acc: 0.8644\n",
      "Epoch 43/50\n",
      "7000/7000 [==============================] - 0s 54us/step - loss: 0.3299 - acc: 0.8649\n",
      "Epoch 44/50\n",
      "7000/7000 [==============================] - 0s 56us/step - loss: 0.3302 - acc: 0.8689\n",
      "Epoch 45/50\n",
      "7000/7000 [==============================] - 0s 68us/step - loss: 0.3288 - acc: 0.8667\n",
      "Epoch 46/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.3289 - acc: 0.8686\n",
      "Epoch 47/50\n",
      "7000/7000 [==============================] - 0s 62us/step - loss: 0.3280 - acc: 0.8670\n",
      "Epoch 48/50\n",
      "7000/7000 [==============================] - 0s 61us/step - loss: 0.3285 - acc: 0.8661\n",
      "Epoch 49/50\n",
      "7000/7000 [==============================] - 0s 50us/step - loss: 0.3280 - acc: 0.8677\n",
      "Epoch 50/50\n",
      "7000/7000 [==============================] - 0s 51us/step - loss: 0.3277 - acc: 0.8667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1deb3788eb8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=50,batch_size=32)#epochs no of iteration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=(y_pred>0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int32 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "y_p=model.predict(sc.transform(np.array([[0,1,100,1,25,8,115000,24,1,1,100000]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.]], dtype=float32)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_p=(y_p>0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.855"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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