notebooks/Churn Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e094f49c-9c00-48bb-923a-f1d6b0e08324",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f04b6036-1267-463a-8af4-ac4b2fed4eef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1.08 s\n",
      "Wall time: 4.14 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from tensorflow.keras.layers import Dense, Activation, Embedding, Flatten, ReLU, LeakyReLU, PReLU, ELU, BatchNormalization, Dropout\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2e6255-8795-40a0-bab1-dc94d958433c",
   "metadata": {},
   "source": [
    "# Reading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aba4a5b0-5b9c-4f27-b316-9fe208aef0bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Tenure</th>\n",
       "      <th>Balance</th>\n",
       "      <th>NumOfProducts</th>\n",
       "      <th>HasCrCard</th>\n",
       "      <th>IsActiveMember</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Exited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <td>608</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <td>Onio</td>\n",
       "      <td>502</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>8</td>\n",
       "      <td>159660.80</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
       "      <td>850</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Female</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>79084.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>15606229</td>\n",
       "      <td>Obijiaku</td>\n",
       "      <td>771</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>96270.64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>15569892</td>\n",
       "      <td>Johnstone</td>\n",
       "      <td>516</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>57369.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101699.77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
       "      <td>709</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42085.58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>15682355</td>\n",
       "      <td>Sabbatini</td>\n",
       "      <td>772</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Male</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>75075.31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>92888.52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>15628319</td>\n",
       "      <td>Walker</td>\n",
       "      <td>792</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38190.78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore Geography  Gender  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]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/Churn_Modelling.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505a4593-1abe-442b-a08a-d6e0a6ed6656",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1c91eaa-bdc1-46c6-b399-3e0bcecb8e97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RowNumber          0\n",
       "CustomerId         0\n",
       "Surname            0\n",
       "CreditScore        0\n",
       "Geography          0\n",
       "Gender             0\n",
       "Age                0\n",
       "Tenure             0\n",
       "Balance            0\n",
       "NumOfProducts      0\n",
       "HasCrCard          0\n",
       "IsActiveMember     0\n",
       "EstimatedSalary    0\n",
       "Exited             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "572d43ea-86a5-4814-ad06-b09bb3bcc7da",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>CreditScore</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>count</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.00000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         RowNumber    CustomerId   CreditScore           Age        Tenure  \\\n",
       "count  10000.00000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
       "mean    5000.50000  1.569094e+07    650.528800     38.921800      5.012800   \n",
       "std     2886.89568  7.193619e+04     96.653299     10.487806      2.892174   \n",
       "min        1.00000  1.556570e+07    350.000000     18.000000      0.000000   \n",
       "25%     2500.75000  1.562853e+07    584.000000     32.000000      3.000000   \n",
       "50%     5000.50000  1.569074e+07    652.000000     37.000000      5.000000   \n",
       "75%     7500.25000  1.575323e+07    718.000000     44.000000      7.000000   \n",
       "max    10000.00000  1.581569e+07    850.000000     92.000000     10.000000   \n",
       "\n",
       "             Balance  NumOfProducts    HasCrCard  IsActiveMember  \\\n",
       "count   10000.000000   10000.000000  10000.00000    10000.000000   \n",
       "mean    76485.889288       1.530200      0.70550        0.515100   \n",
       "std     62397.405202       0.581654      0.45584        0.499797   \n",
       "min         0.000000       1.000000      0.00000        0.000000   \n",
       "25%         0.000000       1.000000      0.00000        0.000000   \n",
       "50%     97198.540000       1.000000      1.00000        1.000000   \n",
       "75%    127644.240000       2.000000      1.00000        1.000000   \n",
       "max    250898.090000       4.000000      1.00000        1.000000   \n",
       "\n",
       "       EstimatedSalary        Exited  \n",
       "count     10000.000000  10000.000000  \n",
       "mean     100090.239881      0.203700  \n",
       "std       57510.492818      0.402769  \n",
       "min          11.580000      0.000000  \n",
       "25%       51002.110000      0.000000  \n",
       "50%      100193.915000      0.000000  \n",
       "75%      149388.247500      0.000000  \n",
       "max      199992.480000      1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8790ef6-3624-4010-bc83-517c1f0af562",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <td>10000.00000</td>\n",
       "      <td>1.000000e+04</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
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       "      <td>2932</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>5014</td>\n",
       "      <td>5457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569094e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>650.528800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.921800</td>\n",
       "      <td>5.012800</td>\n",
       "      <td>76485.889288</td>\n",
       "      <td>1.530200</td>\n",
       "      <td>0.70550</td>\n",
       "      <td>0.515100</td>\n",
       "      <td>100090.239881</td>\n",
       "      <td>0.203700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2886.89568</td>\n",
       "      <td>7.193619e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.653299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.487806</td>\n",
       "      <td>2.892174</td>\n",
       "      <td>62397.405202</td>\n",
       "      <td>0.581654</td>\n",
       "      <td>0.45584</td>\n",
       "      <td>0.499797</td>\n",
       "      <td>57510.492818</td>\n",
       "      <td>0.402769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.556570e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.580000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2500.75000</td>\n",
       "      <td>1.562853e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51002.110000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5000.50000</td>\n",
       "      <td>1.569074e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>652.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>97198.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100193.915000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.25000</td>\n",
       "      <td>1.575323e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>127644.240000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>149388.247500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.00000</td>\n",
       "      <td>1.581569e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>250898.090000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199992.480000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          RowNumber    CustomerId Surname   CreditScore Geography Gender  \\\n",
       "count   10000.00000  1.000000e+04   10000  10000.000000     10000  10000   \n",
       "unique          NaN           NaN    2932           NaN         3      2   \n",
       "top             NaN           NaN   Smith           NaN    France   Male   \n",
       "freq            NaN           NaN      32           NaN      5014   5457   \n",
       "mean     5000.50000  1.569094e+07     NaN    650.528800       NaN    NaN   \n",
       "std      2886.89568  7.193619e+04     NaN     96.653299       NaN    NaN   \n",
       "min         1.00000  1.556570e+07     NaN    350.000000       NaN    NaN   \n",
       "25%      2500.75000  1.562853e+07     NaN    584.000000       NaN    NaN   \n",
       "50%      5000.50000  1.569074e+07     NaN    652.000000       NaN    NaN   \n",
       "75%      7500.25000  1.575323e+07     NaN    718.000000       NaN    NaN   \n",
       "max     10000.00000  1.581569e+07     NaN    850.000000       NaN    NaN   \n",
       "\n",
       "                 Age        Tenure        Balance  NumOfProducts    HasCrCard  \\\n",
       "count   10000.000000  10000.000000   10000.000000   10000.000000  10000.00000   \n",
       "unique           NaN           NaN            NaN            NaN          NaN   \n",
       "top              NaN           NaN            NaN            NaN          NaN   \n",
       "freq             NaN           NaN            NaN            NaN          NaN   \n",
       "mean       38.921800      5.012800   76485.889288       1.530200      0.70550   \n",
       "std        10.487806      2.892174   62397.405202       0.581654      0.45584   \n",
       "min        18.000000      0.000000       0.000000       1.000000      0.00000   \n",
       "25%        32.000000      3.000000       0.000000       1.000000      0.00000   \n",
       "50%        37.000000      5.000000   97198.540000       1.000000      1.00000   \n",
       "75%        44.000000      7.000000  127644.240000       2.000000      1.00000   \n",
       "max        92.000000     10.000000  250898.090000       4.000000      1.00000   \n",
       "\n",
       "        IsActiveMember  EstimatedSalary        Exited  \n",
       "count     10000.000000     10000.000000  10000.000000  \n",
       "unique             NaN              NaN           NaN  \n",
       "top                NaN              NaN           NaN  \n",
       "freq               NaN              NaN           NaN  \n",
       "mean          0.515100    100090.239881      0.203700  \n",
       "std           0.499797     57510.492818      0.402769  \n",
       "min           0.000000        11.580000      0.000000  \n",
       "25%           0.000000     51002.110000      0.000000  \n",
       "50%           1.000000    100193.915000      0.000000  \n",
       "75%           1.000000    149388.247500      0.000000  \n",
       "max           1.000000    199992.480000      1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77f01cec-e0e5-45c6-8e15-4392e969a1a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Geography        10000 non-null  object \n",
      " 5   Gender           10000 non-null  object \n",
      " 6   Age              10000 non-null  int64  \n",
      " 7   Tenure           10000 non-null  int64  \n",
      " 8   Balance          10000 non-null  float64\n",
      " 9   NumOfProducts    10000 non-null  int64  \n",
      " 10  HasCrCard        10000 non-null  int64  \n",
      " 11  IsActiveMember   10000 non-null  int64  \n",
      " 12  EstimatedSalary  10000 non-null  float64\n",
      " 13  Exited           10000 non-null  int64  \n",
      "dtypes: float64(2), int64(9), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acb3fa8d-2f6f-4437-8ac5-cc582003f734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Exited\n",
       "0    7963\n",
       "1    2037\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b89a9e7e-e25e-4dfa-ae1c-c94f312a4f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['France', 'Spain', 'Germany'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Geography'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d31466b-2ea5-4ac5-b5d6-a2e44b8237df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Geography\n",
       "France     5014\n",
       "Germany    2509\n",
       "Spain      2477\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Geography'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f93347-177f-48c2-93a6-137781d391ec",
   "metadata": {},
   "source": [
    "from tensorflow.keras.applications import --> All pretrained & SOTA models like VGG16, ResNet, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a42ed1f8-9974-4a96-bb0c-aca4c1fe2373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4007e751-99c0-4c0b-a0fa-9b854354c8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Male      5457\n",
       "Female    4543\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5a9a477c-de65-4c4e-9e7a-89a5e1d96133",
   "metadata": {},
   "outputs": [],
   "source": [
    "geography = pd.get_dummies(df['Geography'], drop_first = True)\n",
    "gender = pd.get_dummies(df['Gender'], drop_first = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c97e96b-98c2-4785-be97-315f18cfa292",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Geography', 'Gender'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3809edea-bcbc-4eca-b3a2-cc645e57f141",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>Hill</td>\n",
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       "      <td>1</td>\n",
       "      <td>83807.86</td>\n",
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       "      <td>112542.58</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Boni</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15737888</td>\n",
       "      <td>Mitchell</td>\n",
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       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>125510.82</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>Obijiaku</td>\n",
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       "      <td>15584532</td>\n",
       "      <td>Liu</td>\n",
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       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>1</td>\n",
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       "      <th>9999</th>\n",
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       "      <td>792</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore  Age  Tenure    Balance  \\\n",
       "0             1    15634602   Hargrave          619   42       2       0.00   \n",
       "1             2    15647311       Hill          608   41       1   83807.86   \n",
       "2             3    15619304       Onio          502   42       8  159660.80   \n",
       "3             4    15701354       Boni          699   39       1       0.00   \n",
       "4             5    15737888   Mitchell          850   43       2  125510.82   \n",
       "...         ...         ...        ...          ...  ...     ...        ...   \n",
       "9995       9996    15606229   Obijiaku          771   39       5       0.00   \n",
       "9996       9997    15569892  Johnstone          516   35      10   57369.61   \n",
       "9997       9998    15584532        Liu          709   36       7       0.00   \n",
       "9998       9999    15682355  Sabbatini          772   42       3   75075.31   \n",
       "9999      10000    15628319     Walker          792   28       4  130142.79   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Exited  \\\n",
       "0                 1          1               1        101348.88       1   \n",
       "1                 1          0               1        112542.58       0   \n",
       "2                 3          1               0        113931.57       1   \n",
       "3                 2          0               0         93826.63       0   \n",
       "4                 1          1               1         79084.10       0   \n",
       "...             ...        ...             ...              ...     ...   \n",
       "9995              2          1               0         96270.64       0   \n",
       "9996              1          1               1        101699.77       0   \n",
       "9997              1          0               1         42085.58       1   \n",
       "9998              2          1               0         92888.52       1   \n",
       "9999              1          1               0         38190.78       0   \n",
       "\n",
       "      Germany  Spain   Male  \n",
       "0       False  False  False  \n",
       "1       False   True  False  \n",
       "2       False  False  False  \n",
       "3       False  False  False  \n",
       "4       False   True  False  \n",
       "...       ...    ...    ...  \n",
       "9995    False  False   True  \n",
       "9996    False  False   True  \n",
       "9997    False  False  False  \n",
       "9998     True  False   True  \n",
       "9999    False  False  False  \n",
       "\n",
       "[10000 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.concat([df, geography, gender], axis = 1)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "70d59c0c-e9ad-4b82-8ac0-27ba5ce51a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>RowNumber</th>\n",
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       "      <td>125510.82</td>\n",
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       "      <td>4</td>\n",
       "      <td>130142.79</td>\n",
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       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      RowNumber  CustomerId    Surname  CreditScore  Age  Tenure    Balance  \\\n",
       "0             1    15634602   Hargrave          619   42       2       0.00   \n",
       "1             2    15647311       Hill          608   41       1   83807.86   \n",
       "2             3    15619304       Onio          502   42       8  159660.80   \n",
       "3             4    15701354       Boni          699   39       1       0.00   \n",
       "4             5    15737888   Mitchell          850   43       2  125510.82   \n",
       "...         ...         ...        ...          ...  ...     ...        ...   \n",
       "9995       9996    15606229   Obijiaku          771   39       5       0.00   \n",
       "9996       9997    15569892  Johnstone          516   35      10   57369.61   \n",
       "9997       9998    15584532        Liu          709   36       7       0.00   \n",
       "9998       9999    15682355  Sabbatini          772   42       3   75075.31   \n",
       "9999      10000    15628319     Walker          792   28       4  130142.79   \n",
       "\n",
       "      NumOfProducts  HasCrCard  IsActiveMember  EstimatedSalary  Germany  \\\n",
       "0                 1          1               1        101348.88    False   \n",
       "1                 1          0               1        112542.58    False   \n",
       "2                 3          1               0        113931.57    False   \n",
       "3                 2          0               0         93826.63    False   \n",
       "4                 1          1               1         79084.10    False   \n",
       "...             ...        ...             ...              ...      ...   \n",
       "9995              2          1               0         96270.64    False   \n",
       "9996              1          1               1        101699.77    False   \n",
       "9997              1          0               1         42085.58    False   \n",
       "9998              2          1               0         92888.52     True   \n",
       "9999              1          1               0         38190.78    False   \n",
       "\n",
       "      Spain   Male  Exited  \n",
       "0     False  False       1  \n",
       "1      True  False       0  \n",
       "2     False  False       1  \n",
       "3     False  False       0  \n",
       "4      True  False       0  \n",
       "...     ...    ...     ...  \n",
       "9995  False   True       0  \n",
       "9996  False   True       0  \n",
       "9997  False  False       1  \n",
       "9998  False   True       1  \n",
       "9999  False  False       0  \n",
       "\n",
       "[10000 rows x 15 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset[['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Germany', 'Spain', 'Male', 'Exited']]\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "569890bc-120b-46a7-b536-b5474f331f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100090.239881"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['EstimatedSalary'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75eb2930-0b0f-45a9-9009-d81e57378109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(dataset['EstimatedSalary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b89523d9-81e9-49fc-898c-f25c4f34009e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   RowNumber        10000 non-null  int64  \n",
      " 1   CustomerId       10000 non-null  int64  \n",
      " 2   Surname          10000 non-null  object \n",
      " 3   CreditScore      10000 non-null  int64  \n",
      " 4   Age              10000 non-null  int64  \n",
      " 5   Tenure           10000 non-null  int64  \n",
      " 6   Balance          10000 non-null  float64\n",
      " 7   NumOfProducts    10000 non-null  int64  \n",
      " 8   HasCrCard        10000 non-null  int64  \n",
      " 9   IsActiveMember   10000 non-null  int64  \n",
      " 10  EstimatedSalary  10000 non-null  float64\n",
      " 11  Germany          10000 non-null  bool   \n",
      " 12  Spain            10000 non-null  bool   \n",
      " 13  Male             10000 non-null  bool   \n",
      " 14  Exited           10000 non-null  int64  \n",
      "dtypes: bool(3), float64(2), int64(9), object(1)\n",
      "memory usage: 966.9+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be394dc9-78fb-41c8-8bfb-be464fad9cc3",
   "metadata": {},
   "source": [
    "# Splitting Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9ffd4d83-d13a-419f-a48c-640f5ffc6d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.iloc[:, 3:14]\n",
    "y = dataset.iloc[:,14:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d636aef-53b6-4717-8b32-28c4d5235d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 1, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "043a2ba3-753c-4456-8d9c-f9db2fc03d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test  = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66555407-c556-4de3-93b5-e8b9a94bc4ba",
   "metadata": {},
   "source": [
    "# Artificial Neural Network (ANN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb41a50b-4fde-4a04-884e-5c6b78495ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the ANN, This is an empty neural network\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e3fbb25-cd30-467c-a124-b57a8e23fbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the input layer and the first hidden layer\n",
    "# units/output dimension is the number of hidden neurons in the first layer\n",
    "# kernel_initializer/init is the weight initialization parameter, where he_normal/he_uniform are the techniques\n",
    "# While using relu as an activation function he_normal/he_uniform works well\n",
    "# By using hyperparameter tuning, we'll find the exact number of hidden neurons, but now for initialization we used 6\n",
    "classifier.add(Dense(units = 6, kernel_initializer= 'he_uniform', activation = 'relu', input_dim = 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1c1bbd5a-9963-4080-9984-1a3a63e787e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the second hidden layer\n",
    "# All the hidden layers must have RELU or LeakyRELU as the activation function,and relu prevents vanishing gradient\n",
    "classifier.add(Dense(units = 6, kernel_initializer = 'he_uniform', activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68c60e32-7522-41d1-8443-f0d44c14bba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the output layer\n",
    "classifier.add(Dense(units = 1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5936cccd-e604-4e7b-bd2a-2d8202c17964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 6)                 72        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 6)                 42        \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 7         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 121\n",
      "Trainable params: 121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9285c7f8-d451-4466-be0b-4aa338753758",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the ANN\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76bb85c2-4a0a-4819-8885-ee7824ac8d46",
   "metadata": {},
   "source": [
    "### Plotting ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "86c4310d-e1a2-4680-9c07-a43b95b6bce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the model architecture\n",
    "plot_model(classifier, to_file='../images/1 ANN.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "353dbce7-4ff6-4ef5-9ede-77dbd218c43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.6576 - accuracy: 0.6576 - val_loss: 0.5067 - val_accuracy: 0.7849\n",
      "Epoch 2/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4690 - accuracy: 0.8046 - val_loss: 0.4770 - val_accuracy: 0.7868\n",
      "Epoch 3/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4405 - accuracy: 0.8084 - val_loss: 0.4578 - val_accuracy: 0.7944\n",
      "Epoch 4/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.4226 - accuracy: 0.8136 - val_loss: 0.4482 - val_accuracy: 0.7978\n",
      "Epoch 5/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.4083 - accuracy: 0.8153 - val_loss: 0.4325 - val_accuracy: 0.8027\n",
      "Epoch 6/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.3965 - accuracy: 0.8199 - val_loss: 0.4244 - val_accuracy: 0.8042\n",
      "Epoch 7/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3870 - accuracy: 0.8235 - val_loss: 0.4164 - val_accuracy: 0.8050\n",
      "Epoch 8/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3789 - accuracy: 0.8235 - val_loss: 0.4082 - val_accuracy: 0.8069\n",
      "Epoch 9/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3721 - accuracy: 0.8246 - val_loss: 0.4001 - val_accuracy: 0.8080\n",
      "Epoch 10/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3668 - accuracy: 0.8255 - val_loss: 0.3948 - val_accuracy: 0.8129\n",
      "Epoch 11/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3623 - accuracy: 0.8291 - val_loss: 0.3889 - val_accuracy: 0.8258\n",
      "Epoch 12/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3586 - accuracy: 0.8468 - val_loss: 0.3846 - val_accuracy: 0.8417\n",
      "Epoch 13/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3554 - accuracy: 0.8533 - val_loss: 0.3817 - val_accuracy: 0.8436\n",
      "Epoch 14/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3528 - accuracy: 0.8550 - val_loss: 0.3769 - val_accuracy: 0.8429\n",
      "Epoch 15/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3511 - accuracy: 0.8552 - val_loss: 0.3754 - val_accuracy: 0.8455\n",
      "Epoch 16/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3492 - accuracy: 0.8572 - val_loss: 0.3732 - val_accuracy: 0.8466\n",
      "Epoch 17/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3474 - accuracy: 0.8589 - val_loss: 0.3719 - val_accuracy: 0.8455\n",
      "Epoch 18/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3458 - accuracy: 0.8589 - val_loss: 0.3700 - val_accuracy: 0.8466\n",
      "Epoch 19/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3441 - accuracy: 0.8600 - val_loss: 0.3698 - val_accuracy: 0.8485\n",
      "Epoch 20/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3420 - accuracy: 0.8599 - val_loss: 0.3728 - val_accuracy: 0.8493\n",
      "Epoch 21/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3415 - accuracy: 0.8593 - val_loss: 0.3666 - val_accuracy: 0.8493\n",
      "Epoch 22/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3395 - accuracy: 0.8602 - val_loss: 0.3669 - val_accuracy: 0.8489\n",
      "Epoch 23/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3387 - accuracy: 0.8608 - val_loss: 0.3618 - val_accuracy: 0.8508\n",
      "Epoch 24/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.3370 - accuracy: 0.8614 - val_loss: 0.3616 - val_accuracy: 0.8504\n",
      "Epoch 25/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3361 - accuracy: 0.8614 - val_loss: 0.3615 - val_accuracy: 0.8504\n",
      "Epoch 26/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3349 - accuracy: 0.8614 - val_loss: 0.3590 - val_accuracy: 0.8474\n",
      "Epoch 27/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3339 - accuracy: 0.8615 - val_loss: 0.3592 - val_accuracy: 0.8482\n",
      "Epoch 28/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3332 - accuracy: 0.8623 - val_loss: 0.3586 - val_accuracy: 0.8497\n",
      "Epoch 29/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3326 - accuracy: 0.8628 - val_loss: 0.3581 - val_accuracy: 0.8497\n",
      "Epoch 30/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3319 - accuracy: 0.8651 - val_loss: 0.3570 - val_accuracy: 0.8508\n",
      "Epoch 31/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3315 - accuracy: 0.8643 - val_loss: 0.3561 - val_accuracy: 0.8508\n",
      "Epoch 32/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.3312 - accuracy: 0.8655 - val_loss: 0.3585 - val_accuracy: 0.8504\n",
      "Epoch 33/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3304 - accuracy: 0.8647 - val_loss: 0.3572 - val_accuracy: 0.8508\n",
      "Epoch 34/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3304 - accuracy: 0.8638 - val_loss: 0.3577 - val_accuracy: 0.8508\n",
      "Epoch 35/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3298 - accuracy: 0.8645 - val_loss: 0.3564 - val_accuracy: 0.8504\n",
      "Epoch 36/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3297 - accuracy: 0.8643 - val_loss: 0.3549 - val_accuracy: 0.8497\n",
      "Epoch 37/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3294 - accuracy: 0.8668 - val_loss: 0.3549 - val_accuracy: 0.8508\n",
      "Epoch 38/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3288 - accuracy: 0.8638 - val_loss: 0.3577 - val_accuracy: 0.8504\n",
      "Epoch 39/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3290 - accuracy: 0.8636 - val_loss: 0.3545 - val_accuracy: 0.8527\n",
      "Epoch 40/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3286 - accuracy: 0.8653 - val_loss: 0.3570 - val_accuracy: 0.8501\n",
      "Epoch 41/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3283 - accuracy: 0.8638 - val_loss: 0.3555 - val_accuracy: 0.8519\n",
      "Epoch 42/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3274 - accuracy: 0.8649 - val_loss: 0.3605 - val_accuracy: 0.8516\n",
      "Epoch 43/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3281 - accuracy: 0.8645 - val_loss: 0.3556 - val_accuracy: 0.8531\n",
      "Epoch 44/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3273 - accuracy: 0.8666 - val_loss: 0.3570 - val_accuracy: 0.8512\n",
      "Epoch 45/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3270 - accuracy: 0.8649 - val_loss: 0.3585 - val_accuracy: 0.8523\n",
      "Epoch 46/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3272 - accuracy: 0.8662 - val_loss: 0.3572 - val_accuracy: 0.8523\n",
      "Epoch 47/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3271 - accuracy: 0.8656 - val_loss: 0.3553 - val_accuracy: 0.8523\n",
      "Epoch 48/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3272 - accuracy: 0.8668 - val_loss: 0.3552 - val_accuracy: 0.8527\n",
      "Epoch 49/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3266 - accuracy: 0.8662 - val_loss: 0.3549 - val_accuracy: 0.8523\n",
      "Epoch 50/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3268 - accuracy: 0.8670 - val_loss: 0.3552 - val_accuracy: 0.8508\n",
      "Epoch 51/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3267 - accuracy: 0.8638 - val_loss: 0.3553 - val_accuracy: 0.8531\n",
      "Epoch 52/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3266 - accuracy: 0.8649 - val_loss: 0.3559 - val_accuracy: 0.8519\n",
      "Epoch 53/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3265 - accuracy: 0.8662 - val_loss: 0.3551 - val_accuracy: 0.8519\n",
      "Epoch 54/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3261 - accuracy: 0.8643 - val_loss: 0.3553 - val_accuracy: 0.8519\n",
      "Epoch 55/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3260 - accuracy: 0.8677 - val_loss: 0.3564 - val_accuracy: 0.8550\n",
      "Epoch 56/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3261 - accuracy: 0.8653 - val_loss: 0.3562 - val_accuracy: 0.8508\n",
      "Epoch 57/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3259 - accuracy: 0.8658 - val_loss: 0.3556 - val_accuracy: 0.8516\n",
      "Epoch 58/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3258 - accuracy: 0.8655 - val_loss: 0.3555 - val_accuracy: 0.8523\n",
      "Epoch 59/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3257 - accuracy: 0.8653 - val_loss: 0.3570 - val_accuracy: 0.8538\n",
      "Epoch 60/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3256 - accuracy: 0.8673 - val_loss: 0.3565 - val_accuracy: 0.8523\n",
      "Epoch 61/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3256 - accuracy: 0.8656 - val_loss: 0.3573 - val_accuracy: 0.8557\n",
      "Epoch 62/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3251 - accuracy: 0.8675 - val_loss: 0.3655 - val_accuracy: 0.8535\n",
      "Epoch 63/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3257 - accuracy: 0.8651 - val_loss: 0.3555 - val_accuracy: 0.8523\n",
      "Epoch 64/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3256 - accuracy: 0.8660 - val_loss: 0.3561 - val_accuracy: 0.8516\n",
      "Epoch 65/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3256 - accuracy: 0.8662 - val_loss: 0.3571 - val_accuracy: 0.8512\n",
      "Epoch 66/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3255 - accuracy: 0.8675 - val_loss: 0.3566 - val_accuracy: 0.8527\n",
      "Epoch 67/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3254 - accuracy: 0.8670 - val_loss: 0.3567 - val_accuracy: 0.8508\n",
      "Epoch 68/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3254 - accuracy: 0.8675 - val_loss: 0.3560 - val_accuracy: 0.8546\n",
      "Epoch 69/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.3251 - accuracy: 0.8692 - val_loss: 0.3571 - val_accuracy: 0.8542\n",
      "Epoch 70/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3254 - accuracy: 0.8656 - val_loss: 0.3579 - val_accuracy: 0.8535\n",
      "Epoch 71/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3249 - accuracy: 0.8656 - val_loss: 0.3577 - val_accuracy: 0.8550\n",
      "Epoch 72/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3248 - accuracy: 0.8658 - val_loss: 0.3554 - val_accuracy: 0.8535\n",
      "Epoch 73/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3249 - accuracy: 0.8686 - val_loss: 0.3590 - val_accuracy: 0.8527\n",
      "Epoch 74/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3247 - accuracy: 0.8696 - val_loss: 0.3591 - val_accuracy: 0.8542\n",
      "Epoch 75/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8670 - val_loss: 0.3555 - val_accuracy: 0.8535\n",
      "Epoch 76/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3249 - accuracy: 0.8679 - val_loss: 0.3573 - val_accuracy: 0.8535\n",
      "Epoch 77/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3242 - accuracy: 0.8668 - val_loss: 0.3553 - val_accuracy: 0.8519\n",
      "Epoch 78/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3250 - accuracy: 0.8658 - val_loss: 0.3555 - val_accuracy: 0.8519\n",
      "Epoch 79/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3245 - accuracy: 0.8679 - val_loss: 0.3564 - val_accuracy: 0.8527\n",
      "Epoch 80/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3245 - accuracy: 0.8675 - val_loss: 0.3565 - val_accuracy: 0.8546\n",
      "Epoch 81/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3245 - accuracy: 0.8660 - val_loss: 0.3559 - val_accuracy: 0.8523\n",
      "Epoch 82/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8655 - val_loss: 0.3568 - val_accuracy: 0.8554\n",
      "Epoch 83/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3248 - accuracy: 0.8664 - val_loss: 0.3555 - val_accuracy: 0.8516\n",
      "Epoch 84/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3242 - accuracy: 0.8673 - val_loss: 0.3568 - val_accuracy: 0.8535\n",
      "Epoch 85/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8658 - val_loss: 0.3566 - val_accuracy: 0.8527\n",
      "Epoch 86/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3245 - accuracy: 0.8675 - val_loss: 0.3559 - val_accuracy: 0.8523\n",
      "Epoch 87/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8662 - val_loss: 0.3569 - val_accuracy: 0.8508\n",
      "Epoch 88/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3241 - accuracy: 0.8677 - val_loss: 0.3571 - val_accuracy: 0.8531\n",
      "Epoch 89/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3239 - accuracy: 0.8670 - val_loss: 0.3574 - val_accuracy: 0.8554\n",
      "Epoch 90/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3241 - accuracy: 0.8666 - val_loss: 0.3564 - val_accuracy: 0.8527\n",
      "Epoch 91/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3240 - accuracy: 0.8664 - val_loss: 0.3570 - val_accuracy: 0.8523\n",
      "Epoch 92/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3241 - accuracy: 0.8673 - val_loss: 0.3587 - val_accuracy: 0.8512\n",
      "Epoch 93/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3235 - accuracy: 0.8666 - val_loss: 0.3583 - val_accuracy: 0.8535\n",
      "Epoch 94/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3240 - accuracy: 0.8653 - val_loss: 0.3611 - val_accuracy: 0.8501\n",
      "Epoch 95/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3236 - accuracy: 0.8671 - val_loss: 0.3597 - val_accuracy: 0.8531\n",
      "Epoch 96/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3236 - accuracy: 0.8662 - val_loss: 0.3590 - val_accuracy: 0.8519\n",
      "Epoch 97/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3233 - accuracy: 0.8656 - val_loss: 0.3613 - val_accuracy: 0.8538\n",
      "Epoch 98/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3236 - accuracy: 0.8651 - val_loss: 0.3590 - val_accuracy: 0.8531\n",
      "Epoch 99/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3233 - accuracy: 0.8688 - val_loss: 0.3591 - val_accuracy: 0.8516\n",
      "Epoch 100/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3235 - accuracy: 0.8670 - val_loss: 0.3585 - val_accuracy: 0.8519\n",
      "CPU times: total: 18.9 s\n",
      "Wall time: 1min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Fitting the ANN to the training dataset\n",
    "# validation_split tests the model separately for the test dataset\n",
    "model_history = classifier.fit(X_train, y_train, validation_split = 0.33, batch_size = 10, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3b437b49-36a7-40b1-9efc-4719bc6258e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 789us/step\n"
     ]
    }
   ],
   "source": [
    "# Making the predictions and testing the model\n",
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d148b4db-9a69-436b-8ff6-20ef61135dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1531,   62],\n",
       "       [ 221,  186]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4565a5d-f556-4359-af9b-186545452335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8585"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the accuracy\n",
    "score = accuracy_score(y_pred, y_test)\n",
    "score\n",
    "# All accuracies are almost same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3adac711-53c3-4df9-a2ea-fe14a217a34c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List all data in history\n",
    "print(model_history.history.keys())\n",
    "\n",
    "# Summarize history for accuracy\n",
    "plt.plot(model_history.history['accuracy'])\n",
    "plt.plot(model_history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9078d10-fa11-4d2f-8460-2ce5aee8d801",
   "metadata": {},
   "source": [
    "# ANN with Third Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f4a0268c-d473-4370-8452-43a36ae48970",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initializing the ANN, This is an empty neural network\n",
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b1d5be23-bfb6-4e91-a730-123f11856dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the Input Layer and First Hidden layer\n",
    "classifier.add(Dense(units = 10, kernel_initializer = 'he_normal', activation = 'relu', input_dim = 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c957e37d-46f5-40b6-b502-f3f9ea8a210b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Second Hidden Layer\n",
    "classifier.add(Dense(units = 20, kernel_initializer = 'he_normal', activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d2b8ca59-aced-42e8-91d8-f99e31d33cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Third Hidden layer\n",
    "# By adding multiple layers there's a chance of Overfitting\n",
    "classifier.add(Dense(units = 15, kernel_initializer = 'he_normal', activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a383f227-455c-4d69-ab15-d4815d4243d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the Output Layer\n",
    "classifier.add(Dense(units = 1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1479e640-0bf4-40b6-9951-b4a945f491dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 10)                120       \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 20)                220       \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 15)                315       \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 671\n",
      "Trainable params: 671\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9ae01536-8449-4aed-8029-fe17b0799e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the ANN\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0094de8-d00e-4071-9acf-f886b6562871",
   "metadata": {},
   "source": [
    "### Plotting ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "55b4225a-1996-4ade-9393-fd3efea796dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rfArXmo8LIyMjVtsYY/xt7Ts2H4FL6XRdL5VKdCqfzyuKMj09TdVlm69aD7PZLK1ZeEqgS3Rd19Pp9JkzZ2zp2Ww2mUy6L1h4N+DNxzw0d0ct2yMTExPpdDpyk20BLwzvbyZVVTVNowkMzcrIwkajoaoqvdHX1tYYY9VqlcYZxhiNn/QlSk3TKKtsNluv103TbDabpMB2+XgxrF6vUyabm5teru9iZObFsR22lo63HZ/BappGttGUlWfCv85Jh7YWp5lkR0byfPYcmWlKT01gvZGea6t5W12pqprL5czd9qI5rXtzd92yZlcjMz29WCx6yX8/TrOp+blams0mr0oSNr+S7S69bHVt67h8GUZd3CUfd6wfrA10zexSHLOldNZT1WqV2+b9rq7xImb+ArXdaFom/LytrVeSFHnbUdAsUqlL0bpr2dZ8PJ6izumxM+xHMdPwYk3hVcnfylZM19al3AqFgtVR0S4fL1SrVeqgNGi4I1jM1pSIiNnxWTyFXq+qqpJorVfaugHJRlXV1jyth720bBdidj9lYz+K2XsHbXeL9XBzc5M3MH+D9tibNzc3PeYAMbuL2dydTdAU2qXUZvBFk0bM0XKAuWNzibkzPDxcLBar1aqmael02rpvoaN8bHl2d6MYaEzrF+LxeLFYLJVK2WzWmk6vYJtvyWPRum5ZOYiQmHO5HGOsVqu1O7W0tEQuZS+bihRFMQwjHo/fu3evWq2m0+nu8rFCd3HPXHSgTnzu3LmwDfkGkqj7ZilyWd26dcuaeOHCBcbY48eP6ZBymJiYcH9cjy3bHcH9tNElAkZ/j9MM8jOpqkouUHKEMMY0TeNOWk69XueJtCrmDjO+DMtkMpRVvV6nmbZjPi4mqapq84p7dKt0Mc3mtpH9e5aO7bqFyDBaVZq7a07yLfGQ6+T1pUGv0WhQbQj2ZrfbHGJzlZF7jC+nC4UCGe9eIe1alt4pLp5tnk/rJhCXU/Bm7029Xqe+SAKmNze1Fv9xSNM0aifbK6n1kHote9nr2JqPC9QjiWw2a9tD4kIXYt6zOK2H/Pe5XC7HO1y9XqdE6mrWOqRlaiaTocNAxUzq4jVmU5rtYv4m4vfSSMssLkz3CjHbtGwmk9E0zZa/tSDtDHM5Ze6+JT3uWhMmZhFf56Q5krCPp0SB1dXVycnJ4OqWdn0IaLt2T19ZWdnzs0E00b1+/boQo9xIJBLW93LvzM7OHjlyxGPRhPX/CK2ZgWSkUqlHjx5VKpVwzahUKjMzMz5mWKvVarVaKpXyMU9fgJj7D+7pjdx2wpeJxWKLi4u3b992dGqKYX19/ejRo9ZtuT2ytbV1//79xcXFWCzmV55+ATHb/wov3D/K88Lx48dt/4ksQ0NDS0tLDx8+DMuAs2fP+vuDYqlUunHjRjTDCeDrnKGtPLumvwyOxWJRWDb7RZTLgpEZAEmAmAGQBIgZAEmAmAGQBEEOsJ2dndXVVTHPigK0Q0jiIvONomBPdnZ2Tp48KeJJAnaZIaQr2OdI9SeQYgoTHSL4QT8fYd4+6AcIYYMZ1swASALEDIAkQMwASALEDIAkQMwASALEDIAkQMwgWBDSVRiRFrPjHxjPz8+XSqXIVqgAfInMKia8a/+GdGWMUUCvRCJhjSsc5ZCukRaz2fJNRtM0x8bG8vl8ZCtUABsbGxHJxB3DMFKp1KVLlyh+GH1V16Zn8+WvdgZtko1sNvvxxx9fvny5NQz48vJyPp9fWlpaWlr65S9/mc/nKT0ej8/MzKRSqSgOJ2J2wPSyA6zVTms8sZ6tC4TgdoD5Epm1x0yY7CFd6Uu6/NOi9FVT6/d6EdLVN4aGhq5cuVIqlazDSygxX3vHMXyp98iskQ3v2tchXT/55BPG2GuvvUaHr776KmPs008/5RcgpGuXONpJHyh3j+gpMuarDe8js2P4Uu+RWXk7igzvymQP6eoYw9D68W18BL9L2r10rOnhxnxtxaOYuwtf6nLKFBLe1YuY+zqk654pCOnaJV7EHIWYr1Y8irm78KXuYramhChmx/x5SsRDunaX0g6I+Rsca43amL9929WsS/MHEfOV41HMvuiwH8VsRjuka6t3kFmm9J0+EQ6wPfjss88YYzb/SugxXzull/Cl7kQ/vGuUQ7rabCA321tvveVL5sHRl2LWdX1hYUFV1bNnz1JKRGK+dkp34UvdiUh4174O6free+9ZbXj69ClPtIKQrp3RGlaT3NR8uUWEFfO1HR6n2e3Cl5qdRGalUyLDu7J9ENI1l8vRXhf6gYC86xx4szvG8e3TLrRqKDFf2+H9pynH8KVmJ5FZ6V6R4V29iLnfQ7qau+8jVVXX1tZsNyKkK0K6BoL48K4I6YqQrmB/gZCugoGY+5soh3dFSFfBQMz9TcTDuyKkq0gQ0rW/EblU7g6EdBUGRmYAJAFiBkASIGYAJAFiBkASBDnAKpVKj1uO+4udnR3W8y7rKHP37t19tQuoFyqVio+/jbkgYgfYnTt3EM43RP793/+dMfbDH/4wbEP2L6Ojo9euXQv6KSLEDMKF9l1KHPkdEFgzAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxAyAJimmaYdsAfOZf//Vf79y58/XXX9Phs2fPGGPHjh2jwwMHDly7du2DDz4IzT4QDBCzhGxtbf3FX/yFywWbm5vDw8PC7AFiwDRbQoaHh+PxuKIoracURYnH41CylEDMcvLBBx8cOHCgNf3gwYOXLl0Sbw8QAKbZcvL06dNTp069ePHClq4oypMnT15//fVQrAKBgpFZTl577bW//Mu/HBh4qX0HBgb+6q/+CkqWFYhZWqampmwpiqLAiS0xmGZLy3/9138dP378+fPnPOXgwYNffPHFd77znRCtAsGBkVla/vRP//Sv//qvuRvswIED7733HpQsMRCzzFy8eJH7wEzTvHjxYrj2gEDBNFtm/ud//uc73/nOH/7wB8bYt771rWfPnh0+fDhso0BQYGSWmVdeeeWnP/3p4ODg4ODgT3/6UyhZbiBmyblw4cLz58+fP39+4cKFsG0BwXJQwDPK5fKTJ08EPAi08vXXX7/yyiumaf7+979fXV0N25x9yqlTp0ZHRwN/jBk84+PjgRcDgAgzPj4uQGiCptliChMdVlZWmJAXpRd+/etfP3r0yMcMGWMrKys+Zig3wgYzEdNsEC7vvPNO2CYAEUDM8mPboQ1kBc0MgCRAzABIAsQMgCRAzABIQnTFrOv68vJyIpEI2xBxzM7Ozs7Ohm2Fz+i6Pj8/H7YVvjE/P28YRthWOBNdMc/NzSWTyVKpFLYh35DP5x2/ktcvGIYh2H5d1+fm5g4fPqwoiqIora8q5WVE2sYYMwyjUqnk83nHMaNUKiUSiUQiYe2EY2NjU1NTuq4LNNMzYn40727TiDALvVCtVr3bE6lNI5xiseiLVczbppFms6mqarlcpv8XCgXGWCaTsV3WaDQYY41Go3fDOiWTyWQyGcdmLRQKqqo2m81ms6lpWi6X46fK5TKd8viUrvt/p0R3ZI4UhmF89NFHYVvRE4Zh5PN5kU9cXFyMx+MjIyOMsVgs9v777zPGbt26tby8bL1saGiI/yuYmzdv3rx5szV9e3s7mUzOzMzEYrFYLKZp2uXLl2u1Gp0dGRl5/fXXFxcXxRq7N9ESs2EYy8vLiqIkEomtrS3rKVp60an19XX28qK6VCrRqe3tbX4LXZ/P53Vd51O41ny8sLi4+A//8A/+FLINNh+BS+l0XacZINud+U9PT1N12ear1sNsNkvTRZ4S6BJd1/V0On3mzBlbejabTSaTNj3b4N2ANx/z0Nzdtawjn3zyCWPstddeo8NXX32VMfbpp5/yCyYmJtLpdOQm2wJGf+/TDFVVNU2jCQzNysjCRqOhqmqhUDBNc21tjTFWrVZVVaULaCJXr9cZY5qmUVbZbLZer5um2Ww2aSrVLp89rVpbW6NHeK+xLqbZvDi2w9bS8bbjM1hN0xhjm5ubNGXlmdBd/NBmP00yOzKS57PnNJum9NQE1hvpubaat9WVqqo0raX2ojmte3N317L86TYDqD5t16iqyg/p6cVi0Uv+wqbZERIzNf/m5iYdNptNXsskbH4l21162ZrB1nH5Moy6uEs+LjQaDb5eClTMrfm7l856itbz2Wy2o7u6xouY+QvUdqO5u5a2trX1SpIib7tyucwYI5W6FK2LlnXMx2MKdU6q8D3Zj2J2fB1SCn8rWzFdW5dyKxQKVkdFu3xcsHo+Iitma0pExOz4LJ5Cr1dVVUm01itt3YBkQ6OiS9G6aFkXU7tLacd+FLP3DtruFuvh5uYmb2D+Bu20NxeLRetEEWLm+fQoZnN3NkFTaJdSm8EXrfVe6jm2a/iUvtMnwpvtgM0l5s7w8HCxWKxWq5qmpdNp674F7/kkEok33nij1avk3QyR0JjWL8Tj8WKxWCqVstmsNZ2EZPMteSxaRz3EBZsN5GZ76623fMk8OCIk5lwuxxjjPwC0nlpaWqLNN142FSmKYhhGPB6/d+9etVpNp9Nd5GN78/HELkoXKNSJz507F7Yh30ASdd8sRS6rW7duWRPpW2WPHz+mQ8phYmLC/XFd9BAX3nvvPasNT58+5YlW+G/UUUHA6O9xmkEeQlVVaWZLjhDGmKZp3EnLqdfrPJFWxdxhxpdhmUyGsqrX6zTTdszHe0G811gX02xuG9m/Z+nYrluI3PXc18o92+au94jtThFpwGk0GlQbgr3Z7TaH2Fxl5B7jy+lCoUDGu1dIu5ald4qLZ5vnY9sEksvl6IeV1k0jJrzZXq6s1+vUF0nA9Oam1qrX69TqmqZRO9leSa2H1GvZy17H1ny8E6iY9yxO6yH/fS6Xy/G+WK/XKZG6mrUOaZmayWToMFAxk7roZySzZS5ju9j6q4+5+wsCf2FR0dwrxGzTsplMRtM0W/7WgrgYRu8jVVXX1tZsN9Jb0uOuNWFiFvERfJojPXjwIOgHRYfV1dXJycng6pbW7QLart3TV1ZWzp8/734ZTXSvX78uxCg3EokEKdMvZmdnjxw54rFowvp/hNbMQDJSqdSjR48qlUq4ZlQqlZmZGR8zrNVqtVotlUr5mKcvQMz9B/eyRm474cvEYrHFxcXbt287OjXFsL6+fvToUdof7gtbW1v3799fXFyMxWJ+5ekXELP9r/DC/aM8Lxw/ftz2n8gyNDS0tLT08OHDsAw4e/bs8PCwjxmWSqUbN26E8mche4Kvc0bxpyZ3+svgWCwWhWWzX0S5LBiZAZAEiBkASYCYAZAEiBkASRDkAKtUKntur5WJnZ0d5mFHcf9y9+7dfbULqBcqlYqPv425gJEZAEkQNDKPjIzsqxc5beeUtciKoly9enXP7ZyAEDZBw8gMgCRAzABIAsQMgCRAzABIAsQMgCRAzCBYEAVSGJEWs+PfJM7Pz5dKpchWqAB8CeYoJiJkxKNAbm9vT09PU3yf1og2fRcFMtJiNls+42aa5tjYWD6fj2yFCmBjYyMimbhjGEYqlbp06RJ9GY8+xGnTs/nyh/6CNslmXq1Wu3fvXrPZ/PGPf/zuu+9aRbu8vJzP55eWlpaWln75y1/ymHvxeHxmZiaVSkVxOBHwnbEeP2jWaqc1BFHP1gVCcCFdeWyXEDNh3kK6ZrNZ2wcDqSnpo6K29O4s6QXbtzWt3Yw+vsm/RkgfQrR+4lPTNI+xaUx8BN+doaGhK1eulEol6/ASbpjIrnGMeOg9mGNkI0JGPwpka0Qb/ql9RIFsi+8js7n7uWP3IIDCwkS24n1kdox46D2YI29HkREhmVxRIM3d7sTHakSBbEsQYralhxgm0hGPYu4u4qHLKVNIRHTNlscAABlSSURBVEgvYu6jKJD0UOuqrbVybCmIAtklXsQcYphIRzyKubuIh+5itqaEKGbH/HlKpKJA0u18hexovJeUdkDM3+BYa9TG/O3brmZdmt+XMJHt8ChmX3TYj2I2oxQFslAo2KLPIAqkUD777DPGmM2/Ij5MZI/0EvHQnehHhIxIFMharfbb3/72ww8/dLEBUSADRNf1hYUFVVXPnj1LKWGFieyR7iIeuhORiJB9EQVS1/WHDx/evHmTDmu12vT0NEMUSBd6mWa0BukjNzVfbhERCRPJ8TjNbhfx0OwkmCOdEhkRkkkRBZIc4La7uIMaUSCd6bowjm+fbDZr9VVwohAmkuP9pynHiIdmJ8Ec6V6RESG9iDn6USAdp+7cwW4iCqQjiAIZKOIjQiIKJKJAgv0FokAKBmLub6IcERJRIAUDMfc3EY8IiSiQIkEUyP5G5FK5OxAFUhgYmQGQBIgZAEmAmAGQBIgZAEmAmAGQBQG7zMbHx8MuJQBhIs92znK5/OTJk6CfAtpx9+5dxtjVq1fDNmT/curUqdHR0aCfIkLMIFxoE/Xq6mrYhoBgwZoZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQBIgZAEmAmAGQhINhGwD859mzZ7///e/54X//938zxh4/fsxT/uRP/uTYsWMhWAaCRDFNM2wbgM/8y7/8y9///d+7XPDP//zPf/d3fyfMHiAGiFlCDMP4sz/7s+fPnzueHRwc/PLLL2OxmGCrQNBgzSwhsVjs3LlzBw86rKEOHjz4N3/zN1CylEDMcnLx4sWvv/66Nf3FixcXL14Ubw8QAKbZcvKHP/zh2LFj5Pqy8sorrzx79uyP/uiPQrEKBApGZjn51re+9bd/+7eDg4PWxMHBwfHxcShZViBmablw4YLNB/b8+fMLFy6EZQ8IGkyzpeV///d/jx8//p//+Z885ciRI19++aWjYwxIAEZmaTl48GAymeQz7cHBwYsXL0LJEgMxy0wymeQz7efPnyeTyXDtAYGCabbMmKZ56tSp3/3ud4yxV1999Xe/+52iKGEbBYICI7PMKIoyNTV16NChQ4cOXbp0CUqWG4zMkvNv//Zv8Xic/vPDH/4wbHNAgIhwh9y5c6dcLgt4EHDkj//4jxlj//iP/xi2IfuX0dHRa9euBf0UEdPscrlcqVQEPCg67OzsfPTRR2Fb8f+88cYbp0+f9jHDjz76aGdnx8cM5aZSqYgZzAT9UDEyMvLgwQMxz4oCq6urk5OTESky/SXzn//5n/uVoaIoV69ePX/+vF8Zys3ExISYB+FXR/nxUcYgysCbDYAkQMwASALEDIAkQMwASEJ0xazr+vLyciKRCNsQcczOzs7OzoZthc/ouj4/Px+2Fb4xPz9vGEbYVjgTXTHPzc0lk8lSqRS2IaxWqym7TE9Ph21O9xiGIXhHp67rc3Nzhw8fptprfVUpLyPSNsbY9vb29PQ0Nev6+rrtbKlUSiQSiUTC2gnHxsampqZ0XRdrqTfM4BkfHx8fH+/iRmEWupPL5Xh1FYtFL7esrKxEwXIbxWLRF6sYYysrK3te1mw2VVUtl8v0/0KhwBjLZDK2yxqNBmOs0Wj0blhHNJtNak1um7VxC4WCqqrNZrPZbGqalsvl+KlyuUynPD6o6/7fKRDz3ngUsJUIipmkJVLM2WzWJl1q0EKh0Jph71Z1iq1ZrZ2tXq8zxug1ZJpmtVpljFWrVX6xpmnZbNbjg4SJOVrTbMMwlpeXFUVJJBJbW1vWU7T0olM0I7IuqkulEp3a3t7mt9D1+Xxe13U+hWvNx53t7e1EIjE7Oxv0jlSbj8CldLqu0wyQMZbP52mWSNVlm69aD7PZLE0XeUqgS3Rd19Pp9JkzZ2zp2Ww2mUwuLy+73Mu7AW8+5qG5O21ZerVZ0TSN/vPJJ58wxl577TU6fPXVVxljn376Kb9yYmIinU5HbrIt4IXh/c2kqqqmaTSBoZkPWdhoNFRVpTf62toaY6xarfLGoDcovU01TaOsstlsvV43TbPZbGYyGZd83E2iqSmhqqrH2WAXIzMvju2wtXTcHj6DpV64ublJU1b28gjDD20tnslkWie9XmAeRmaqN2oC6430XFvN2+pKVVWa1lJ70ZzWvbm7aFkrzWaTWabZVJ82y1VV5Yf0dI9Ttv04zabm39zcpEOqX6pTEja/ku0uvWy909ZxufCoi7vk406z2axWq9QFrWsnF7qbZrsUx2wpnfUUzQNp4uf9rq7xImb+ArXdaFom/LytrVeSFHnb0Z8okEpditZdy1ofal0Gt1aULYU6p8eZ9n4Us+PrkFJaZ0SU7tK6lFuhULA6Ktrl45FcLmd9PbsgWMzWlIiI2fFZPIVer3ymY73S1g1INlTtLkXrsWW5o66d8V5S2rEfxey9g7a7xXq4ubnJG5i/QXvszdSxvFwJMbuL2dydTdB46FJqM/iiFQoF24Sr1VPILFP6Tp+4Tx1g7thcYu4MDw8Xi8VqtappWjqdtu5b6CgfK7FYjPtIIkiUbWslHo8Xi8VSqZTNZq3pJCSbb8lj0bpo2Vqt9tvf/vbDDz90sYHcbG+99VanmQsmQmKmn3NrtVq7U0tLS7T5xsumIkVRDMOIx+P37t2rVqvpdLq7fKwYhiHsb1M7gjrxuXPnwjbkG0ii7pulyGV169YtayJ9pp9Hk6Yc9qz27lpW1/WHDx/evHmTDmu1Gm0Keu+996w2PH36lCdaIb9AhBAw+nucZpCHUFVVcoGSI4Qxpmkad9Jy6vU6T6RVMXeY8WVYJpOhrOr1Os20HfNxMalQKKytrXHzvP/g3MU0m9tG9u9ZOrbrFiJ3PV/Mc8+2ues9YrtTRBpwGo0G1YZgb3a7zSE2Vxm5x/hyulAokPHuFdKuZemd4ujZJge47S7exLlcjn5Yad00YsKb7eXKer1OfZEETG9uaq16vU6trmkatZPtldR6SL2Wvex1bM3HBf67VCaT6einji7EvGdxWg/573O5XI77+er1OiVSV7PWIS1TM5kMHQYqZlIX9yrZNGO72OZWbDQafNcdd2G6V4jZpmUzmYymaY5uS8epO3ewm7utr6oqf6Fz6C3p8XdKYWIW8XVOmiNF5Bs6YqDPBgVXt7TrQ0DbtXv6ysrKnp8Noonu9evXhRjlRiKRsO4X6J3Z2dkjR454LJqw/h+hNTOQjFQq9ejRo9C/5VipVGZmZnzMsFar1Wq1VCrlY56+ADH3H9zLGrnthC8Ti8UWFxdv377t6NQUw/r6+tGjR0dGRvzKcGtr6/79+4uLi7FYzK88/QJitv8VXrh/lOeF48eP2/4TWYaGhpaWlh4+fBiWAWfPnh0eHvYxw1KpdOPGjaGhIR/z9At8nTO0lWfX9JfBsVgsCstmv4hyWTAyAyAJEDMAkgAxAyAJEDMAkgAxAyALAnaZjY+Ph11KAMJEzHZOcVEgr169KuZZUaBcLi8sLNAObfmYnJy8cuXK6Oho2Ib0B3fv3hXzIEFiPnny5H6LALqwsCBrkScnJ0dHR2Utne8I+6sErJkBkASIGQBJgJgBkASIGQBJgJgBkASIGQQLQroKI9JidvwD4/n5+VKpFNkKFYAvkVnFhHeNeEhXwzAqlUo+n3cMA+4YyjfKIV0jLWaz5ZuMpmmOjY3l8/nIVqgANjY2IpKJO4ZhpFKpS5cu0Wcu6au6Nj2bL3+1M2iTbGSz2Y8//vjy5cuOYcCtkeL4Z4zj8fjMzEwqlYrgcBJpMTPG+Ccd+Fda4vH44uIiYyyaFRo0hmHk8/koZLIni4uL8XicPtkTi8Xef/99xtitW7dsISCpiUP5dsfNmzf5R7NbOXHiBN8paf0o78jIyOuvv06dMFJEXcyODA0NXblypVQqWYeXsGK+9ohj+FLvkVkjG941+iFd3XEP5YuQrl3iaCd9A909oqeYmK+OeP9utmP4Uu+RWXk7igzvyuQK6erYwdxD+eIj+F3S7qVjTQ835msrHsXcXfhSl1OmkPCuXsTcRyFd21WFSyhfhHTtEi9ijkjMV45HMXcXvtRdzNaUEMXsmD9PiVRI1z0vdgzl6/0REPM3ONYatTF/+7arWZfmDyLmK8ejmH3RYT+K2YxSSNc973UM5RtBMfelA4wx9tlnnzHGbP6V0GO+dkov4UvdiX5414iEdPVCxEP5cvpSzLquLywsqKp69uxZSolIzNdO6S58qTsRCe/aFyFdPdIulC9CunYGj9zJV7bkprY5GMOK+doOj9PsduFLzU4is9IpkeFdmRQhXfnjbB3M9BDKF97sjnF8+2SzWR4o1EooMV/b4f2nKcfwpWYnkVnpXpHhXb2IOfohXVut4lntGcoXIV0R0jUQxId3RUhXhHQF+wuEdBUMxNzfRDm8K0K6CgZi7m8iHt4VIV1FgpCu/Y3IpXJ3IKSrMDAyAyAJEDMAkgAxAyAJEDMAkiDIAbazs7O6uirmWVGAdghJXGS+ORTsyc7OzsmTJ0U8ScAuM4R0BfscebZzgnChfZcSTxMAgTUzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJIAMQMgCRAzAJJwMGwDgP9sbGyUy2V++PnnnzPGfvGLX/CU0dHRH/3oRyFYBoJEMU0zbBuAz6ytrY2NjQ0ODg4M2GdeL168eP78+cOHD999991QbAPBATFLyIsXL06cOPHll186nj127NgXX3xx4MABwVaBoMGaWUIGBgZ+9rOfHTp0qPXUoUOHLl68CCVLCcQsJ8lk8quvvmpN/+qrr5LJpHh7gAAwzZaW06dP1+t1W+KpU6fq9bqiKKGYBAIFI7O0TE1NDQ4OWlMGBwd//vOfQ8mygpFZWj7//PPvf//7tsTf/OY3b775Zij2gKDByCwt3/ve9958803rOPyDH/wASpYYiFlmPvjgA+64HhwcvHTpUrj2gEDBNFtmnjx58sYbb1ATK4ry+PHj06dPh20UCAqMzDJz6tSpt99+e2BgYGBg4O2334aS5QZilpypqSlFUQYGBqampsK2BQQLptmS8+zZsxMnTjDGnj59OjQ0FLY5IEjM4BkfHw+7lACEyfj4uAChCfoTyJGRkatXr4p5VhQol8sLCwsrKythG8IYYxsbG4qivPPOO35lODk5eeXKldHRUb8ylJu7d++KeZAgMZ88efL8+fNinhURFhYWIlLkn/zkJ4yxb3/7235lODk5OTo6GpHSRZ8HDx6IeRA+TiA/PsoYRBl4swGQBIgZAEmAmAGQBIgZAEmIrph1XV9eXk4kEmEbIo7Z2dnZ2dmwrfAZXdfn5+fDtsI35ufnDcMI2wpnoivmubm5ZDJZKpXCNuT/qdVq+Xw+kUj07x/3G4Yh2Hhd1+fm5g4fPqwoiqIora8q5WVE2sYYMwyjUqlQs7aerdVq3LDp6WlKHBsbm5qa0nVdrKXeELAxZXx8vLsdMMIs3JNsNquqarFYrNfrXq6n7SJBW9UpxWLRF6sYYysrK3te1mw2VVUtl8v0/0KhwBjLZDK2yxqNBmOs0Wj0blinZDKZTCbTrpvlcjkuk2KxyNPL5bKqqs1m0+NTuu7/nRLdkTk6TE9PN5vNpaUlVVW/+93vhm1OlxiGkc/nRT5xcXExHo+PjIwwxmKx2Pvvv88Yu3Xr1vLysvUy2jEeyr7xmzdv3rx5s93ZEydOcJ2oqsrTR0ZGXn/99cXFRSE2dkC0xGwYxvLysqIoiURia2vLeoqWXnRqfX2dvbyoLpVKdGp7e5vfQtfn83ld1/kUrjUfd2hmePPmzVgs5mNJW7H5CFxKp+t6qVSiU/l8niaBVF22+ar1MJvN0pqFpwS6RNd1PZ1OnzlzxpaezWaTyaRNzzZ4N+DNxzw0d6ct68729nYikZidna1UKq1nJyYm0ul05CbbAkZ/79MMVVU1TaMJDM3KyMJGo6GqaqFQME1zbW2NMVatVvnLkiZy9CVKTdMoq2w2S1PiZrNJU6l2+bjYU61WGWPFYpFmXKqqrq2teSlIF9NsXhzbYWvpeNvxGaymaYyxzc1NmrLyTPjXOenQ1uI0yezISJ7PntNsmtLbViX0dGoOa83b6kpV1VwuZ+62F81p3Zu705a1WdXaWGQ/oaqqbRVAT7fOvV0QNs2OkJip+jY3N+mw2WzyWiZh8yvZ7tLL1gy2jssbgLq4Sz7tyGazvFtwzVBncqe7NbNLccyW0llP0Usnm812dFfXeBEzf4HabjR319LWtrZeSVLkbUdBs0ilLkXrtGVtVjlWS7PZrFarVBB6uVhP8Qrfk/0oZpKKNYXXsnXFwjFdW5dyKxQKVkdFu3za4agZPhq4IFjM1pSIiNnxWTyFXq98xLNeaesGJBtVVVvztB522rJ7mmoll8uRAR3dxdmPYvbeQdvdYj3c3NzkDczfoJ32ZheT3IGY3cVs7r4ZaQrtUmoz+KLtea/Nwk6fCG+2AzaXmDvDw8PFYrFarWqalk6nrfsWvOdDo4Rtk4DjIBAFyNp+IR6PF4vFUqlEaxkOVa/Nt+SxaB31EO/EYrG+qNsIiZmcTLVard2ppaUl0pWXTUWKohiGEY/H7927V61W0+l0F/lMTEwwxv7jP/6DDumuCxcudFq0oKFOfO7cubAN+QaSqPtmKXJZ3bp1y5pI1fv48WM6pByoIVzoood4xzAMRwP4b9RRQcDo73GaQR5CVVXJBUqOEMaYpmncScup1+s8kVbF3GHGl2GZTIayqtfrNNN2zMfdqkwmw5d2jmsnR7qYZnPb6Fl7lo7tuoXIXc8N455tc9d7xHbX+TToNRoNqg3B3ux2m0NsrjJyj/E6LxQKZLx7hbRrWasL0xGej9W3UigU+M8W9Xq91WsNb/be1Ot16oskYHpzU2vV63VqdU3TqJ1sr6TWQ+q17GWvY2s+e8J3AuVyOY/7froQ857FaT3kv89ZDavX65RIXc1ah7RMzWQydBiomEld3PNvU5rtYtsrstFo8DrnLkz3CjHbtGwmk9E0rd0rmLVA6fx3qUwm4/gioLekx11rwsQs4uucNEUR9vGUKLC6ujo5ORlc3dKuDwFt1+7pKysre342iCa6169fF2KUG4lEwvq7ce/Mzs4eOXLEY9GE9f8IrZmBZKRSqUePHjnuoBJJpVKZmZnxMcNarVar1VKplI95+gLE3H9wT2/kthO+TCwWW1xcvH37tqNTUwzr6+tHjx6l/eG+sLW1df/+/cXFxaC393YBxGz/K7xw/yjPC8ePH7f9J7IMDQ0tLS09fPgwLAPOnj07PDzsY4alUunGjRvRDCeAr3OGtvLsmv4yOBaLRWHZ7BdRLgtGZgAkAWIGQBIgZgAkAWIGQBIEOcB2dnZWV1fFPCsK0A4hiYvMN4qCPdnZ2Tl58qSIJwnYZYaQrmCfI9WfQIopTHSI5tc5/YJ5+zonIIQNZlgzAyAJEDMAkgAxAyAJEDMAkgAxAyAJEDMAkgAxA6H0Y4TXKIdxtRJpMTv+gfH8/HypVOqLyg0IXyKzig/vyvo2wmukw7haiLSYzZZvMpqmOTY2ls/n+6JyA2JjYyMimXSEYRipVOrSpUsUTow+smvTs/nyRzwFW5jNZj/++OPLly/booLH4/GZmZlUKhXxISTSYmaWSJ/8Ky3xeJyiaUa/coPAl8is4sO7sj6P8BrZMK5Woi5mR4aGhq5cuVIqlazDS1gxX3vEMXyp98isfRHelfV/hFcW2TCuVsTsTe1lb7ajnfTtcveIngJivrbD+95sx/Cl3iOz8nYUGd6Vdb43u98jvJodfvjeyj79CL4j7SrXmh5WzNd2eBRzd+FLXU6ZQsK7diFmOSK8Ms9hXK1AzN/gRcxhxXxth0cxdxe+1F3M1pToiNnxcTylXyK8dldpEPM3ONYgNSp/3barZZf29iXmazs8itkXHUogZrNPIrxGXMx96QBjjH322WeMMZtDRXzM1x7pJXypO30RgtRKH0V4jSx9KWZd1xcWFlRVPXv2LKWEFfO1R7oLX+pOBMO7MokivEYujKsVAaN/L9OM1oib5Kbm6ysixJivjnicZrcLX2p2EpmVTokM78r88Gb3UYRXAt5s0+yhMI5vn2w2ywOFWgkx5msr3n+acgxfanYSmZXuFRnetQsx93WEV6KjMK5WENK1vwk6pKsV8eFdPYZ0tdHvEV47CuNqBSFdgWz0dYTXyIZxtQIx9zf9Et6V9XOE1yiHcbUCMfc3fRTelfVthNcoh3G1gpCu/Y3IpbIv9GOE134xGCMzAJIAMQMgCRAzAJIAMQMgCYIcYJVKpcctx/3Fzs4O63mXdZS5e/fuvtoF1AuVSqXTH8O6Q8QOsDt37iCcL9jPjI6OXrt2LeiniBAzAEAAWDMDIAkQMwCSADEDIAkQMwCS8H8zmvO1i4I2uAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the model architecture\n",
    "plot_model(classifier, to_file='../images/2 ANN with Third Layer.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8809dc85-4a1e-4526-8843-b14d8e6440fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4845 - accuracy: 0.8020 - val_loss: 0.4659 - val_accuracy: 0.7944\n",
      "Epoch 2/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4240 - accuracy: 0.8162 - val_loss: 0.4416 - val_accuracy: 0.8152\n",
      "Epoch 3/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4034 - accuracy: 0.8300 - val_loss: 0.4246 - val_accuracy: 0.8194\n",
      "Epoch 4/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3852 - accuracy: 0.8378 - val_loss: 0.4040 - val_accuracy: 0.8323\n",
      "Epoch 5/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3674 - accuracy: 0.8479 - val_loss: 0.3955 - val_accuracy: 0.8413\n",
      "Epoch 6/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3552 - accuracy: 0.8563 - val_loss: 0.3815 - val_accuracy: 0.8425\n",
      "Epoch 7/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3475 - accuracy: 0.8556 - val_loss: 0.3764 - val_accuracy: 0.8421\n",
      "Epoch 8/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3428 - accuracy: 0.8582 - val_loss: 0.3715 - val_accuracy: 0.8463\n",
      "Epoch 9/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3384 - accuracy: 0.8597 - val_loss: 0.3731 - val_accuracy: 0.8455\n",
      "Epoch 10/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3364 - accuracy: 0.8591 - val_loss: 0.3694 - val_accuracy: 0.8482\n",
      "Epoch 11/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3349 - accuracy: 0.8612 - val_loss: 0.3709 - val_accuracy: 0.8455\n",
      "Epoch 12/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3320 - accuracy: 0.8608 - val_loss: 0.3684 - val_accuracy: 0.8417\n",
      "Epoch 13/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3298 - accuracy: 0.8660 - val_loss: 0.3727 - val_accuracy: 0.8463\n",
      "Epoch 14/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3295 - accuracy: 0.8627 - val_loss: 0.3713 - val_accuracy: 0.8417\n",
      "Epoch 15/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3279 - accuracy: 0.8655 - val_loss: 0.3659 - val_accuracy: 0.8455\n",
      "Epoch 16/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3267 - accuracy: 0.8662 - val_loss: 0.3713 - val_accuracy: 0.8455\n",
      "Epoch 17/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3253 - accuracy: 0.8673 - val_loss: 0.3712 - val_accuracy: 0.8474\n",
      "Epoch 18/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3235 - accuracy: 0.8653 - val_loss: 0.3670 - val_accuracy: 0.8451\n",
      "Epoch 19/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3234 - accuracy: 0.8662 - val_loss: 0.3673 - val_accuracy: 0.8451\n",
      "Epoch 20/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3202 - accuracy: 0.8688 - val_loss: 0.3675 - val_accuracy: 0.8451\n",
      "Epoch 21/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3207 - accuracy: 0.8684 - val_loss: 0.3886 - val_accuracy: 0.8444\n",
      "Epoch 22/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3183 - accuracy: 0.8696 - val_loss: 0.3712 - val_accuracy: 0.8425\n",
      "Epoch 23/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3201 - accuracy: 0.8677 - val_loss: 0.3688 - val_accuracy: 0.8440\n",
      "Epoch 24/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3180 - accuracy: 0.8683 - val_loss: 0.3709 - val_accuracy: 0.8425\n",
      "Epoch 25/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3175 - accuracy: 0.8714 - val_loss: 0.3716 - val_accuracy: 0.8425\n",
      "Epoch 26/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3164 - accuracy: 0.8686 - val_loss: 0.3708 - val_accuracy: 0.8417\n",
      "Epoch 27/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3165 - accuracy: 0.8705 - val_loss: 0.3732 - val_accuracy: 0.8410\n",
      "Epoch 28/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3149 - accuracy: 0.8694 - val_loss: 0.3760 - val_accuracy: 0.8417\n",
      "Epoch 29/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3147 - accuracy: 0.8726 - val_loss: 0.3703 - val_accuracy: 0.8448\n",
      "Epoch 30/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3143 - accuracy: 0.8698 - val_loss: 0.3749 - val_accuracy: 0.8440\n",
      "Epoch 31/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3136 - accuracy: 0.8720 - val_loss: 0.3750 - val_accuracy: 0.8444\n",
      "Epoch 32/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3135 - accuracy: 0.8698 - val_loss: 0.3725 - val_accuracy: 0.8432\n",
      "Epoch 33/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3112 - accuracy: 0.8714 - val_loss: 0.3743 - val_accuracy: 0.8421\n",
      "Epoch 34/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3133 - accuracy: 0.8714 - val_loss: 0.3755 - val_accuracy: 0.8402\n",
      "Epoch 35/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3110 - accuracy: 0.8712 - val_loss: 0.3763 - val_accuracy: 0.8444\n",
      "Epoch 36/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3099 - accuracy: 0.8726 - val_loss: 0.3741 - val_accuracy: 0.8421\n",
      "Epoch 37/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3102 - accuracy: 0.8712 - val_loss: 0.3777 - val_accuracy: 0.8421\n",
      "Epoch 38/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3097 - accuracy: 0.8739 - val_loss: 0.3770 - val_accuracy: 0.8429\n",
      "Epoch 39/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3099 - accuracy: 0.8707 - val_loss: 0.3777 - val_accuracy: 0.8406\n",
      "Epoch 40/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3091 - accuracy: 0.8729 - val_loss: 0.3798 - val_accuracy: 0.8402\n",
      "Epoch 41/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3081 - accuracy: 0.8718 - val_loss: 0.3912 - val_accuracy: 0.8417\n",
      "Epoch 42/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3076 - accuracy: 0.8707 - val_loss: 0.3793 - val_accuracy: 0.8429\n",
      "Epoch 43/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3077 - accuracy: 0.8724 - val_loss: 0.3791 - val_accuracy: 0.8432\n",
      "Epoch 44/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3069 - accuracy: 0.8716 - val_loss: 0.3854 - val_accuracy: 0.8413\n",
      "Epoch 45/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3069 - accuracy: 0.8705 - val_loss: 0.3827 - val_accuracy: 0.8432\n",
      "Epoch 46/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3054 - accuracy: 0.8733 - val_loss: 0.3803 - val_accuracy: 0.8421\n",
      "Epoch 47/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3050 - accuracy: 0.8742 - val_loss: 0.3808 - val_accuracy: 0.8421\n",
      "Epoch 48/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3056 - accuracy: 0.8709 - val_loss: 0.3827 - val_accuracy: 0.8406\n",
      "Epoch 49/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3056 - accuracy: 0.8733 - val_loss: 0.3785 - val_accuracy: 0.8413\n",
      "Epoch 50/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3063 - accuracy: 0.8739 - val_loss: 0.3854 - val_accuracy: 0.8406\n",
      "Epoch 51/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3055 - accuracy: 0.8729 - val_loss: 0.3789 - val_accuracy: 0.8421\n",
      "Epoch 52/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3038 - accuracy: 0.8746 - val_loss: 0.3819 - val_accuracy: 0.8425\n",
      "Epoch 53/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3038 - accuracy: 0.8724 - val_loss: 0.3813 - val_accuracy: 0.8436\n",
      "Epoch 54/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3040 - accuracy: 0.8709 - val_loss: 0.3801 - val_accuracy: 0.8376\n",
      "Epoch 55/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3033 - accuracy: 0.8729 - val_loss: 0.3808 - val_accuracy: 0.8448\n",
      "Epoch 56/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3030 - accuracy: 0.8752 - val_loss: 0.3860 - val_accuracy: 0.8444\n",
      "Epoch 57/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3031 - accuracy: 0.8726 - val_loss: 0.3844 - val_accuracy: 0.8444\n",
      "Epoch 58/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3018 - accuracy: 0.8735 - val_loss: 0.3901 - val_accuracy: 0.8413\n",
      "Epoch 59/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3017 - accuracy: 0.8716 - val_loss: 0.3832 - val_accuracy: 0.8451\n",
      "Epoch 60/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3011 - accuracy: 0.8759 - val_loss: 0.3946 - val_accuracy: 0.8432\n",
      "Epoch 61/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3011 - accuracy: 0.8753 - val_loss: 0.3832 - val_accuracy: 0.8406\n",
      "Epoch 62/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3012 - accuracy: 0.8692 - val_loss: 0.3818 - val_accuracy: 0.8395\n",
      "Epoch 63/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.3002 - accuracy: 0.8753 - val_loss: 0.3964 - val_accuracy: 0.8448\n",
      "Epoch 64/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2986 - accuracy: 0.8737 - val_loss: 0.3822 - val_accuracy: 0.8402\n",
      "Epoch 65/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3000 - accuracy: 0.8733 - val_loss: 0.3865 - val_accuracy: 0.8398\n",
      "Epoch 66/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2997 - accuracy: 0.8740 - val_loss: 0.3864 - val_accuracy: 0.8429\n",
      "Epoch 67/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2995 - accuracy: 0.8746 - val_loss: 0.3821 - val_accuracy: 0.8459\n",
      "Epoch 68/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2986 - accuracy: 0.8739 - val_loss: 0.3861 - val_accuracy: 0.8406\n",
      "Epoch 69/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2981 - accuracy: 0.8765 - val_loss: 0.3907 - val_accuracy: 0.8417\n",
      "Epoch 70/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2991 - accuracy: 0.8718 - val_loss: 0.3865 - val_accuracy: 0.8402\n",
      "Epoch 71/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2983 - accuracy: 0.8753 - val_loss: 0.3862 - val_accuracy: 0.8413\n",
      "Epoch 72/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2966 - accuracy: 0.8740 - val_loss: 0.3838 - val_accuracy: 0.8413\n",
      "Epoch 73/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2973 - accuracy: 0.8757 - val_loss: 0.3971 - val_accuracy: 0.8436\n",
      "Epoch 74/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2967 - accuracy: 0.8761 - val_loss: 0.3858 - val_accuracy: 0.8402\n",
      "Epoch 75/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2963 - accuracy: 0.8767 - val_loss: 0.3906 - val_accuracy: 0.8417\n",
      "Epoch 76/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2964 - accuracy: 0.8750 - val_loss: 0.3937 - val_accuracy: 0.8432\n",
      "Epoch 77/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2964 - accuracy: 0.8778 - val_loss: 0.3847 - val_accuracy: 0.8372\n",
      "Epoch 78/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2953 - accuracy: 0.8772 - val_loss: 0.3990 - val_accuracy: 0.8440\n",
      "Epoch 79/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2950 - accuracy: 0.8744 - val_loss: 0.3875 - val_accuracy: 0.8342\n",
      "Epoch 80/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2954 - accuracy: 0.8778 - val_loss: 0.3878 - val_accuracy: 0.8368\n",
      "Epoch 81/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2944 - accuracy: 0.8746 - val_loss: 0.3903 - val_accuracy: 0.8398\n",
      "Epoch 82/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2942 - accuracy: 0.8770 - val_loss: 0.3980 - val_accuracy: 0.8402\n",
      "Epoch 83/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2941 - accuracy: 0.8755 - val_loss: 0.3906 - val_accuracy: 0.8379\n",
      "Epoch 84/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2939 - accuracy: 0.8767 - val_loss: 0.3980 - val_accuracy: 0.8432\n",
      "Epoch 85/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2921 - accuracy: 0.8783 - val_loss: 0.3999 - val_accuracy: 0.8429\n",
      "Epoch 86/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2931 - accuracy: 0.8750 - val_loss: 0.3925 - val_accuracy: 0.8417\n",
      "Epoch 87/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2933 - accuracy: 0.8772 - val_loss: 0.3967 - val_accuracy: 0.8417\n",
      "Epoch 88/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2930 - accuracy: 0.8774 - val_loss: 0.4024 - val_accuracy: 0.8398\n",
      "Epoch 89/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2920 - accuracy: 0.8780 - val_loss: 0.3975 - val_accuracy: 0.8345\n",
      "Epoch 90/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2916 - accuracy: 0.8767 - val_loss: 0.3927 - val_accuracy: 0.8398\n",
      "Epoch 91/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2918 - accuracy: 0.8774 - val_loss: 0.3952 - val_accuracy: 0.8410\n",
      "Epoch 92/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2906 - accuracy: 0.8791 - val_loss: 0.4050 - val_accuracy: 0.8372\n",
      "Epoch 93/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2907 - accuracy: 0.8778 - val_loss: 0.3970 - val_accuracy: 0.8398\n",
      "Epoch 94/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2918 - accuracy: 0.8755 - val_loss: 0.3968 - val_accuracy: 0.8376\n",
      "Epoch 95/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2913 - accuracy: 0.8787 - val_loss: 0.4045 - val_accuracy: 0.8402\n",
      "Epoch 96/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2901 - accuracy: 0.8750 - val_loss: 0.4016 - val_accuracy: 0.8402\n",
      "Epoch 97/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2907 - accuracy: 0.8809 - val_loss: 0.3991 - val_accuracy: 0.8387\n",
      "Epoch 98/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2915 - accuracy: 0.8796 - val_loss: 0.3990 - val_accuracy: 0.8376\n",
      "Epoch 99/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2905 - accuracy: 0.8783 - val_loss: 0.3997 - val_accuracy: 0.8379\n",
      "Epoch 100/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.2879 - accuracy: 0.8780 - val_loss: 0.4000 - val_accuracy: 0.8334\n",
      "CPU times: total: 18.3 s\n",
      "Wall time: 1min 8s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model_history = classifier.fit(X_train, y_train, validation_split = 0.33, batch_size = 10, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ad0ebafd-f606-433a-a089-760de0210654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 803us/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "438504af-2c36-45e1-ba06-74fc50f3e1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1493,  100],\n",
       "       [ 205,  202]], dtype=int64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dd84ce4b-6ae4-4d14-ac69-ade3b53a2d35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8475"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This time after adding the third layer we can see that train & validation accuracy has increased, but test has decreased\n",
    "score = accuracy_score(y_pred, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e75dfda8-61dc-42cd-878f-0bb7cdff802a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List all data in history\n",
    "print(model_history.history.keys())\n",
    "\n",
    "# Summarize history for accuracy\n",
    "plt.plot(model_history.history['accuracy'])\n",
    "plt.plot(model_history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3fc9c6-669c-41dd-8341-8152b48528f0",
   "metadata": {},
   "source": [
    "# ANN with Third Hidden layer and Dropout Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ce36a8ba-9a4c-45fc-a884-c1c7d36e6ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "10e4bf68-a121-4421-a64c-c27679f89720",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the Input Layer and First Hidden layer\n",
    "classifier.add(Dense(units = 10, kernel_initializer = 'he_normal', activation = 'relu', input_dim = 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "dcf302f3-2aa9-4dc7-9b4d-fad1edfb0537",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Later on we'll find the dropout values by using hyperparameter tuning\n",
    "classifier.add(Dropout(0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2fbd7a3e-db3f-4c05-afcb-17e938fb7f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Second Hidden Layer\n",
    "classifier.add(Dense(units = 20, kernel_initializer= 'he_normal', activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "32bd2e36-5692-4f35-9e63-ab7b4eef6715",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Dropout(0.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "93530442-44ef-46c0-9ac5-a6a62c34c3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Third Hidden layer\n",
    "classifier.add(Dense(units = 15, kernel_initializer = 'he_normal', activation = 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "68a2db3f-20ef-4482-85c0-72202ea8df9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "11099eff-b89f-428c-864c-de98fb968696",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the output layer\n",
    "classifier.add(Dense(units = 1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "052fe7f8-0149-4b75-9510-599c001d93ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_7 (Dense)             (None, 10)                120       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 10)                0         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 20)                220       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 20)                0         \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 15)                315       \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 15)                0         \n",
      "                                                                 \n",
      " dense_10 (Dense)            (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 671\n",
      "Trainable params: 671\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "832e9d41-64a1-4b81-ab6f-ba87ccc904cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the ANN\n",
    "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c69db2f-2852-401c-9fef-b3f848e634c9",
   "metadata": {},
   "source": [
    "### Plotting ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "db282361-c670-4a2d-8eff-9ecacf5848a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the model architecture\n",
    "plot_model(classifier, to_file='../images/3 ANN with Third Hidden Layer and Dropout Layer.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f08d22db-b789-4319-883a-9d62209be779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.6444 - accuracy: 0.7367 - val_loss: 0.5117 - val_accuracy: 0.7842\n",
      "Epoch 2/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.5346 - accuracy: 0.7903 - val_loss: 0.4997 - val_accuracy: 0.7891\n",
      "Epoch 3/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.5016 - accuracy: 0.7990 - val_loss: 0.4875 - val_accuracy: 0.7861\n",
      "Epoch 4/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4832 - accuracy: 0.8024 - val_loss: 0.4779 - val_accuracy: 0.7864\n",
      "Epoch 5/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4740 - accuracy: 0.8065 - val_loss: 0.4668 - val_accuracy: 0.7883\n",
      "Epoch 6/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4614 - accuracy: 0.8106 - val_loss: 0.4572 - val_accuracy: 0.7914\n",
      "Epoch 7/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4457 - accuracy: 0.8091 - val_loss: 0.4350 - val_accuracy: 0.8050\n",
      "Epoch 8/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4344 - accuracy: 0.8151 - val_loss: 0.4267 - val_accuracy: 0.8061\n",
      "Epoch 9/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4299 - accuracy: 0.8164 - val_loss: 0.4196 - val_accuracy: 0.8099\n",
      "Epoch 10/100\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 0.4242 - accuracy: 0.8171 - val_loss: 0.4129 - val_accuracy: 0.8111\n",
      "Epoch 11/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4196 - accuracy: 0.8244 - val_loss: 0.4062 - val_accuracy: 0.8111\n",
      "Epoch 12/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4021 - accuracy: 0.8309 - val_loss: 0.4010 - val_accuracy: 0.8179\n",
      "Epoch 13/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4072 - accuracy: 0.8263 - val_loss: 0.4040 - val_accuracy: 0.8084\n",
      "Epoch 14/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3994 - accuracy: 0.8332 - val_loss: 0.4018 - val_accuracy: 0.8171\n",
      "Epoch 15/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3992 - accuracy: 0.8317 - val_loss: 0.3965 - val_accuracy: 0.8141\n",
      "Epoch 16/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3936 - accuracy: 0.8330 - val_loss: 0.3921 - val_accuracy: 0.8239\n",
      "Epoch 17/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3863 - accuracy: 0.8345 - val_loss: 0.3960 - val_accuracy: 0.8164\n",
      "Epoch 18/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3987 - accuracy: 0.8336 - val_loss: 0.3936 - val_accuracy: 0.8186\n",
      "Epoch 19/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3908 - accuracy: 0.8352 - val_loss: 0.3924 - val_accuracy: 0.8213\n",
      "Epoch 20/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3857 - accuracy: 0.8337 - val_loss: 0.3944 - val_accuracy: 0.8198\n",
      "Epoch 21/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3845 - accuracy: 0.8380 - val_loss: 0.3910 - val_accuracy: 0.8247\n",
      "Epoch 22/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3881 - accuracy: 0.8343 - val_loss: 0.3898 - val_accuracy: 0.8247\n",
      "Epoch 23/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3892 - accuracy: 0.8406 - val_loss: 0.3908 - val_accuracy: 0.8213\n",
      "Epoch 24/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3811 - accuracy: 0.8449 - val_loss: 0.3922 - val_accuracy: 0.8228\n",
      "Epoch 25/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3773 - accuracy: 0.8416 - val_loss: 0.3856 - val_accuracy: 0.8285\n",
      "Epoch 26/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3822 - accuracy: 0.8388 - val_loss: 0.3823 - val_accuracy: 0.8323\n",
      "Epoch 27/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3814 - accuracy: 0.8421 - val_loss: 0.3832 - val_accuracy: 0.8296\n",
      "Epoch 28/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3828 - accuracy: 0.8403 - val_loss: 0.3846 - val_accuracy: 0.8254\n",
      "Epoch 29/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3776 - accuracy: 0.8405 - val_loss: 0.3828 - val_accuracy: 0.8315\n",
      "Epoch 30/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3758 - accuracy: 0.8427 - val_loss: 0.3860 - val_accuracy: 0.8270\n",
      "Epoch 31/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3797 - accuracy: 0.8433 - val_loss: 0.3776 - val_accuracy: 0.8372\n",
      "Epoch 32/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3781 - accuracy: 0.8464 - val_loss: 0.3753 - val_accuracy: 0.8383\n",
      "Epoch 33/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3817 - accuracy: 0.8446 - val_loss: 0.3780 - val_accuracy: 0.8349\n",
      "Epoch 34/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3761 - accuracy: 0.8408 - val_loss: 0.3819 - val_accuracy: 0.8304\n",
      "Epoch 35/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3747 - accuracy: 0.8416 - val_loss: 0.3798 - val_accuracy: 0.8319\n",
      "Epoch 36/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3761 - accuracy: 0.8444 - val_loss: 0.3719 - val_accuracy: 0.8395\n",
      "Epoch 37/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3709 - accuracy: 0.8449 - val_loss: 0.3704 - val_accuracy: 0.8398\n",
      "Epoch 38/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3755 - accuracy: 0.8436 - val_loss: 0.3799 - val_accuracy: 0.8349\n",
      "Epoch 39/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3707 - accuracy: 0.8470 - val_loss: 0.3720 - val_accuracy: 0.8383\n",
      "Epoch 40/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3761 - accuracy: 0.8408 - val_loss: 0.3742 - val_accuracy: 0.8376\n",
      "Epoch 41/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3649 - accuracy: 0.8479 - val_loss: 0.3740 - val_accuracy: 0.8345\n",
      "Epoch 42/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3717 - accuracy: 0.8431 - val_loss: 0.3756 - val_accuracy: 0.8372\n",
      "Epoch 43/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3736 - accuracy: 0.8453 - val_loss: 0.3748 - val_accuracy: 0.8410\n",
      "Epoch 44/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3646 - accuracy: 0.8481 - val_loss: 0.3717 - val_accuracy: 0.8402\n",
      "Epoch 45/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3707 - accuracy: 0.8475 - val_loss: 0.3721 - val_accuracy: 0.8383\n",
      "Epoch 46/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3682 - accuracy: 0.8485 - val_loss: 0.3746 - val_accuracy: 0.8387\n",
      "Epoch 47/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3702 - accuracy: 0.8475 - val_loss: 0.3761 - val_accuracy: 0.8368\n",
      "Epoch 48/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3693 - accuracy: 0.8494 - val_loss: 0.3733 - val_accuracy: 0.8376\n",
      "Epoch 49/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3684 - accuracy: 0.8446 - val_loss: 0.3686 - val_accuracy: 0.8417\n",
      "Epoch 50/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3652 - accuracy: 0.8466 - val_loss: 0.3701 - val_accuracy: 0.8421\n",
      "Epoch 51/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3702 - accuracy: 0.8475 - val_loss: 0.3724 - val_accuracy: 0.8432\n",
      "Epoch 52/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3680 - accuracy: 0.8462 - val_loss: 0.3680 - val_accuracy: 0.8463\n",
      "Epoch 53/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3732 - accuracy: 0.8410 - val_loss: 0.3728 - val_accuracy: 0.8342\n",
      "Epoch 54/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3716 - accuracy: 0.8459 - val_loss: 0.3719 - val_accuracy: 0.8391\n",
      "Epoch 55/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3683 - accuracy: 0.8479 - val_loss: 0.3710 - val_accuracy: 0.8425\n",
      "Epoch 56/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3708 - accuracy: 0.8444 - val_loss: 0.3707 - val_accuracy: 0.8402\n",
      "Epoch 57/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3691 - accuracy: 0.8440 - val_loss: 0.3718 - val_accuracy: 0.8357\n",
      "Epoch 58/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3655 - accuracy: 0.8464 - val_loss: 0.3685 - val_accuracy: 0.8440\n",
      "Epoch 59/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3713 - accuracy: 0.8442 - val_loss: 0.3700 - val_accuracy: 0.8387\n",
      "Epoch 60/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3685 - accuracy: 0.8492 - val_loss: 0.3700 - val_accuracy: 0.8444\n",
      "Epoch 61/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3689 - accuracy: 0.8502 - val_loss: 0.3729 - val_accuracy: 0.8413\n",
      "Epoch 62/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3663 - accuracy: 0.8483 - val_loss: 0.3699 - val_accuracy: 0.8425\n",
      "Epoch 63/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3665 - accuracy: 0.8487 - val_loss: 0.3720 - val_accuracy: 0.8436\n",
      "Epoch 64/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3678 - accuracy: 0.8475 - val_loss: 0.3738 - val_accuracy: 0.8376\n",
      "Epoch 65/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3680 - accuracy: 0.8466 - val_loss: 0.3713 - val_accuracy: 0.8406\n",
      "Epoch 66/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3640 - accuracy: 0.8459 - val_loss: 0.3667 - val_accuracy: 0.8413\n",
      "Epoch 67/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3729 - accuracy: 0.8477 - val_loss: 0.3738 - val_accuracy: 0.8395\n",
      "Epoch 68/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3660 - accuracy: 0.8481 - val_loss: 0.3732 - val_accuracy: 0.8372\n",
      "Epoch 69/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3666 - accuracy: 0.8479 - val_loss: 0.3695 - val_accuracy: 0.8402\n",
      "Epoch 70/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3649 - accuracy: 0.8455 - val_loss: 0.3708 - val_accuracy: 0.8395\n",
      "Epoch 71/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3697 - accuracy: 0.8446 - val_loss: 0.3723 - val_accuracy: 0.8395\n",
      "Epoch 72/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3673 - accuracy: 0.8442 - val_loss: 0.3695 - val_accuracy: 0.8429\n",
      "Epoch 73/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3644 - accuracy: 0.8511 - val_loss: 0.3718 - val_accuracy: 0.8406\n",
      "Epoch 74/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3627 - accuracy: 0.8500 - val_loss: 0.3686 - val_accuracy: 0.8448\n",
      "Epoch 75/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3678 - accuracy: 0.8522 - val_loss: 0.3751 - val_accuracy: 0.8379\n",
      "Epoch 76/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3670 - accuracy: 0.8492 - val_loss: 0.3732 - val_accuracy: 0.8402\n",
      "Epoch 77/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3699 - accuracy: 0.8423 - val_loss: 0.3756 - val_accuracy: 0.8357\n",
      "Epoch 78/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3643 - accuracy: 0.8440 - val_loss: 0.3767 - val_accuracy: 0.8376\n",
      "Epoch 79/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3691 - accuracy: 0.8485 - val_loss: 0.3736 - val_accuracy: 0.8432\n",
      "Epoch 80/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3698 - accuracy: 0.8459 - val_loss: 0.3684 - val_accuracy: 0.8455\n",
      "Epoch 81/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3702 - accuracy: 0.8447 - val_loss: 0.3685 - val_accuracy: 0.8448\n",
      "Epoch 82/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3616 - accuracy: 0.8513 - val_loss: 0.3684 - val_accuracy: 0.8455\n",
      "Epoch 83/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3656 - accuracy: 0.8419 - val_loss: 0.3707 - val_accuracy: 0.8425\n",
      "Epoch 84/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3645 - accuracy: 0.8500 - val_loss: 0.3683 - val_accuracy: 0.8463\n",
      "Epoch 85/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3653 - accuracy: 0.8479 - val_loss: 0.3712 - val_accuracy: 0.8417\n",
      "Epoch 86/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3635 - accuracy: 0.8475 - val_loss: 0.3669 - val_accuracy: 0.8466\n",
      "Epoch 87/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3628 - accuracy: 0.8507 - val_loss: 0.3684 - val_accuracy: 0.8436\n",
      "Epoch 88/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3678 - accuracy: 0.8494 - val_loss: 0.3701 - val_accuracy: 0.8444\n",
      "Epoch 89/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3618 - accuracy: 0.8472 - val_loss: 0.3720 - val_accuracy: 0.8406\n",
      "Epoch 90/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3612 - accuracy: 0.8515 - val_loss: 0.3704 - val_accuracy: 0.8448\n",
      "Epoch 91/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3579 - accuracy: 0.8502 - val_loss: 0.3629 - val_accuracy: 0.8504\n",
      "Epoch 92/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3596 - accuracy: 0.8462 - val_loss: 0.3664 - val_accuracy: 0.8478\n",
      "Epoch 93/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3672 - accuracy: 0.8490 - val_loss: 0.3692 - val_accuracy: 0.8444\n",
      "Epoch 94/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3650 - accuracy: 0.8475 - val_loss: 0.3718 - val_accuracy: 0.8436\n",
      "Epoch 95/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3609 - accuracy: 0.8500 - val_loss: 0.3698 - val_accuracy: 0.8451\n",
      "Epoch 96/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3601 - accuracy: 0.8446 - val_loss: 0.3682 - val_accuracy: 0.8429\n",
      "Epoch 97/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3587 - accuracy: 0.8498 - val_loss: 0.3661 - val_accuracy: 0.8478\n",
      "Epoch 98/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3594 - accuracy: 0.8457 - val_loss: 0.3687 - val_accuracy: 0.8413\n",
      "Epoch 99/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3635 - accuracy: 0.8485 - val_loss: 0.3732 - val_accuracy: 0.8349\n",
      "Epoch 100/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3613 - accuracy: 0.8518 - val_loss: 0.3679 - val_accuracy: 0.8463\n",
      "CPU times: total: 18.5 s\n",
      "Wall time: 1min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model_history = classifier.fit(X_train, y_train, validation_split= 0.33, batch_size = 10, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "6667067d-0bd3-4f40-8cd5-3772af04df16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 749us/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = classifier.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "13217246-ff9d-4433-822f-277c571f3832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1569,   24],\n",
       "       [ 262,  145]], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0ea0b3a7-1ec6-46ca-a139-b04ad575eb41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.857"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(y_pred, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "411d70e5-8089-46ef-8183-ceca93d94cc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List all data in history\n",
    "print(model_history.history.keys())\n",
    "\n",
    "# Summarize history for accuracy\n",
    "plt.plot(model_history.history['accuracy'])\n",
    "plt.plot(model_history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c93068c-cfd9-4459-845e-16e3d81c0021",
   "metadata": {},
   "source": [
    "# Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fb51d683-f590-4e40-bdf0-77a2f351f24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Sanket\\AppData\\Local\\Temp\\ipykernel_14640\\2762355705.py:26: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
      "  model = KerasClassifier(build_fn = create_model, verbose = 0)\n"
     ]
    }
   ],
   "source": [
    "def create_model(layers, activation):\n",
    "    # Initialising the model\n",
    "    model = Sequential()\n",
    "    for i, nodes in enumerate(layers):\n",
    "        # For first layer we have to provide input dimensions\n",
    "        if i == 0:\n",
    "            model.add(Dense(nodes, input_dim = X_train.shape[1]))\n",
    "            model.add(Activation(activation))\n",
    "            model.add(Dropout(0.3))\n",
    "        \n",
    "        else:\n",
    "            model.add(Dense(nodes))\n",
    "            model.add(Activation(activation))\n",
    "            model.add(Dropout(0.3))\n",
    "\n",
    "    # Last layer will have only 1 output\n",
    "    model.add(Dense(units = 1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))\n",
    "    \n",
    "    model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
    "    # Plot the model architecture vertically\n",
    "    plot_model(model, to_file='../images/4 ANN Hyperparameter Tuned.png', show_shapes=True, show_layer_names=True)\n",
    "    # Plot the model architecture horizontally\n",
    "    plot_model(model, to_file='../images/5 ANN Hyperparameter Tuned Horizontal.png', show_shapes=True, show_layer_names=True, rankdir='LR')\n",
    "    return model\n",
    "\n",
    "model = KerasClassifier(build_fn = create_model, verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "97286c6a-03e1-4189-9090-2b9b11829c13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 37.8 s\n",
      "Wall time: 1min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Iterations where 1combination is 1 neural network and the number is the number of neurons\n",
    "layers = [[20], [20,40], [45,30,15]]\n",
    "\n",
    "# There is a way for taking this layers as well will discuss later\n",
    "activations = ['sigmoid', 'relu']\n",
    "param_grid = dict(layers = layers, activation = activations, batch_size = [128, 256], epochs = [30])\n",
    "\n",
    "# Creating dictionary of parameters\n",
    "grid = GridSearchCV(estimator = model, param_grid = param_grid, cv = 5) #RandomizedSearchCV can also be used\n",
    "\n",
    "grid_result = grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "88e8ecf3-f664-41ac-97f2-35dc37fee3fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8556249976158142 {'activation': 'relu', 'batch_size': 256, 'epochs': 30, 'layers': [45, 30, 15]}\n"
     ]
    }
   ],
   "source": [
    "print(grid_result.best_score_, grid_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "61657379-655a-4796-974b-4f0bb121f79e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 790us/step\n"
     ]
    }
   ],
   "source": [
    "pred_y = grid.predict(X_test)\n",
    "y_pred = (pred_y > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a7b090e9-fde9-43d7-bf73-2fc6d5e32cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1533,   60],\n",
       "       [ 247,  160]], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a22e2563-c198-4ec0-ad6f-c451eb8b43cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8465"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = accuracy_score(y_pred, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ba0446ef-5f78-46ee-81a3-96e80fcfe2d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List all data in history\n",
    "print(model_history.history.keys())\n",
    "\n",
    "# Summarize history for accuracy\n",
    "plt.plot(model_history.history['accuracy'])\n",
    "plt.plot(model_history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd191b14-ae06-4a05-b235-0a3168b4c519",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}