Churn_Modelling_ANN_model.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "fc92e914",
   "metadata": {},
   "source": [
    "# ANN\n",
    "\n",
    "Artificial Neural Networks (ANN) are multi-layer fully-connected neural nets that look like the figure below. They consist of an input layer, multiple hidden layers, and an output layer. Every node in one layer is connected to every other node in the next layer. We make the network deeper by increasing the number of hidden layers.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "763f26e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fae7f4c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\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",
       "   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",
       "   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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data set\n",
    "dataset= pd.read_csv('Churn_Modelling.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f22f412",
   "metadata": {},
   "source": [
    "### Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f417fddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  \n",
       "0          1               1        101348.88  \n",
       "1          0               1        112542.58  \n",
       "2          1               0        113931.57  \n",
       "3          0               0         93826.63  \n",
       "4          1               1         79084.10  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= dataset.iloc[:,3:13]\n",
    "y=dataset.iloc[:,13]\n",
    "X.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02b9af70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    0\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3eca4a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.7963\n",
       "1    0.2037\n",
       "Name: Exited, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Exited.value_counts()/dataset.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "39fb4da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "geography=pd.get_dummies(X[\"Geography\"],drop_first=True)\n",
    "gender= pd.get_dummies(X['Gender'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f955c8f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Germany</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>41</td>\n",
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       "      <td>83807.86</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n",
       "0          619    France  Female   42       2       0.00              1   \n",
       "1          608     Spain  Female   41       1   83807.86              1   \n",
       "2          502    France  Female   42       8  159660.80              3   \n",
       "3          699    France  Female   39       1       0.00              2   \n",
       "4          850     Spain  Female   43       2  125510.82              1   \n",
       "\n",
       "   HasCrCard  IsActiveMember  EstimatedSalary  Germany  Spain  Male  \n",
       "0          1               1        101348.88        0      0     0  \n",
       "1          0               1        112542.58        0      1     0  \n",
       "2          1               0        113931.57        0      0     0  \n",
       "3          0               0         93826.63        0      0     0  \n",
       "4          1               1         79084.10        0      1     0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=pd.concat([X,geography,gender],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64937aa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(['Geography','Gender'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cefb2ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
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       "      <th>Male</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619   42       2       0.00              1          1   \n",
       "1          608   41       1   83807.86              1          0   \n",
       "2          502   42       8  159660.80              3          1   \n",
       "3          699   39       1       0.00              2          0   \n",
       "4          850   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Germany  Spain  Male  \n",
       "0               1        101348.88        0      0     0  \n",
       "1               1        112542.58        0      1     0  \n",
       "2               0        113931.57        0      0     0  \n",
       "3               0         93826.63        0      0     0  \n",
       "4               1         79084.10        0      1     0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9dc63a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting into training and test \n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd0c80a",
   "metadata": {},
   "source": [
    "### Features Scalling\n",
    "\n",
    "Because in neural network features will multiply by weight +b , so make it so this important how much time is taking, suppose \n",
    "The magnitude of features are very high number, so weight* features multiplication will takes more time \n",
    "So we try to scaled down all the features in same scale.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8a0d2c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature scalling is must to converage will happen quickly\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.transform(X_test)\n",
    "\n",
    "# Because in neural network features will multiply by weight +b , so make it so this important how much time is taking, suppose \n",
    "# The magnitude of features are very high number, so weight* features multiplication will takes more time \n",
    "# So we try to scaled down all the features in same scale.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02b52a4b",
   "metadata": {},
   "source": [
    "### Now we will build the ANN Model !\n",
    "1. Define your model: create a **Sequential** model and **add layers**.\n",
    "2. Compile your model: specify loss function and optimizers and call the **.compile()** function.\n",
    "3. Fit your model: train the model on data by calling the **.fit()** function.\n",
    "4. Make predictions: use the model to generate predictions on new data by calling functions such as .evaluate() or **.predict()**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c6690ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential  # for all deep learning neural network\n",
    "from keras.layers import Dense #for hidden layer \n",
    "from keras.layers import LeakyReLU,PReLU,ELU # for Activation function \n",
    "from keras. layers import Dropout  # to avoid overfittin---> if neural network is too deep we usually used this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4d2c00e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the ANN\n",
    "classifier= Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b267acbb",
   "metadata": {},
   "source": [
    "### Creating input layer/Hidden layer/Output layer\n",
    "- The Dense function in Keras constructs a fully connected neural network layer, automatically initializing the weights as biases. \n",
    "- units: The first argument, representing number of nodes in this layer. Since we’re constructing the output layer, and we said it has only one node, this value is 1\n",
    "- activation: The activation function of a logistic regression model is the logistic function, or alternatively called the sigmoid\n",
    "  - input_dim= no fo features in training\n",
    "  - We know in **hidden layer** ---------> generally used **Relu** activation function to avoid vanishing gradient problem\n",
    "  - With **Relu** Activation function----> generally **he_unifrom and he_normal** work very well\n",
    "  - In final layer/ **output layer**-----> we generally used **sigmoid** Activation fucntion, and weight initilization **glorot_uniform** works pretty well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a01adff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the input layer and the first hidden layer\n",
    "classifier.add(Dense(units=6,kernel_initializer='he_uniform',activation='relu',input_dim=11))\n",
    "# input_dim= no fo features in training\n",
    "# We know in hidden layer we generally used Relu activation function to avoid vanishing gradient problem\n",
    "# With Relu Activation function----> generally he_unifrom and he_normal work very well\n",
    "# In final layer/ output layer----> we generally used sigmoid Activation fucntion, and weight initilization glorot_uniform works\n",
    "# pretty well.\n",
    "\n",
    "#classifier.add(dropout(0.3))\n",
    "\n",
    "# Adding the second hidden layer\n",
    "classifier.add(Dense(units=6,kernel_initializer='he_uniform',activation='relu'))\n",
    "\n",
    "# Adding the output layer\n",
    "classifier.add(Dense(units=1,kernel_initializer='glorot_uniform',activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "311475a0",
   "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",
      "Total params: 121\n",
      "Trainable params: 121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a227a5e6",
   "metadata": {},
   "source": [
    "### Complining the ANN\n",
    "- optimizer: Which optimizer to use in order to minimize the loss function.\n",
    "- loss: The loss function to minimize. Since we’re building a binary 0/1 classifier, the loss function to minimize is binary_crossentropy\n",
    "- metrics: Which metric to report statistics on, for classification problems we set this as accuracy.\n",
    "  - We mostly prefer optimizer----> Adam\n",
    "  - Loss function for Binary----> binary_crossentropy\n",
    "  - loss function for multiple class -----> categorical_crossentropy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e27c6e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compiling ANN\n",
    "classifier.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5cd6bd4",
   "metadata": {},
   "source": [
    "### Fitting the ANN to the Training set\n",
    "- epochs: Number of times to go over the entire training data. When training models we pass through the training data not just once but multiple times.\n",
    " - We will prefer to give data in **batch size** in training, so it will run \n",
    " - We will also have to mention How many epochs we will set, since in this dataset there are 10,000 records, so no of epoch * batch size----> batch size =10, epochs=100 \n",
    " - validation_split-----> to test the model seperatly for the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20e3c847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "536/536 [==============================] - 2s 2ms/step - loss: 0.6543 - accuracy: 0.6776 - val_loss: 0.5142 - val_accuracy: 0.7838\n",
      "Epoch 2/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4866 - accuracy: 0.7932 - val_loss: 0.4789 - val_accuracy: 0.7952\n",
      "Epoch 3/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4637 - accuracy: 0.7960 - val_loss: 0.4630 - val_accuracy: 0.7955\n",
      "Epoch 4/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4503 - accuracy: 0.7962 - val_loss: 0.4518 - val_accuracy: 0.7955\n",
      "Epoch 5/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4408 - accuracy: 0.7962 - val_loss: 0.4436 - val_accuracy: 0.7952\n",
      "Epoch 6/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4331 - accuracy: 0.7959 - val_loss: 0.4372 - val_accuracy: 0.7959\n",
      "Epoch 7/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4263 - accuracy: 0.7960 - val_loss: 0.4287 - val_accuracy: 0.7993\n",
      "Epoch 8/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4181 - accuracy: 0.8052 - val_loss: 0.4196 - val_accuracy: 0.8122\n",
      "Epoch 9/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4094 - accuracy: 0.8149 - val_loss: 0.4126 - val_accuracy: 0.8194\n",
      "Epoch 10/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.4024 - accuracy: 0.8186 - val_loss: 0.4068 - val_accuracy: 0.8179\n",
      "Epoch 11/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3966 - accuracy: 0.8199 - val_loss: 0.4022 - val_accuracy: 0.8194\n",
      "Epoch 12/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3915 - accuracy: 0.8218 - val_loss: 0.3988 - val_accuracy: 0.8217\n",
      "Epoch 13/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3868 - accuracy: 0.8238 - val_loss: 0.3945 - val_accuracy: 0.8254\n",
      "Epoch 14/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3832 - accuracy: 0.8268 - val_loss: 0.3917 - val_accuracy: 0.8258\n",
      "Epoch 15/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3795 - accuracy: 0.8294 - val_loss: 0.3899 - val_accuracy: 0.8270\n",
      "Epoch 16/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3776 - accuracy: 0.8332 - val_loss: 0.3876 - val_accuracy: 0.8326\n",
      "Epoch 17/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3755 - accuracy: 0.8393 - val_loss: 0.3855 - val_accuracy: 0.8360\n",
      "Epoch 18/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3734 - accuracy: 0.8416 - val_loss: 0.3857 - val_accuracy: 0.8360\n",
      "Epoch 19/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3714 - accuracy: 0.8391 - val_loss: 0.3836 - val_accuracy: 0.8379\n",
      "Epoch 20/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3698 - accuracy: 0.8414 - val_loss: 0.3828 - val_accuracy: 0.8364\n",
      "Epoch 21/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3682 - accuracy: 0.8427 - val_loss: 0.3808 - val_accuracy: 0.8387\n",
      "Epoch 22/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3665 - accuracy: 0.8438 - val_loss: 0.3797 - val_accuracy: 0.8391\n",
      "Epoch 23/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3647 - accuracy: 0.8444 - val_loss: 0.3798 - val_accuracy: 0.8379\n",
      "Epoch 24/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3628 - accuracy: 0.8447 - val_loss: 0.3796 - val_accuracy: 0.8444\n",
      "Epoch 25/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3609 - accuracy: 0.8483 - val_loss: 0.3783 - val_accuracy: 0.8478\n",
      "Epoch 26/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3588 - accuracy: 0.8494 - val_loss: 0.3784 - val_accuracy: 0.8448\n",
      "Epoch 27/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3568 - accuracy: 0.8490 - val_loss: 0.3755 - val_accuracy: 0.8463\n",
      "Epoch 28/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3549 - accuracy: 0.8507 - val_loss: 0.3750 - val_accuracy: 0.8470\n",
      "Epoch 29/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3533 - accuracy: 0.8528 - val_loss: 0.3731 - val_accuracy: 0.8485\n",
      "Epoch 30/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3524 - accuracy: 0.8541 - val_loss: 0.3729 - val_accuracy: 0.8497\n",
      "Epoch 31/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3508 - accuracy: 0.8535 - val_loss: 0.3733 - val_accuracy: 0.8466\n",
      "Epoch 32/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3496 - accuracy: 0.8554 - val_loss: 0.3718 - val_accuracy: 0.8493\n",
      "Epoch 33/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3485 - accuracy: 0.8554 - val_loss: 0.3736 - val_accuracy: 0.8482\n",
      "Epoch 34/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3473 - accuracy: 0.8569 - val_loss: 0.3725 - val_accuracy: 0.8508\n",
      "Epoch 35/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3467 - accuracy: 0.8554 - val_loss: 0.3710 - val_accuracy: 0.8478\n",
      "Epoch 36/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3456 - accuracy: 0.8565 - val_loss: 0.3717 - val_accuracy: 0.8455\n",
      "Epoch 37/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3452 - accuracy: 0.8572 - val_loss: 0.3707 - val_accuracy: 0.8459\n",
      "Epoch 38/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3446 - accuracy: 0.8569 - val_loss: 0.3701 - val_accuracy: 0.8478\n",
      "Epoch 39/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3433 - accuracy: 0.8578 - val_loss: 0.3733 - val_accuracy: 0.8466\n",
      "Epoch 40/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3430 - accuracy: 0.8586 - val_loss: 0.3689 - val_accuracy: 0.8482\n",
      "Epoch 41/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3412 - accuracy: 0.8571 - val_loss: 0.3714 - val_accuracy: 0.8535\n",
      "Epoch 42/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3409 - accuracy: 0.8589 - val_loss: 0.3742 - val_accuracy: 0.8459\n",
      "Epoch 43/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3407 - accuracy: 0.8593 - val_loss: 0.3687 - val_accuracy: 0.8459\n",
      "Epoch 44/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3394 - accuracy: 0.8576 - val_loss: 0.3688 - val_accuracy: 0.8436\n",
      "Epoch 45/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3391 - accuracy: 0.8606 - val_loss: 0.3659 - val_accuracy: 0.8512\n",
      "Epoch 46/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3382 - accuracy: 0.8602 - val_loss: 0.3664 - val_accuracy: 0.8501\n",
      "Epoch 47/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3375 - accuracy: 0.8619 - val_loss: 0.3651 - val_accuracy: 0.8523\n",
      "Epoch 48/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3376 - accuracy: 0.8615 - val_loss: 0.3647 - val_accuracy: 0.8523\n",
      "Epoch 49/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3368 - accuracy: 0.8614 - val_loss: 0.3633 - val_accuracy: 0.8542\n",
      "Epoch 50/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3361 - accuracy: 0.8612 - val_loss: 0.3662 - val_accuracy: 0.8519\n",
      "Epoch 51/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3359 - accuracy: 0.8619 - val_loss: 0.3628 - val_accuracy: 0.8573\n",
      "Epoch 52/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3347 - accuracy: 0.8642 - val_loss: 0.3648 - val_accuracy: 0.8512\n",
      "Epoch 53/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3352 - accuracy: 0.8628 - val_loss: 0.3623 - val_accuracy: 0.8550\n",
      "Epoch 54/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3338 - accuracy: 0.8602 - val_loss: 0.3648 - val_accuracy: 0.8485\n",
      "Epoch 55/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3337 - accuracy: 0.8642 - val_loss: 0.3658 - val_accuracy: 0.8546\n",
      "Epoch 56/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3337 - accuracy: 0.8627 - val_loss: 0.3624 - val_accuracy: 0.8561\n",
      "Epoch 57/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3320 - accuracy: 0.8627 - val_loss: 0.3626 - val_accuracy: 0.8519\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3326 - accuracy: 0.8627 - val_loss: 0.3635 - val_accuracy: 0.8527\n",
      "Epoch 59/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3321 - accuracy: 0.8628 - val_loss: 0.3605 - val_accuracy: 0.8546\n",
      "Epoch 60/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3327 - accuracy: 0.8630 - val_loss: 0.3654 - val_accuracy: 0.8554\n",
      "Epoch 61/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3313 - accuracy: 0.8615 - val_loss: 0.3639 - val_accuracy: 0.8482\n",
      "Epoch 62/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3318 - accuracy: 0.8630 - val_loss: 0.3612 - val_accuracy: 0.8565\n",
      "Epoch 63/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3311 - accuracy: 0.8621 - val_loss: 0.3629 - val_accuracy: 0.8542\n",
      "Epoch 64/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3305 - accuracy: 0.8649 - val_loss: 0.3626 - val_accuracy: 0.8527\n",
      "Epoch 65/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3303 - accuracy: 0.8612 - val_loss: 0.3630 - val_accuracy: 0.8519\n",
      "Epoch 66/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3307 - accuracy: 0.8610 - val_loss: 0.3612 - val_accuracy: 0.8603\n",
      "Epoch 67/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3299 - accuracy: 0.8660 - val_loss: 0.3609 - val_accuracy: 0.8573\n",
      "Epoch 68/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3298 - accuracy: 0.8658 - val_loss: 0.3591 - val_accuracy: 0.8584\n",
      "Epoch 69/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3297 - accuracy: 0.8634 - val_loss: 0.3628 - val_accuracy: 0.8470\n",
      "Epoch 70/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3290 - accuracy: 0.8655 - val_loss: 0.3622 - val_accuracy: 0.8519\n",
      "Epoch 71/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3291 - accuracy: 0.8642 - val_loss: 0.3625 - val_accuracy: 0.8565\n",
      "Epoch 72/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3283 - accuracy: 0.8645 - val_loss: 0.3614 - val_accuracy: 0.8508\n",
      "Epoch 73/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3274 - accuracy: 0.8647 - val_loss: 0.3610 - val_accuracy: 0.8557\n",
      "Epoch 74/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3283 - accuracy: 0.8636 - val_loss: 0.3614 - val_accuracy: 0.8535\n",
      "Epoch 75/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3284 - accuracy: 0.8636 - val_loss: 0.3625 - val_accuracy: 0.8470\n",
      "Epoch 76/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3271 - accuracy: 0.8668 - val_loss: 0.3608 - val_accuracy: 0.8573\n",
      "Epoch 77/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3277 - accuracy: 0.8643 - val_loss: 0.3621 - val_accuracy: 0.8546\n",
      "Epoch 78/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3275 - accuracy: 0.8627 - val_loss: 0.3614 - val_accuracy: 0.8523\n",
      "Epoch 79/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3261 - accuracy: 0.8645 - val_loss: 0.3614 - val_accuracy: 0.8557\n",
      "Epoch 80/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3266 - accuracy: 0.8683 - val_loss: 0.3672 - val_accuracy: 0.8527\n",
      "Epoch 81/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3262 - accuracy: 0.8642 - val_loss: 0.3623 - val_accuracy: 0.8504\n",
      "Epoch 82/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3264 - accuracy: 0.8651 - val_loss: 0.3613 - val_accuracy: 0.8580\n",
      "Epoch 83/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3268 - accuracy: 0.8645 - val_loss: 0.3592 - val_accuracy: 0.8584\n",
      "Epoch 84/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3259 - accuracy: 0.8664 - val_loss: 0.3621 - val_accuracy: 0.8504\n",
      "Epoch 85/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3262 - accuracy: 0.8636 - val_loss: 0.3602 - val_accuracy: 0.8542\n",
      "Epoch 86/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3261 - accuracy: 0.8662 - val_loss: 0.3604 - val_accuracy: 0.8576\n",
      "Epoch 87/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3258 - accuracy: 0.8642 - val_loss: 0.3591 - val_accuracy: 0.8554\n",
      "Epoch 88/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3260 - accuracy: 0.8642 - val_loss: 0.3602 - val_accuracy: 0.8588\n",
      "Epoch 89/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3256 - accuracy: 0.8658 - val_loss: 0.3636 - val_accuracy: 0.8535\n",
      "Epoch 90/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3264 - accuracy: 0.8649 - val_loss: 0.3608 - val_accuracy: 0.8576\n",
      "Epoch 91/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3261 - accuracy: 0.8642 - val_loss: 0.3636 - val_accuracy: 0.8576\n",
      "Epoch 92/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8645 - val_loss: 0.3625 - val_accuracy: 0.8523\n",
      "Epoch 93/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3248 - accuracy: 0.8610 - val_loss: 0.3611 - val_accuracy: 0.8580\n",
      "Epoch 94/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3255 - accuracy: 0.8628 - val_loss: 0.3634 - val_accuracy: 0.8523\n",
      "Epoch 95/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3248 - accuracy: 0.8651 - val_loss: 0.3622 - val_accuracy: 0.8565\n",
      "Epoch 96/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3249 - accuracy: 0.8627 - val_loss: 0.3630 - val_accuracy: 0.8504\n",
      "Epoch 97/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8647 - val_loss: 0.3611 - val_accuracy: 0.8576\n",
      "Epoch 98/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3247 - accuracy: 0.8664 - val_loss: 0.3621 - val_accuracy: 0.8527\n",
      "Epoch 99/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3246 - accuracy: 0.8670 - val_loss: 0.3609 - val_accuracy: 0.8531\n",
      "Epoch 100/100\n",
      "536/536 [==============================] - 1s 1ms/step - loss: 0.3247 - accuracy: 0.8634 - val_loss: 0.3622 - val_accuracy: 0.8542\n"
     ]
    }
   ],
   "source": [
    "#fitting the ANN to the training set\n",
    "model_history=classifier.fit(X_train,y_train,validation_split=0.33,batch_size=10,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "adba9c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [False],\n",
       "       [False],\n",
       "       [False]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting the model\n",
    "y_pred= classifier.predict(X_test)\n",
    "y_pred= (y_pred>0.5)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c57f5c34",
   "metadata": {},
   "source": [
    "#### Printing the confusion matrix, accuracy, and classification report\n",
    "The confusion matrix shows us how many classes were correctly classified vs misclassified. The numbers on the diagonal axis represent the number of correctly classified points, the rest are the misclassified ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ef2bbe4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1500   95]\n",
      " [ 182  223]]\n",
      "The accuracy of the model is 0.8615\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.94      0.92      1595\n",
      "           1       0.70      0.55      0.62       405\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.80      0.75      0.77      2000\n",
      "weighted avg       0.85      0.86      0.86      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "\n",
    "cm= confusion_matrix(y_test,y_pred)\n",
    "print(cm)\n",
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "print('The accuracy of the model is',accuracy)\n",
    "cl_report = classification_report(y_test,y_pred)\n",
    "print(cl_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b18eafb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(model_history.history.keys())\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": 32,
   "id": "c7ea70a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b09cef8be0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model_history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e917c881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2b09cf2fc10>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7eyRYf8GhdwSnmAksdPd7zGxmsHxnczskrWtnWRUzn1nG62tK+cyYftz75dNqTduYGUN6dWFIM25bF5HjI50RwGSg0N03AJjZU8A0IDUBONDDkod73YE9QFNva5gGfDb4PA94HSWAE1ZlJM7sNzcw6431OM6/T5vIdWefpCN8kXYsnQQwBNiaslwETKlT50FgPrAd6AFc7e6HXvHjwCtm5sD/uvvsoHyAu+8AcPcdZlb/ASqAmd0M3AwwfPjwNJorzVG6v5rnPtzG0x8UUbK/uuZBaB9u2ceOsiou+8RAZk5N/yFWInLiSicBNHSIV/fa0UuApcDngNHAAjN7y93LgXPdfXsQ4BeY2cfu/ma6DQwSxmxIXgaa7nZydNydHz2/kife30I84Zw+vBeXTBzAtn3JNzYN7d2FX0w/XSdrRTqQdBJAETAsZXkoySP9VDcA93jypoJCM9sInAK87+7bAdy9xMyeJTml9CZQbGaDgqP/QUBJM/sizfCLhet49N3NXDN5ODd+egQn9+/R9EYi0q6l88SrxcAYMxtpZjnAdJLTPam2ABcCmNkAYBywwcy6mVmPoLwb8HlgRbDNfOD64PP1wPPN6Ygcuz99tJ0HXl3Hl88cyk+uOlXBXyQkmhwBuHvMzGYAL5O8DHSOu680s1uD9bOAu4G5Zrac5JTRne6+y8xGAc8GJwqzgCfc/aVg1/cAvzezG0kmkK+0cN+kCYmEs2TLXv7xDx9x1oje/LiRd8WKSMekR0GExLZ9lXy0dR+rtpezakc5m3ZXsG1vJdXBG6Oev+1cPaVSpIPSoyBC6EB1jBeX7eAPS7ayeFPyHrvMDOPkvO6cMrAHF40fwJBeXbhk4kAFf5EQUgLogDbtquCRRRv5w5IiDkbijOrXje9PHcd5Y/I4uX/3Zr+GUEQ6BiWAduiFZdv57dubufPSUzjzpN415dv3VXLX/JUsWF1MVoZx+ScH87dnD+eM4b01ty8i9SgBtCNV0Tg//r/VPPruZrIzjWtmv8t/XHUqX80fxutrSvjO75YSiSWYccHJXHf2SXrBt4gckRJAO1FSXsU35i1mxbZyvvmZkdx83mi+87ulfP+Py3juw228vX43pwzswS//9ow2fdm4iLQfSgDtxP2vrGVt8QEe/rt8Lp4wAIC5N5zFj19czSOLNjH9rGHcdcVEze+LSNqUANqBbfsqeebDIq6ZPLwm+ANkZWbwr5dPZMYFJ+sqHhE5auncCSxtbPYb63GHW84f3eB6BX8RORZKACe40v3VPLV4K186Y4iesS8iLUoJ4AT3679uIBpP8K3PntzWTRGRDkYJ4AS272CEx97ZzBc/OZiR/bq1dXNEpINRAjiB/ddLa6iIxPn2BQ3P/YuINIcSwAnqd4u38OT7W7j1/NGcMjC3rZsjIh2QEsAJaOnWffzLcyv5zJh+fO+ScW3dHBHpoJQATjCl+6u59dEl9M/txH9PP53MDD3DR0Rah24EO4EU7T3I1x9ZzL7KCE9/6xx6d8tp6yaJSAemBHCCWLGtjBvmLqYqGueRr09m4uCebd0kEeng0poCMrOpZrbGzArNbGYD63ua2Z/M7CMzW2lmNwTlw8zsNTNbHZTfnrLNXWa2zcyWBj+XtVy32pe31pVy9f++Q3aG8fS3zuFTo/u2dZNEJASaHAGYWSbwEHAxUAQsNrP57r4qpdptwCp3v9zM8oA1ZvY4EAO+6+4fBC+HX2JmC1K2/bm739eiPWpn3lxbyk2/LWBUv27M+8ZkBugRziJynKQzApgMFLr7BnePAE8B0+rUcaCHJd860h3YA8TcfYe7fwDg7vuB1cCQFmt9O/fWulK++dsCRud158lvnq3gLyLHVToJYAiwNWW5iPpB/EFgPLAdWA7c7u6J1ApmNgI4HXgvpXiGmS0zszlm1psGmNnNZlZgZgWlpaVpNLd9eG1NCTfNK2Bkv248ftMUnfAVkeMunQTQ0HWIXmf5EmApMBiYBDxoZjV3L5lZd+Bp4A53Lw+KfwWMDurvAO5v6Mvdfba757t7fl5eXhrNPbFVReP8+59WccMjixmV150nvnk2fRT8RaQNpHMVUBEwLGV5KMkj/VQ3APe4uwOFZrYROAV438yySQb/x939mUMbuHvxoc9m9jDwwrF1of1YXlTGd36/lMKSA1z/qZOYeel4uuToBS4i0jbSSQCLgTFmNhLYBkwHrq1TZwtwIfCWmQ0AxgEbgnMCvwFWu/vPUjcws0HuviNYvApYcezdOLFVVMf4+YK1zFm0kbwenfjtNyZz3tj2P5oRkfatyQTg7jEzmwG8DGQCc9x9pZndGqyfBdwNzDWz5SSnjO50911m9mngOmC5mS0NdvlDd38R+KmZTSI5nbQJuKVFe3YCSCScV1bt5O4XVrNtXyXXThnOnZecQs+u2W3dNBERLDlr0z7k5+d7QUFBWzejSRXVMf64pIhHFm1k0+6DjOnfnZ986ROcNaJPWzdNRELIzJa4e37dct0J3MJ2HajmyocWUbS3ktOH9+IfLxnH1IkDycrUY5dE5MSiBNCCovEEtz3+AaX7q3n8pimce3K/tm6SiEijlABa0H+++DHvbdzDz68+TcFfRE54mpdoIc9+WMScRRu54dwRXHX60LZujohIk5QAWsDBSIx/fnYFk0f24YeXjW/r5oiIpEUJoAX8dd0uKiJx7rhwDNk62Ssi7YSiVQt4dXUxPTpncdZIXeYpIu2HEkAzxRPOwtUlXDCuv47+RaRdUcRqpqVb97K7IsLFEwa0dVNERI6KEkAzLVhVQlaGcf44PdtHRNoXJYBmWrBqJ2eP6ktuZz3fR0TaFyWAZthQeoD1pRWa/hGRdkkJoBkWri4B4MLx/du4JSIiR08JoBkWrCpm/KBchvbu2tZNERE5akoAx2j3gWoKNu/hYh39i0g7pQRwjBasKibh8PmJA9u6KSIix0QJ4Bj9ecVOhvXpwsTBuW3dFBGRY5JWAjCzqWa2xswKzWxmA+t7mtmfzOwjM1tpZjc0ta2Z9TGzBWa2Lvjdu2W61PrKKqO8vX4Xl546iORrj0VE2p8mE4CZZQIPAZcCE4BrzGxCnWq3Aavc/TTgs8D9ZpbTxLYzgYXuPgZYGCy3CwtXFxONO1NP1fSPiLRf6YwAJgOF7r7B3SPAU8C0OnUc6GHJw+HuwB4g1sS204B5wed5wJXN6cjx9OcVOxmY25lJQ3u1dVNERI5ZOglgCLA1ZbkoKEv1IDAe2A4sB25390QT2w5w9x0Awe8GL6cxs5vNrMDMCkpLS9NobuuqqI7x5tpSpp46kIwMTf+ISPuVTgJoKMp5neVLgKXAYGAS8KCZ5aa57RG5+2x3z3f3/Ly8tn/ezmtrSqiOJTT9IyLtXjoJoAgYlrI8lOSRfqobgGc8qRDYCJzSxLbFZjYIIPhdcvTNP/7+vGIn/brncNYIPftfRNq3dF4KvxgYY2YjgW3AdODaOnW2ABcCb5nZAGAcsAHYd4Rt5wPXA/cEv59vVk+OwUdb9/HwWxvYdzBKWWWUiupYzbrMDGNgz84M7d2VgbmdqYrFKa+M8pfVJVx5+hAyNf0jIu1ckwnA3WNmNgN4GcgE5rj7SjO7NVg/C7gbmGtmy0lO+9zp7rsAGto22PU9wO/N7EaSCeQrLdu1I3u7cBc3/baAztmZjOjblX7dcxjetysZwWWd0ViCHWWVvLJ9J7srImRlGD27ZDOkdxeunTz8eDZVRKRVmPtRTcm3qfz8fC8oKGj2fl5dVcy3n/iAkX278eiNk+mf2/mI9SOxBNmZpmv+RaRdMrMl7p5ftzydKaAO5e31u7jlsSWcOjiXuTdMpne3nCa3ycnSDdMi0vGELgH8dd0uAB67aQo99BIXEQmx0B3aRmIJOmVlKPiLSOiFLgFE4wmyM0PXbRGRekIXCSPxhOb0RUQIYwKIOTkaAYiIhDABaAQgIgKEMQHE4hoBiIgQwgQQjTvZWbqhS0QkdAkgEktoBCAiQhgTgC4DFREBwpgAYjoJLCICIUwA0XjyTmARkbALXSRMPtkzdN0WEakndJFQ9wGIiCSFLhJGNQIQEQFCmAA0AhARSUorEprZVDNbY2aFZjazgfXfM7Olwc8KM4ubWR8zG5dSvtTMys3sjmCbu8xsW8q6y1q4bw3SfQAiIklNvhDGzDKBh4CLgSJgsZnNd/dVh+q4+73AvUH9y4HvuPseYA8wKWU/24BnU3b/c3e/r2W6kh6NAEREktKJhJOBQnff4O4R4Clg2hHqXwM82UD5hcB6d9989M1sOdG4k52pR0GIiKSTAIYAW1OWi4KyesysKzAVeLqB1dOpnxhmmNkyM5tjZr0b2efNZlZgZgWlpaVpNLdx8YQTTzg5mZnN2o+ISEeQTgJo6HDZG6l7ObAomP45vAOzHOAK4A8pxb8CRpOcItoB3N/QDt19trvnu3t+Xl5eGs1tXDSeAPSSdxERSC8BFAHDUpaHAtsbqdvQUT7ApcAH7l58qMDdi9097u4J4GGSU02tqjqWTACaAhIRSS8BLAbGmNnI4Eh+OjC/biUz6wmcDzzfwD7qnRcws0Epi1cBK9Jt9LGKBAlAj4IQEUnjKiB3j5nZDOBlIBOY4+4rzezWYP2soOpVwCvuXpG6fXBe4GLgljq7/qmZTSI5nbSpgfUt7tAUkG4EExFJIwEAuPuLwIt1ymbVWZ4LzG1g24NA3wbKrzuKdraIQyMAnQMQEQnZncAaAYiIHBaqSFitEYCISI1QRUJdBioiclioImHNOQBNAYmIhCwBaAQgIlIjVJFQJ4FFRA4LVSTUFJCIyGGhioSRePIRRjlZehSEiEi4EkDNCEBPAxURCVUC0GWgIiKHhSoSRvQ0UBGRGqFKABoBiIgcFqpIePh9AKHqtohIg0IVCXUZqIjIYaGKhNF4gqwMIyND5wBEREKVACKxhOb/RUQCoYqG0XhC8/8iIoG0oqGZTTWzNWZWaGYzG1j/PTNbGvysMLO4mfUJ1m0ys+XBuoKUbfqY2QIzWxf87t1y3WpYJK4RgIjIIU1GQzPLBB4CLgUmANeY2YTUOu5+r7tPcvdJwA+AN9x9T0qVC4L1+SllM4GF7j4GWBgst6pIzHUCWEQkkE40nAwUuvsGd48ATwHTjlD/GuDJNPY7DZgXfJ4HXJnGNs2iEYCIyGHpRMMhwNaU5aKgrB4z6wpMBZ5OKXbgFTNbYmY3p5QPcPcdAMHv/o3s82YzKzCzgtLS0jSa27hILK4RgIhIIJ1o2NA1k95I3cuBRXWmf8519zNITiHdZmbnHU0D3X22u+e7e35eXt7RbFpPNO5k60mgIiJAegmgCBiWsjwU2N5I3enUmf5x9+3B7xLgWZJTSgDFZjYIIPhdkn6zj00kltAIQEQkkE40XAyMMbORZpZDMsjPr1vJzHoC5wPPp5R1M7Mehz4DnwdWBKvnA9cHn69P3a61RHQZqIhIjaymKrh7zMxmAC8DmcAcd19pZrcG62cFVa8CXnH3ipTNBwDPmtmh73rC3V8K1t0D/N7MbgS2AF9piQ4dSSSWoEfnJrssIhIKaUVDd38ReLFO2aw6y3OBuXXKNgCnNbLP3cCF6Te1+aLxBJ10FZCICBCyO4EjMU0BiYgcEqpoqPsAREQOC1U0jGoEICJSI1TRUCMAEZHDQhUNdR+AiMhhoYqGGgGIiBwWqmgYjTvZmXoUhIgIhCgBxBNOPOHkZGa2dVNERE4IoUkA0XjwQnhNAYmIACFKANWxZALQFJCISFJoEkAkSAB6FISISFJoouGhKSDdCCYikhSaaHhoBKBzACIiSaGJhhoBiIjUFppoWK0RgIhILaGJhroMVESkttBEw5pzAJoCEhEB0kwAZjbVzNaYWaGZzWxg/ffMbGnws8LM4mbWx8yGmdlrZrbazFaa2e0p29xlZttStrusJTtWV0QjABGRWpp8JaSZZQIPARcDRcBiM5vv7qsO1XH3e4F7g/qXA99x9z1m1gn4rrt/ELwcfomZLUjZ9ufufl8L96lBOgksIlJbOtFwMlDo7hvcPQI8BUw7Qv1rgCcB3H2Hu38QfN4PrAaGNK/Jx0ZTQCIitaUTDYcAW1OWi2gkiJtZV2Aq8HQD60YApwPvpRTPMLNlZjbHzHo3ss+bzazAzApKS0vTaG7DInEHICdLj4IQEYH0EkBDEdMbqXs5sMjd99TagVl3kknhDncvD4p/BYwGJgE7gPsb2qG7z3b3fHfPz8vLS6O5DTs8AtDTQEVEIL0EUAQMS1keCmxvpO50gumfQ8wsm2Twf9zdnzlU7u7F7h539wTwMMmpplajy0BFRGpLJxouBsaY2UgzyyEZ5OfXrWRmPYHzgedTygz4DbDa3X9Wp/6glMWrgBVH3/z0RfQ0UBGRWpq8CsjdY2Y2A3gZyATmuPtKM7s1WD8rqHoV8Iq7V6Rsfi5wHbDczJYGZT909xeBn5rZJJLTSZuAW5rfncZpBCAiUluTCQAgCNgv1imbVWd5LjC3TtlfafgcAu5+3VG0s9kOvw9ACUBEBHQnsIhIaIUmGkbjCbIyjIwMnQMQEYEQJYBILKH5fxGRFKGJiNF4QvP/IiIpQhMRI3GNAEREUoUmIkZirhPAIiIpQhMRNQIQEaktNBExEotrBCAikiI0ETEad7L1JFARkRqhSQCRWEIjABGRFKGJiBFdBioiUktoIqJuBBMRqS00ETEaT9BJCUBEpEZoImIkpikgEZFUoYmIUd0HICJSS2giokYAIiK1hSYi6k5gEZHa0oqIZjbVzNaYWaGZzWxg/ffMbGnws8LM4mbW50jbmlkfM1tgZuuC371brlv16T4AEZHamoyIZpYJPARcCkwArjGzCal13P1ed5/k7pOAHwBvuPueJradCSx09zHAwmC51WgEICJSWzoRcTJQ6O4b3D0CPAVMO0L9a4An09h2GjAv+DwPuPIo235UonEnO1OPghAROSSdBDAE2JqyXBSU1WNmXYGpwNNpbDvA3XcABL/7N7LPm82swMwKSktL02huffGEE084OZmZx7S9iEhHlE4CaOiw2RupezmwyN33HMO2DXL32e6e7+75eXl5R7NpjWg8eCG8poBERGqkExGLgGEpy0OB7Y3Unc7h6Z+mti02s0EAwe+SdBp8LKpjyQSgKSARkcPSSQCLgTFmNtLMckgG+fl1K5lZT+B84Pk0t50PXB98vr7Odi0qEiQAPQpCROSwrKYquHvMzGYALwOZwBx3X2lmtwbrZwVVrwJecfeKprYNVt8D/N7MbgS2AF9pqU7VdWgKSDeCiYgc1mQCAHD3F4EX65TNqrM8F5ibzrZB+W7gwvSbeuwOjQB0DkBE5LBQRESNAERE6gtFRKzWCEBEpJ5QRERdBioiUl8oImLNOQBNAYmI1AhFRIzGk/eeaQQgInJYKCJiJB4HdBJYRCRVKCKipoBEROoLRUSM1EwB6VEQIiKHhCMB1IwA9DRQEZFDQpEAdBmoiEh9oYiIET0NVESknlAkAI0ARETqC0VEPPw+gFB0V0QkLaGIiLoMVESkvlBExGg8QVaGkZGhcwAiIoeEIgFEYgnN/4uI1BGKqBiNJzT/LyJSR1pR0cymmtkaMys0s5mN1PmsmS01s5Vm9kZQNi4oO/RTbmZ3BOvuMrNtKesua7Fe1TF+UC5TJw5srd2LiLRL5u5HrmCWCawFLgaKSL7o/Rp3X5VSpxfwNjDV3beYWX93L2lgP9uAKe6+2czuAg64+33pNjY/P98LCgrSrS4iIoCZLXH3/Lrl6YwAJgOF7r7B3SPAU8C0OnWuBZ5x9y0AdYN/4EJgvbtvPrqmi4hIa0gnAQwBtqYsFwVlqcYCvc3sdTNbYmZ/18B+pgNP1imbYWbLzGyOmfVu6MvN7GYzKzCzgtLS0jSaKyIi6UgnATR07WTdeaMs4EzgC8AlwL+Y2diaHZjlAFcAf0jZ5lfAaGASsAO4v6Evd/fZ7p7v7vl5eXlpNFdERNKRlUadImBYyvJQYHsDdXa5ewVQYWZvAqeRPHcAcCnwgbsXH9og9bOZPQy8cPTNFxGRY5XOCGAxMMbMRgZH8tOB+XXqPA98xsyyzKwrMAVYnbL+GupM/5jZoJTFq4AVR9t4ERE5dk2OANw9ZmYzgJeBTGCOu680s1uD9bPcfbWZvQQsAxLAr919BUCQEC4Gbqmz65+a2SSS00mbGlgvIiKtqMnLQE8kugxUROToNecyUBER6YDa1QjAzEqBY72PoB+wqwWb016Esd9h7DOEs99h7DMcfb9Pcvd6l1G2qwTQHGZW0NAQqKMLY7/D2GcIZ7/D2GdouX5rCkhEJKSUAEREQipMCWB2WzegjYSx32HsM4Sz32HsM7RQv0NzDkBERGoL0whARERSKAGIiIRUKBJAOm80a+/MbJiZvWZmq4O3st0elPcxswVmti743eBjt9szM8s0sw/N7IVgOQx97mVmfzSzj4O/80919H6b2XeCf9srzOxJM+vcEfscPB6/xMxWpJQ12k8z+0EQ29aY2SVH810dPgEEbyJ7iOQTSScA15jZhLZtVauIAd919/HA2cBtQT9nAgvdfQywMFjuaG6n9sMHw9DnXwAvufspJJ+8u5oO3G8zGwL8A5Dv7qeSfC7ZdDpmn+cCU+uUNdjP4P/4dGBisM0vg5iXlg6fAEjvjWbtnrvvcPcPgs/7SQaEIST7Oi+oNg+4sk0a2ErMbCjJ91D8OqW4o/c5FzgP+A2Au0fcfR8dvN8kH17ZxcyygK4kH0vf4frs7m8Ce+oUN9bPacBT7l7t7huBQpIxLy1hSADpvNGsQzGzEcDpwHvAAHffAckkAfRvw6a1hgeA75N8Cu0hHb3Po4BS4JFg6uvXZtaNDtxvd98G3AdsIfkCqTJ3f4UO3Oc6Gutns+JbGBJAOm806zDMrDvwNHCHu5e3dXtak5l9EShx9yVt3ZbjLAs4A/iVu58OVNAxpj4aFcx5TwNGAoOBbmb2tbZt1QmhWfEtDAkgnTeadQhmlk0y+D/u7s8ExcWHXr4T/C5pq/a1gnOBK8xsE8mpvc+Z2WN07D5D8t90kbu/Fyz/kWRC6Mj9vgjY6O6l7h4FngHOoWP3OVVj/WxWfAtDAkjnjWbtnpkZyTnh1e7+s5RV84Hrg8/Xk3x7W4fg7j9w96HuPoLk3+tf3P1rdOA+A7j7TmCrmY0Lii4EVtGx+70FONvMugb/1i8keZ6rI/c5VWP9nA9MN7NOZjYSGAO8n/Ze3b3D/wCXkXw/8Xrgn9q6Pa3Ux0+THPotA5YGP5cBfUleNbAu+N2nrdvaSv3/LPBC8LnD9xmYBBQEf9/PAb07er+BfwM+Jvn62EeBTh2xzyRfn7sDiJI8wr/xSP0E/imIbWuAS4/mu/QoCBGRkArDFJCIiDRACUBEJKSUAEREQkoJQEQkpJQARERCSglARCSklABERELq/wOG/L+5UpP0GAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model_history.history['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7b3c8f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i07K70r7/CfCTDPutAE7IZm2Z5EXVxSQi0kFnUqfJj0fUghARSVFApMmPRXUtJhGRFAVEmjCLSQEhIgIKiF2EWUwagxARAQXELjSLSURkJwVEmjCLSQEhIgIKiF3kxyO0apqriAiggNiFBqlFRHZSQKTJj2kMQkSkgwIijWYxiYjspIBIozOpRUR2UkCkyU/NYnLf79tKiIj0GAqINPnxKABtCQWEiIgCIs3O+1JrHEJERAGRaIdn/wGWPJkWEBqHEBHJ6v0gDgvRGMz5H2jaRv6gYwEFhIgIqAUR9B8NW5aTH0+1IDTVVUREAQFA+WjYuoK8qLqYREQ6KCAAyo+EHWsptBYA3TRIRAQFRND/CADKmtYCakGIiIACIigfDUBJ4ypA01xFRCDLAWFmU81siZktM7NbMqw/y8y2m9m81Nd3u7pvt+ofAqJP/UoAWtrUghARydo0VzOLAncA5wPVwCwze8zdF3Xa9BV3v2Q/9+0eBaVQPIDCupXACepiEhEhuy2IKcAyd1/h7q3AfcDlB2Hf/dN/NPk7VgLQmlAXk4hIlwLCzG4ys1ILfmlmc83sgo/YbSiwJu1xdWpZZ6ea2Ttm9qSZjd/HfTGzG8xstpnNrqmp6crhZFY+mnjtB4C6mEREoOstiL9w9x3ABcAA4PPADz5iH8uwrPNV8OYCI939BODHwCP7sG9Y6H63u09298kDBgz4iJL2onw00cZNFNOkLiYREboeEB1v2BcBv3L3d8j8Jp6uGhie9ngYsC59A3ff4e71qe+fAOJmVtGVfbtdaqB6lG3ULCYREboeEHPMbCYhIJ42sxLgoz5mzwLGmFmVmeUB04HH0jcws0FmZqnvp6Tq2dKVfbtdeUdAbFAXk4gIXZ/FdD0wAVjh7o1m1p/QzbRH7t5uZl8GngaiwD3u/q6Z3ZhafxdwJfBXZtYONAHTPdytJ+O++354+yB1stwRkQ3qYhIRoesBcSowz90bzOxa4ETg9o/aKdVt9ESnZXelff8T4Cdd3Ter8oqhZDCjt29gUUIBISLS1S6mnwKNZnYC8LfAKuB/s1ZVrvQfTVVkg67mKiJC1wOiPdX1czlwu7vfDpRkr6wcKT+CEWygsVUBISLS1YCoM7NbgeuAx1NnOsezV1aOlB9JP3ZQvX59risREcm5rgbE1UAL4XyIDYST1v4za1XlSmqqa/PGpTSpFSEivVyXAiIVCr8FyszsEqDZ3XveGERqqutwX8/86trc1iIikmNdvdTGVcBbwKeAq4A3zezKbBaWE/2qcIwq28Dc1bW5rkZEJKe6Os3128BJ7r4JwMwGAM8CD2arsJyIF2D9RnHKjtXcs3pbrqsREcmpro5BRDrCIWXLPux7eDnqQiYn3+G9lesIE7dERHqnrr7JP2VmT5vZ58zsc8DjHMyT2A6msZcS91aOb57F6q2Nua5GRCRnutTF5O7fMrMrgNMJF+m7291nZLWyXBlxKu2FFUxLvMXc1dsYWV6c64pERHKiy91E7v6Qu/+Nu3+9x4YDQCRKZOwlnBN5m3c+2JDrakREcmavAWFmdWa2I8NXnZntOFhFHmyRcZdSZC3Y8hdzXYqISM7sNSDcvcTdSzN8lbh76cEq8qAbdSbN0RLG73iJhpb2XFcjIpITPXMm0oGK5VE7/FzOi8xh/uoDuI2piMhhTAGxByUTP0lfa2DT/GdzXYqISE4oIPageNwFNFFAn+WP57oUEZGcUEDsSbyQFQPP59SG51m7tjrX1YiIHHQKiL2ouOCbFFkLq57+Ua5LERE56BQQezFw9ATmFZzM2NW/I9Gis6pFpHdRQHyExpO+RD92sOK5X+S6FBGRg0oB8REmnXkJCxlN2ds/g6RuIiQivUdWA8LMpprZEjNbZma37GW7k8wskX6PCTNbaWYLzGyemc3OZp17kx+Psajq81S2VdMw/7FclSEictBlLSBS962+A5gGjAM+bWbj9rDdvwNPZ3ias919grtPzladXXHsudeyKllJ83P/BgmdWS0ivUM2WxBTgGXuvsLdW4H7gMszbPcV4CFgU4Z1h4Rxw/pxb+lfUF63hMQbd+W6HBGRgyKbATEUWJP2uDq17ENmNhT4BJDpXdeBmWY2x8xu2NOLmNkNZjbbzGbX1GTvshgTL/wczyUmknz+n6F2zUfvICJymMtmQFiGZZ1v0fZD4GZ3zzT6e7q7n0joovprMzsz04u4+93uPtndJw8YMOCACt6bC48dxP2VX6U9kSTxx2+A7jYnIj1cNgOiGhie9ngYsK7TNpOB+8xsJXAlcKeZfRzA3del/t0EzCB0WeWMmXH9xX/Gf7VdSXTZ07Do0VyWIyKSddkMiFnAGDOrMrM8YDqwyzQgd69y91HuPgp4EPiSuz9iZsVmVgJgZsXABcDCLNbaJScfUc7qIz/LIqpIPv4N2K5LcIhIz5W1gHD3duDLhNlJi4EH3P1dM7vRzG78iN0HAq+a2TvAW8Dj7v5UtmrdF9+cNp6vtn6JtuZGuP86aGvOdUkiIllh3oP60idPnuyzZ2f/lImbH5zP9rdncFf8Nph4HVz2Y7BMQy4iIoc2M5uzp1MJdCb1frj1omOYU3Q6vyu4Gt7+P5jzq1yXJCLS7RQQ+6FvUR7/+onj+Hbtpazseyo8eQts/SDXZYmIdCsFxH46f9xALp0wjGs3fYakReHJmzX1VUR6FAXEAfiHS8fTXDSIe+LTYenTsOTJXJckItJtFBAHoF9xHj/45PH8YNtZbCyoCq2IVt03QkR6BgXEATpv3ECuP/Movrr9Wti+Gl7571yXJCLSLRQQ3eCbFx5NcuRpPOYfI/mnH2nAWkR6BAVEN4hHI/z40ydyZ/RaWpJG4pnv5rokEZEDpoDoJoPKCrj56nO4q+0Soosfg1Wv57okEZEDooDoRmcfXcmasdezwfvT8vgtkEzmuiQRkf2mgOhmt1w2iR/xafI3zcMXPJDrckRE9psCoptVlhYw9sK/ZH6yiqYnvwfNO3JdkojIflFAZME1p1Txf/2/Ql7zJlofvUlnWIvIYUkBkQXRiPHnn7qS29uvIG/xwzDvt7kuSURknykgsuTYoWVsO/HLvJ4cR/Lxb0LNklyXJCKyTxQQWfSNC8fxnchN1Cfz8Ac/r8twiMhhRQGRRf2K8/jzC0/hq81fhI2L4KHrIZnIdVkiIl2igMiya04eycaBZ3Jb7HpY8gQ88S0NWovIYUEBkWXRiPHPHx/PHQ3n8Hz/6TD7l/CnH+a6LBGRj6SAOAgmjezPV88dw/XrLmH1kGnw7D/Aaz/OdVkiInulgDhIvnLOGE4+ooJL1nyG+tEXw8y/h6e/rctxiMghK6sBYWZTzWyJmS0zs1v2st1JZpYwsyv3dd/DRTRi3D59IvG8Aq7a/AXaJ/0lvP4TmPFFaGvOdXkiIrvJWkCYWRS4A5gGjAM+bWbj9rDdvwNP7+u+h5uBpQX891UnsHhTI1/aOp3kOd+FBQ/AXWfo6q8icsjJZgtiCrDM3Ve4eytwH3B5hu2+AjwEbNqPfQ87Zx1dyfcuGcfMxZv4p+1T4boZ0N4Cv5oKj38TWhtyXaKICJDdgBgKrEl7XJ1a9iEzGwp8ArhrX/dNe44bzGy2mc2uqak54KIPhs+dXsX1Z1Txqz+t5Jfrq+BLr8PJN8KsX8CvLoId63NdoohIVgPCMizrfALAD4Gb3b3z2WNd2TcsdL/b3Se7++QBAwbse5U58u2LxjLt2EH88+OLePjdWpj27/Dp+2DzUvjFubBhQa5LFJFeLpsBUQ0MT3s8DFjXaZvJwH1mthK4ErjTzD7exX0Pa5GI8f+unsBpo8v5xu/f4cE51XD0VLg+NRTzywth1i8h0ZbbQkWk18pmQMwCxphZlZnlAdOBx9I3cPcqdx/l7qOAB4EvufsjXdm3JyiIR/nln5/EGUdW8K0H3+H+Wath0HHwhedhyER4/G/gzlNh8R909rWIHHRZCwh3bwe+TJidtBh4wN3fNbMbzezG/dk3W7XmUkE8ys8/O5mPjRnAzQ8t4NevrYSSQfC5P8L0e8EM7r8WfjwJXvg32Lws1yWLSC9h3oM+mU6ePNlnz56d6zL2S3Nbgi/f+zbPLt7IV88dw9fPG4OZQaIdFvwe3rkXPngFcBhxahjUPuYSiMZyXbqIHMbMbI67T864TgFx6GhPJPm7GQt4YHY1nzl5BN+//FiikbTx+h3rQ1jM+gXUroKy4XDSX8KJn4Wi/rkrXEQOWwqIw4i78x9PL+GnLy7n1CPKue3qExhcVrjrRskEvP8UvPFTWPkKRPPhuCth5GnQ1gTtzVA6FI65GGL5uTkQETksKCAOQw/MXsM/PPYu8WiEf/vkcVx03ODMG25cBLN+Du/cD22dTrIrKoeJ18KxV0LFGIgXZn4OEem1FBCHqZWbG7jp/nm8s6aWT00axj9cNp7i/D2MObTUQ0MN5BVDrADWzobZ98B7T4AnAAtdUoOPhxOmw1FTIRo/qMcjIoceBcRhrC2R5PZnl3LHi8sYVV7M7dMncPywvl1/gh3rYdWfYMty2LIsdEnVrYfiAaFlMep0GDYFSgZm7RhE5NClgOgB3lixha/fP4+auhauOXkEV04axnFDy8JMp32RaIflz8Hc/4WlMyHRGpYXDwBPhutCJdpCSyS/BAr7wtDJMOqMMMYRyw/bJBNQOiRMwxWRw5YCoofY3tjGPz2+iMfeWUdre5IxlX24+qThXHHiMPoV5+37E7a3wPr5sOZN2LwEonmheyoShdZGaKmD+o1QPQta63fff8RpMO0HMPiEAz84EckJBUQPs72xjT8uWMeDc6p5e3UtebEIlxw/mGtPGcnE4X33vVXxURLtsH4eVM8GPLQimreHu+I1bg3TbI+5GIorQkukdBhEdC8qkcOBAqIHW7x+B799cxUz5q6loTXB+CGlXHvKSC6fMISivCyfRNdUCy/9B7z1M0i271xeNgImfBomXAP9Ru2+XzIZuqY6B1nDFqhbBwOOOfAB9ET73k8ibKmHub8O3WcjTj6w1xI5jCkgeoH6lnYeeXstv3ljFe9tqKM4L8rFxw/mU5OHM3lkv+5vVezy4jXhxL2GzeENfvEfYPkLgIcWRawwtDoSrdBcC807wol9o8+BI88Hi4QTAJc/F4ImVhC6rYZPCetHnAqxVBdaoi0EU3FF5vGPtiZ45TZ47Ucw5Qtw3vd3bc0k2kIwvPjv0LAJ4sXw2Udh+EnZ+/mIHMIUEL2IuzNn1Tbun7WGxxesp7E1wYj+RUw7dhBTjx3EhGx0QWWyvRrmPwC1q8NYR3sTROJh0Lugb1i+7Flo3By2Lx0aTvYbeCysmxem6a57O4RKXkmYnrtjLdSuCdN2i8pDiAw6LrRSSodBogVmfge2fQCDJ4RusbGXwSfvhkgM5t8PL/9XWD/iNDjja/DULdC4BT73BAw6Nvs/F5FDjAKil2poaeeJBev5w/z1vLZsM+1JZ1BpAeeMreS8sZWcNrqCgng0dwUmk+FNPNEGw07afdyipR5WvBhmW21aDH2HQ/8joLBfeLx+Xvg3vXur/xFw8W1wxFnhTPOn/y6ESHNtCKVBx8PZfxfOAzGDbavgnqnhOS76zxAknoDK8VBx5EH7UYjkigJC2N7YxrOLNzJz0QZeWbqZxtYERXlRzhs7kEtPGMKZR1WQH8thWOyvZALqNoTWRdM2qPoziBfsXL/4D/DwDVA5Fs78Wzjqwt27pmqWwK+mhZZEB4uEwfezvw19Kg/OsciBcQ8fKIaeCAVlua7msKGAkF00tyV4Y8UWnn53A08u3EBtYxslBTGmjh/EpScM4bTR5cSiPWgWUltzGAPZW9da41bYtjJM8QWYd2+4KGKsILRG6jeFEwwjMag6E0afDcNPhsL+uwZSh2QC3v4/WPgQnPE3YfuuSCbDeM7Gd8PU45Fn7N8getM22PReuK9Ipvp6old/CM9+L7T+rpuhkz+7SAEhe9SWSPKnZZt57J11PPPuRupa2ikvzuPSE4Zw5aRhjB9SenDGLA5Fm5fB898P17sqHQwlg8O5IStfhZYdO7eLFYR7eFT9GRx5XjjJ8JnvwcYFkNcnnEMy+S/g/O+HW8rO/V9Y+gwceQ6c/jUoHx2e962fwxt3hkumfMhgyg1w7nchv8/uNTZuDeGXV7xz2baV8OvLQtDEi0PAjb00jPH01MurzP89PPyXMOpjsHZO+F199tHQLZktifbwuyrtdJ20ZBI2LoR+I/fekmncGv5/xPbjHKZupICQLmluS/DS+zU8Om8tzy7aRGsiyVED+3DlpGF8fOJQKkt6ySfRj5JoD29CGxeEGVXN22HrCvjg5Z3BUTY8BMJRU+GFf4HX74B4UbigYqwwXOLkg1cg2RZmaq15M4yTjD4Xxl0eBuv7joBX/gve/Fl4oxt3eWgNtTeFwfpNi8NMrIIyOOvv4KTrd4ZDWyNc8E9hwH/pTNi+BsqPhHP+HsZeHk5+XPgQrH4t1FVQFv5t3g5NW8OJklUfC5djGXHq3s9radoGz/1TOKmydEj4isRC6LXUheceOgmGnAjF5Zmfo6053Id97ewwG+6oC8MU5I7XrV0TWlUlA6FfVZjs0GHFi/CbK0OL7rqHwzH/9lPhSgDnfS+8dr+qzMeweVn4HVSO3bf/AxsWwiN/BRvmh9/xud+DgeNg+fPwzHd33lO+4ugwvlZ1Zpi112cAVM+B126HRY+FDxfDJofaE63hcjhbloV6J1wDR08LHwAatsD6t8GiYXZfxweC9pbwu9y+Fk64et+OIUUBIftse2Mbf5i/jofmhpPxohHjz44awPSThnPOMZU9qwuquyTawh/rtlXhzTyvaOe61W+EFsLIU+G4T4U3zbqN8PpPQnfW8JPhzG+G/vPOVr0Of/x6ePOPF4SAKRkYulIGHB3elFa8EN6MmraGfa57ZOesLHdY8iQ8932oWRxmhbXWhcvEjzwtXGKleTu0NoS6ivqHN6IPXgpBUzIY+o8Ob8qF/WDk6XDMRWHbla+GMZ76jWGbug3Qsn1n7Xl9wvOSep8prgxTlIvKU298m8Mn6br14Y0awviPJ8PMtOFTYN3ccOzp8ktDCEEI5fIx8BdP7QyO9fNDSNRvSG1fBkddEFpyI04Nr/f8P4efPQ4DjwtvsMNOCvW2NkBBafi9pF8FuaUuhP3L/xl+FsddBW//JtQw8NjwoaHvCDj9JmjcFgJvzVs7fy99R4TJEvllMOmz4cPG6tdCoERiIcT7VYUZfHXrwoy/gtKwT4dILIRtvDB8sGhvDs938wc7u0j3gQJCDsjymnoemlPNQ3Or2bijhUGlBVx90nCuPmk4Q/rqEuI51xEAM78dPlFe9wgMOGr37ZIJeOe+0NIZfU74dFpQuufnbakPz7vk8RBmzbUhCBq3hHAZdlK4EGT/I+CKX+wMt5b68Aaf1yd8am+p2zl1eesHYf+GmvCJuagiBEbJ4LD/0MkhWJc8Be/OCDPVhk4KXUdDJoYW09YPwjRqT4bXixfAKV8KLZd0iXaoeS+82Va/Be8+GsKrfExq/wSc/MVwYuf8+0KrsLNofgj10mEhqDYtBjyE/NR/Dy2ixq3w6m2h23DideH8m/T7sHTM1lv+fPgAUXVmmACRX7Jzm9bGsE/HG3wyEVpG8+8PP6chE8NXojWE8spXQzCM+lj4Gnnarq2qfaCAkG7Rnkjy3HubuPfN1by8NPSTnzkmtCrOHTuQvJhaFTmVTIRWTDYHpd3DJVfefRjefzp0Q13wL5nHRw41rY2h7nn3hjA55+93PdN/8zKoXRmCLa84tIaWvxBaZ/Ubwxt0R3fRyNNydRTdTgEh3W7N1kZ+P3sND8yuZsOOZvoVxbnshCFcsb9XmRWRnFBASNYkks7L79fw4Nxqnlm0kdb2JFUVxVw4Ppy5ffzQMiIRhYXIoSpnAWFmU4HbgSjwC3f/Qaf1lwP/BCSBduBr7v5qat1KoA5IAO17OoB0Cojc2t7YxuML1vPkwvW8vnwL7UmnKC/KmMo+jBlYwqSR/Thv7EAGlOg+2SKHipwEhJlFgfeB84FqYBbwaXdflLZNH6DB3d3MjgcecPdjUutWApPdfXNXX1MBcejY3tjG80s2Mr96O+9vrGPJhno217dgBpNH9uPsYyo5aVR/jhtaltvLfYj0cnsLiGxeD3oKsMzdV6SKuA+4HPgwINw9/S40xXw4F04Od2VFcT4xcRifmDgMCBcRfG9DHU8t3MDT727gP55aAkBeNML4oaWcMKwvxw0tY8KIvhxRUawxDJFDQDYDYiiwJu1xNbDbNQPM7BPAvwGVwMVpqxyYaWYO/Mzd7870ImZ2A3ADwIgRI7qncul2ZsbYwaWMHVzK188/ii31LcxZtY3Zq7Yxb3UtD8xew/+8thKAij75nFzVn4kj+jK0byGDygoY3r+Iij7qmhI5mLIZEJk+Au7WQnD3GcAMMzuTMB5xXmrV6e6+zswqgWfM7D13fznD/ncDd0PoYuq26iWryvvkc8H4QVwwfhAQBrtX1NQzZ9U23vxgK2+u2MLjC9bvss/QvoWcOLIfxw8to6Ikj7LCOH2L8hhSVkhlSb4Gw0W6WTYDohpIvxDKMGDdnjZ295fNbLSZVbj7Zndfl1q+ycxmELqsdgsI6RmiEWPMwBLGDCxh+pQRuDu1jW2s297Ehu3NfLC5gbdX1zJ75Vb+8M7u/43iUWNo30ImDO/LKUeUM6WqP0P6FpIfi6i7SmQ/ZTMgZgFjzKwKWAtMB65J38DMjgSWpwapTwTygC1mVgxE3L0u9f0FwPezWKscYsyMfsV59CvOY/yQXS94VtvYyrbGttS/raytbWbttiZWbm7g1WWbeWTezgDJi0YoLYxzclV/LpswhLOOHnB4XtZcJAeyFhDu3m5mXwaeJkxzvcfd3zWzG1Pr7wKuAD5rZm1AE3B1KiwGErqdOmq8192fylatcnjpW5RH36I8wryGXbk7y2samLNqK1saWtnR1E5NXQsvLNnE4wvWU1IQY/SAPpQUxCgtiFOcH6UoL0ZBPMqo8iJOHV3OiP5FanWIoBPlpJfouKz5kws2sG57Ezua26lrbqOxJUFTW4LG1nbaEuFvYUhZAccMLqWsME5ZYZxBZQXhXI7KEob1K9RYh/QouZrmKnLIiEcjnHV0JWcdnfnucKHlUc/rK7byxvItrNzSwPsb69je2EZdy85bmubFIozsX8TI8mIGlOTTlkjS0p4kYlBVUcyRlX04srIPo8qLdX6HHPbUghD5CNub2li2qY6lG+tZXlPPyi2NrNrSwNaGNvJjEfLjEVrbk6ytbSL9z2lIanpucX6M/FiE4vwYxw8rY0pVf46qLMGBmroWNte3MKqimD75+rwmB59aECIHoKwwzqSR/Zk0sv9et2tqTbBicz3LNtWzcnMjK7c0UL2tkU11zbS0JdnW2MaDc6oBKM6L0tyeJJEMiRKLGCeO6McZYyo+nH2VH4sQTevOGlNZwojyooyvLZINCgiRblKYF2X8kLLdZl11cHeqtzXx5gdbWVBdS0lBGN/oX5zHgrXbeWVpDbc98/5eX2P8kFIuOm4wg8sKqN7WRPW2RtoTzoCSfAaU5BONGBt2NLNxezORiHHa6ArOHFNBZWkBiaSzpb6F+pZ2hvQtVBeYfCR1MYkcQrY3tbGjqY2W9gTNbUmSqb/PRNKZs2obTyxYz9zVtR9uX1mSTzwaoaa+hdb2cAOdvGiEytJ8mloTbGloBaCiTx7bGts+bLF07Du8fxHD+hUyrF8hQ/oW0ic/RlFejLxYhNrGVjbXt7K9sZXBfQs5amAfjqwsoSQ/hhma6dVD6HLfIj3Ixh3N1Le0MzStFeDu7Ghupz2RpH9xHmZGMhmuf/XK0hpW1DQwoCSfgWUFFOdFWVfbxOqtjazZ2kR1bSPrapt3CY+uikeNgliU/HiEij4hcIb3K6I4P0prIklbu1NaGGNMZQlHVvahqqJ4txtLJZLO1oZW3B0HCmJRyori3fGjki7QGIRIDzKwtICBnZaZGWWFu76pRiLGuCGljBuyl9uKprQnkmyub6W+pZ2m1gTN7Qn6Fsap6JNPaWGcdbVNvL+xjmWb6mlqS+AerpvTlkjS3BZaO5t2NLNqSwOvLt1Mc3uCeDRCPGI0praHMNYyqqKYowb2oTAe4/2NdSzdVEdzW3KXeo6s7MOUqv6MG1zK1oZW1m9voqaulXjUyI9FKMqPhTGbIysYVBbuoOfuNLclyes0dtMhmXTqWtrZ0dRGWVGc0gKF0EdRC0JEupW779L9lD54//7GOt7fWM/SjXU0tCY4emAJRw8qYUT/IqIRI2LGtsZWZq3cyuyV26hPTTGu6JNHRZ98EkmnpT1JbWMrO5rDupHlRSSSzub6FprbkphBaUGcvkVxkqnQaG5L0NDSTrLTLLOjB5XQpyAenq+pjZb2JNGIEYsY+fFouN5XYZyCeDQEYXsSd6dfUR79i/OIR40VmxtYXtPAutqmMFstL0ZZYZxTRpdzwbiBjB9S+uHPI5F0NtU1s2ZrE+tqmygrijOyfxHD+hURjxrtSae5LUF+LHrQbuGrLiYROey0J5JsrGuhvDhvtwH1ju6zPy3bzJxV2yjMi1LRJ5xh3xEgtY1tRCNGQTxCfixKSUF44y4tiLO5oYX3N9Tx3oY6mtsSlBWFiz/mxyIkk0570mlqS7CjqY3tTW00tSUojEcpiEcxYGvq+QEGluYzekAfhvcroi2RpKG1nY07WphfXUvSw9WJ41GjvqV9t5Dq0NHgSV9XlBelb2GcWDRCeyJJW9JxdyJmRCOGAe1JJ5kKrGf+5s/26+esLiYROezEohGG9i3MuC69++wLB7muDu2JJK2JJEV5md9Gt9S38Px7m3h9xRaiZhTnxygpiIXL1/crYkjfAmob21i1pZHVWxtJJJ2CeIS8WDivJlxvrI1EMkksGiEetQ/HlhLJMF4TixiRiGWtu0wtCBGRXmxvLYiD08klIiKHHQWEiIhkpIAQEZGMFBAiIpKRAkJERDJSQIiISEYKCBERyUgBISIiGfWoE+XMrAZYtZ+7VwCbu7Gcw0FvPGboncfdG48Zeudx7+sxj3T3AZlW9KiAOBBmNntPZxP2VL3xmKF3HndvPGboncfdncesLiYREclIASEiIhkpIHa6O9cF5EBvPGboncfdG48Zeudxd9sxawxCREQyUgtCREQyUkCIiEhGvT4gzGyqmS0xs2Vmdkuu68kWMxtuZi+Y2WIze9fMbkot729mz5jZ0tS//XJda3czs6iZvW1mf0w97g3H3NfMHjSz91K/81N7+nGb2ddT/7cXmtnvzKygJx6zmd1jZpvMbGHasj0ep5ndmnp/W2JmF+7La/XqgDCzKHAHMA0YB3zazMbltqqsaQe+4e5jgVOAv04d6y3Ac+4+Bngu9binuQlYnPa4Nxzz7cBT7n4McALh+HvscZvZUOCrwGR3PxaIAtPpmcf8P8DUTssyHmfqb3w6MD61z52p970u6dUBAUwBlrn7CndvBe4DLs9xTVnh7uvdfW7q+zrCG8ZQwvH+OrXZr4GP56TALDGzYcDFwC/SFvf0Yy4FzgR+CeDure5eSw8/biAGFJpZDCgC1tEDj9ndXwa2dlq8p+O8HLjP3Vvc/QNgGeF9r0t6e0AMBdakPa5OLevRzGwUMBF4Exjo7ushhAhQmcPSsuGHwN8CybRlPf2YjwBqgF+lutZ+YWbF9ODjdve1wH8Bq4H1wHZ3n0kPPuZO9nScB/Qe19sDwjIs69Hzfs2sD/AQ8DV335HrerLJzC4BNrn7nFzXcpDFgBOBn7r7RKCBntG1skepPvfLgSpgCFBsZtfmtqpDwgG9x/X2gKgGhqc9HkZolvZIZhYnhMNv3f3h1OKNZjY4tX4wsClX9WXB6cBlZraS0H14jpn9hp59zBD+X1e7+5upxw8SAqMnH/d5wAfuXuPubcDDwGn07GNOt6fjPKD3uN4eELOAMWZWZWZ5hMGcx3JcU1aYmRH6pBe7+21pqx4D/jz1/Z8Djx7s2rLF3W9192HuPorwu33e3a+lBx8zgLtvANaY2dGpRecCi+jZx70aOMXMilL/188ljLP15GNOt6fjfAyYbmb5ZlYFjAHe6vKzunuv/gIuAt4HlgPfznU9WTzOMwhNy/nAvNTXRUA5YdbD0tS//XNda5aO/yzgj6nve/wxAxOA2anf9yNAv55+3MA/Au8BC4H/A/J74jEDvyOMs7QRWgjX7+04gW+n3t+WANP25bV0qQ0REcmot3cxiYjIHiggREQkIwWEiIhkpIAQEZGMFBAiIpKRAkLkEGBmZ3VcbVbkUKGAEBGRjBQQIvvAzK41s7fMbJ6Z/Sx1r4l6M/tvM5trZs+Z2YDUthPM7A0zm29mMzqu0W9mR5rZs2b2Tmqf0amn75N2D4ffps4IFskZBYRIF5nZWOBq4HR3nwAkgM8AxcBcdz8ReAn4XmqX/wVudvfjgQVpy38L3OHuJxCuF7Q+tXwi8DXCvUmOIFxLSiRnYrkuQOQwci4wCZiV+nBfSLgoWhK4P7XNb4CHzawM6OvuL6WW/xr4vZmVAEPdfQaAuzcDpJ7vLXevTj2eB4wCXs36UYnsgQJCpOsM+LW737rLQrPvdNpub9ev2Vu3UUva9wn09yk5pi4mka57DrjSzCrhw/sAjyT8HV2Z2uYa4FV33w5sM7OPpZZfB7zk4R4c1Wb28dRz5JtZ0cE8CJGu0icUkS5y90Vm9vfATDOLEK6m+deEG/KMN7M5wHbCOAWEyy7flQqAFcDnU8uvA35mZt9PPcenDuJhiHSZruYqcoDMrN7d++S6DpHupi4mERHJSC0IERHJSC0IERHJSAEhIiIZKSBERCQjBYSIiGSkgBARkYz+P1bNCX+EvVZvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for loss\n",
    "plt.plot(model_history.history['loss'])\n",
    "plt.plot(model_history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e42668f6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}