Artificial Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5598ebf1",
   "metadata": {},
   "source": [
    "### Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c11bfea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6bc9b6c",
   "metadata": {},
   "source": [
    "### Importing the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f119a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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": [
    "data = pd.read_csv(\"Churn_Modelling.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2f77e67f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = data.iloc[:,3:-1].values\n",
    "y1 = data.iloc[:,-1:].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1421b68",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e59bcdec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['France', 'Spain', 'Germany'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Encoding the categorical values\n",
    "### In our dataset Geography is the categorical data- France,Spain,Germany\n",
    "data.Geography.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6233360d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct = ColumnTransformer(transformers=[(\"encode\",OneHotEncoder(),[1])],remainder='passthrough')\n",
    "X1 = np.array(ct.fit_transform(X1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31fcc741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 0.0, ..., 1, 1, 101348.88],\n",
       "       [0.0, 0.0, 1.0, ..., 0, 1, 112542.58],\n",
       "       [1.0, 0.0, 0.0, ..., 1, 0, 113931.57],\n",
       "       ...,\n",
       "       [1.0, 0.0, 0.0, ..., 0, 1, 42085.58],\n",
       "       [0.0, 1.0, 0.0, ..., 1, 0, 92888.52],\n",
       "       [1.0, 0.0, 0.0, ..., 1, 0, 38190.78]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Encoded Data\n",
    "X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12004a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### LabelEncoding the Gender column\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "X1[:,4] = le.fit_transform(X1[:,4])\n",
    "X1[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cc15dbc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, 0.0, 0.0, ..., 1, 1, 64716.08],\n",
       "       [0.0, 1.0, 0.0, ..., 1, 1, 131886.46],\n",
       "       [1.0, 0.0, 0.0, ..., 1, 1, 99661.46],\n",
       "       ...,\n",
       "       [1.0, 0.0, 0.0, ..., 1, 0, 195033.07],\n",
       "       [0.0, 1.0, 0.0, ..., 1, 0, 176680.16],\n",
       "       [1.0, 0.0, 0.0, ..., 1, 0, 151316.37]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Splitting the data into test and training set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X1,y1,test_size=0.2,random_state=2)\n",
    "X_train[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "05d1e267",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train,X_test = scaler.fit_transform(X_train),scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde24c45",
   "metadata": {},
   "source": [
    "### Building an ANN-(ARTIFICIAL NEURAL NETWORK)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a1a7e8",
   "metadata": {},
   "source": [
    "1. Initializing the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a21df092",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann = tns.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2df1359",
   "metadata": {},
   "source": [
    "2. Adding the 1st Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "452a0872",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.add(tns.keras.layers.Dense(units = 6, activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fcdb10b",
   "metadata": {},
   "source": [
    "3. Adding the 2nd Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "648b3238",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.add(tns.keras.layers.Dense(units = 6, activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13bb3a3",
   "metadata": {},
   "source": [
    "4. Adding the output Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d872dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.add(tns.keras.layers.Dense(units = 1, activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a829bfc9",
   "metadata": {},
   "source": [
    "### Training the ANN model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e677ab",
   "metadata": {},
   "source": [
    "1. Compiling the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ea0659ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c458e0e",
   "metadata": {},
   "source": [
    "2. Training the ANN on training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "994b8f05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "250/250 [==============================] - 4s 2ms/step - loss: 0.6214 - accuracy: 0.7074\n",
      "Epoch 2/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.4982 - accuracy: 0.8021\n",
      "Epoch 3/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.4604 - accuracy: 0.8119\n",
      "Epoch 4/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.4426 - accuracy: 0.8174\n",
      "Epoch 5/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.4316 - accuracy: 0.8179\n",
      "Epoch 6/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.4239 - accuracy: 0.8189\n",
      "Epoch 7/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.4178 - accuracy: 0.8225\n",
      "Epoch 8/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.4126 - accuracy: 0.8249\n",
      "Epoch 9/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.4081 - accuracy: 0.8255\n",
      "Epoch 10/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.4047 - accuracy: 0.8281\n",
      "Epoch 11/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.4009 - accuracy: 0.8313\n",
      "Epoch 12/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3966 - accuracy: 0.8347\n",
      "Epoch 13/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3914 - accuracy: 0.8372\n",
      "Epoch 14/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3858 - accuracy: 0.8415\n",
      "Epoch 15/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3786 - accuracy: 0.8486\n",
      "Epoch 16/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3696 - accuracy: 0.8525\n",
      "Epoch 17/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3603 - accuracy: 0.8543\n",
      "Epoch 18/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3527 - accuracy: 0.8574\n",
      "Epoch 19/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3479 - accuracy: 0.8569\n",
      "Epoch 20/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3443 - accuracy: 0.8584\n",
      "Epoch 21/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3427 - accuracy: 0.8569\n",
      "Epoch 22/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3401 - accuracy: 0.8580\n",
      "Epoch 23/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3398 - accuracy: 0.8585\n",
      "Epoch 24/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3380 - accuracy: 0.8605\n",
      "Epoch 25/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3373 - accuracy: 0.8583\n",
      "Epoch 26/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3370 - accuracy: 0.8593\n",
      "Epoch 27/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3360 - accuracy: 0.8610\n",
      "Epoch 28/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3360 - accuracy: 0.8611\n",
      "Epoch 29/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3350 - accuracy: 0.8619\n",
      "Epoch 30/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3348 - accuracy: 0.8596\n",
      "Epoch 31/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3346 - accuracy: 0.8612\n",
      "Epoch 32/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3344 - accuracy: 0.8606\n",
      "Epoch 33/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3337 - accuracy: 0.8621\n",
      "Epoch 34/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3341 - accuracy: 0.8622\n",
      "Epoch 35/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3334 - accuracy: 0.8630\n",
      "Epoch 36/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3338 - accuracy: 0.8616\n",
      "Epoch 37/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3333 - accuracy: 0.8614\n",
      "Epoch 38/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3335 - accuracy: 0.8625\n",
      "Epoch 39/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3329 - accuracy: 0.8609\n",
      "Epoch 40/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3326 - accuracy: 0.8619\n",
      "Epoch 41/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3326 - accuracy: 0.8612\n",
      "Epoch 42/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3322 - accuracy: 0.8626\n",
      "Epoch 43/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3324 - accuracy: 0.8639\n",
      "Epoch 44/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3324 - accuracy: 0.8611\n",
      "Epoch 45/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3325 - accuracy: 0.8614\n",
      "Epoch 46/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3321 - accuracy: 0.8625\n",
      "Epoch 47/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8621\n",
      "Epoch 48/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3318 - accuracy: 0.8639\n",
      "Epoch 49/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3318 - accuracy: 0.8634\n",
      "Epoch 50/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3319 - accuracy: 0.8624\n",
      "Epoch 51/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3314 - accuracy: 0.8643\n",
      "Epoch 52/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3317 - accuracy: 0.8625\n",
      "Epoch 53/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3316 - accuracy: 0.8648\n",
      "Epoch 54/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3320 - accuracy: 0.8624\n",
      "Epoch 55/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3313 - accuracy: 0.8641\n",
      "Epoch 56/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3313 - accuracy: 0.8633\n",
      "Epoch 57/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3312 - accuracy: 0.8611\n",
      "Epoch 58/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3312 - accuracy: 0.8633\n",
      "Epoch 59/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3312 - accuracy: 0.8631\n",
      "Epoch 60/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3312 - accuracy: 0.8641\n",
      "Epoch 61/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3310 - accuracy: 0.8654\n",
      "Epoch 62/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3313 - accuracy: 0.8634\n",
      "Epoch 63/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3309 - accuracy: 0.8615\n",
      "Epoch 64/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3304 - accuracy: 0.8640\n",
      "Epoch 65/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3308 - accuracy: 0.8630\n",
      "Epoch 66/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3309 - accuracy: 0.8626\n",
      "Epoch 67/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3308 - accuracy: 0.8631\n",
      "Epoch 68/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3308 - accuracy: 0.8618\n",
      "Epoch 69/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3306 - accuracy: 0.8645\n",
      "Epoch 70/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3306 - accuracy: 0.8636\n",
      "Epoch 71/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3304 - accuracy: 0.8624\n",
      "Epoch 72/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3307 - accuracy: 0.8622\n",
      "Epoch 73/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3307 - accuracy: 0.8620\n",
      "Epoch 74/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3305 - accuracy: 0.8627\n",
      "Epoch 75/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3302 - accuracy: 0.8644\n",
      "Epoch 76/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3305 - accuracy: 0.8626\n",
      "Epoch 77/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3308 - accuracy: 0.8648\n",
      "Epoch 78/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3297 - accuracy: 0.8636\n",
      "Epoch 79/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3301 - accuracy: 0.8637\n",
      "Epoch 80/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3301 - accuracy: 0.8624\n",
      "Epoch 81/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3302 - accuracy: 0.8633\n",
      "Epoch 82/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3304 - accuracy: 0.8643\n",
      "Epoch 83/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8649\n",
      "Epoch 84/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8624\n",
      "Epoch 85/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3303 - accuracy: 0.8627\n",
      "Epoch 86/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3298 - accuracy: 0.8629\n",
      "Epoch 87/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3297 - accuracy: 0.8635\n",
      "Epoch 88/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3296 - accuracy: 0.8635\n",
      "Epoch 89/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8649\n",
      "Epoch 90/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3300 - accuracy: 0.8636\n",
      "Epoch 91/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3297 - accuracy: 0.8629\n",
      "Epoch 92/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3300 - accuracy: 0.8630\n",
      "Epoch 93/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3296 - accuracy: 0.8644\n",
      "Epoch 94/100\n",
      "250/250 [==============================] - ETA: 0s - loss: 0.3293 - accuracy: 0.86 - 0s 1ms/step - loss: 0.3294 - accuracy: 0.8631\n",
      "Epoch 95/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3297 - accuracy: 0.8630\n",
      "Epoch 96/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3298 - accuracy: 0.8634\n",
      "Epoch 97/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3295 - accuracy: 0.8643\n",
      "Epoch 98/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3296 - accuracy: 0.8650\n",
      "Epoch 99/100\n",
      "250/250 [==============================] - 0s 2ms/step - loss: 0.3294 - accuracy: 0.8636\n",
      "Epoch 100/100\n",
      "250/250 [==============================] - 0s 1ms/step - loss: 0.3293 - accuracy: 0.8633\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2dd83064518>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ann.fit(X_train,y_train,batch_size = 32 ,epochs = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83801ed3",
   "metadata": {},
   "source": [
    "3. Making the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8ac2aa33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction is : False\n"
     ]
    }
   ],
   "source": [
    "### Predicting a new results with following features\n",
    "# 1.Geography = France\n",
    "# 2.credit score = 600\n",
    "# 3.Gender = Male\n",
    "# 4.Age = 40 \n",
    "# 5.Tenure = 3 years\n",
    "# 6.Balance = $60000\n",
    "# 7.Number of products = 2\n",
    "# 8.Active member = yes\n",
    "# 9.Customer with credit card = yes\n",
    "# 10.Estimated salary = $50000\n",
    "# he will exit or not??\n",
    "res = bool(ann.predict(scaler.transform([[1.0,0.0,0.0,600,1,40,3,60000,2,1,1,50000]])) > 0.5)\n",
    "print(f\"Prediction is : {res}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b79b0b",
   "metadata": {},
   "source": [
    "4.Predicting the test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f17320b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [0, 1],\n",
       "       [0, 1],\n",
       "       ...,\n",
       "       [1, 1],\n",
       "       [0, 1],\n",
       "       [0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = ann.predict(X_test)\n",
    "y_pred = (y_pred > 0.5)\n",
    "np.c_[y_pred,y_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1bfc039",
   "metadata": {},
   "source": [
    "5. Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "43f6c880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1562,   55],\n",
       "       [ 226,  157]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "matrix = confusion_matrix(y_test,y_pred)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4169e0",
   "metadata": {},
   "source": [
    "6. Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "98ffdbdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 85.95%\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test,y_pred)\n",
    "print(f\"Accuracy is {accuracy*100}%\")"
   ]
  }
 ],
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