Churn model prediction using ann.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e557882",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5ac2367",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RowNumber</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>Surname</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Gender</th>\n",
       "      <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Churn_Modelling.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd290764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 14)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e1eab3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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      ],
      "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,3:13]\n",
    "y=df.iloc[:,13]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "224b9d8a",
   "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>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Male\n",
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "...    ...\n",
       "9995     1\n",
       "9996     1\n",
       "9997     0\n",
       "9998     1\n",
       "9999     0\n",
       "\n",
       "[10000 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#categorical encoding\n",
    "#it crtes colums for each category of the coloumns,eg:\n",
    "geo=pd.get_dummies(x['Geography'],drop_first=True,dtype=int)#\n",
    "gen=pd.get_dummies(x['Gender'],drop_first=True,dtype=int)\n",
    "gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bbfbabdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.concat([x,geo,gen],axis=1)\n",
    "x=x.drop(['Geography','Gender'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "77a4b321",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a2f9e4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar=StandardScaler()\n",
    "x_train=scalar.fit_transform(x_train)\n",
    "x_test=scalar.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6d8e3027",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b610baf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2b67dab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cls=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fb8c0780",
   "metadata": {},
   "outputs": [],
   "source": [
    "cls.add(Dense(6,kernel_initializer='he_uniform',activation='relu',input_dim=11))\n",
    "#cls.add(Dense(6,kernel_initializer='he_uniform',activation='relu',input_dim=11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5cf08836",
   "metadata": {},
   "outputs": [],
   "source": [
    "cls.add(Dense(6,kernel_initializer='he_uniform',activation='relu'))\n",
    "cls.add(Dense(1,kernel_initializer='glorot_uniform',activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e31022e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cls.compile(optimizer='Adamax',loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f247a3b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.9598 - accuracy: 7.4641e-04 - val_loss: 1.8642 - val_accuracy: 0.0027\n",
      "Epoch 2/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.9434 - accuracy: 0.0050 - val_loss: 1.8469 - val_accuracy: 0.0102\n",
      "Epoch 3/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.9257 - accuracy: 0.0136 - val_loss: 1.8283 - val_accuracy: 0.0216\n",
      "Epoch 4/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.9121 - accuracy: 0.0319 - val_loss: 1.8118 - val_accuracy: 0.0435\n",
      "Epoch 5/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8975 - accuracy: 0.0629 - val_loss: 1.8007 - val_accuracy: 0.0992\n",
      "Epoch 6/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8898 - accuracy: 0.1463 - val_loss: 1.7951 - val_accuracy: 0.1738\n",
      "Epoch 7/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8855 - accuracy: 0.2021 - val_loss: 1.7934 - val_accuracy: 0.2207\n",
      "Epoch 8/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8829 - accuracy: 0.2334 - val_loss: 1.7911 - val_accuracy: 0.2491\n",
      "Epoch 9/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8806 - accuracy: 0.2530 - val_loss: 1.7896 - val_accuracy: 0.2446\n",
      "Epoch 10/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8794 - accuracy: 0.2515 - val_loss: 1.7888 - val_accuracy: 0.2484\n",
      "Epoch 11/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8786 - accuracy: 0.2489 - val_loss: 1.7881 - val_accuracy: 0.2507\n",
      "Epoch 12/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8777 - accuracy: 0.2555 - val_loss: 1.7870 - val_accuracy: 0.2484\n",
      "Epoch 13/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8766 - accuracy: 0.2558 - val_loss: 1.7858 - val_accuracy: 0.2620\n",
      "Epoch 14/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8751 - accuracy: 0.2668 - val_loss: 1.7840 - val_accuracy: 0.2654\n",
      "Epoch 15/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8730 - accuracy: 0.2780 - val_loss: 1.7818 - val_accuracy: 0.2802\n",
      "Epoch 16/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8705 - accuracy: 0.2913 - val_loss: 1.7786 - val_accuracy: 0.3006\n",
      "Epoch 17/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8685 - accuracy: 0.3219 - val_loss: 1.7753 - val_accuracy: 0.3321\n",
      "Epoch 18/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8654 - accuracy: 0.3224 - val_loss: 1.7744 - val_accuracy: 0.3237\n",
      "Epoch 19/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8638 - accuracy: 0.3116 - val_loss: 1.7723 - val_accuracy: 0.2991\n",
      "Epoch 20/20\n",
      "536/536 [==============================] - 1s 2ms/step - loss: 1.8627 - accuracy: 0.2956 - val_loss: 1.7710 - val_accuracy: 0.3116\n"
     ]
    }
   ],
   "source": [
    "model_history=cls.fit(x_train,y_train,validation_split=0.33,batch_size=10,epochs=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "96ac5bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(model_history.history.keys())\n",
    "plt.plot(model_history.history['accuracy'])\n",
    "plt.plot(model_history.history['val_accuracy'])\n",
    "plt.title('model accuaracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train','test'],loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ded7298c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(model_history.history.keys())\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ab6a14af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False],\n",
       "       [False, False, False, False, False, False]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=cls.predict(x_test)\n",
    "y_pred=(y_pred>0.5)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f948bd1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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