classifier.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><b>telco customer churn prediction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "telco customer leaving or stay in service forecasting "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4><b> Import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix , classification_report\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4><b> Data preparation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "set up dataframe (import from csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"customer.csv\")\n",
    "df.head()\n",
    "\n",
    "df = df.drop([\"customerID\"], axis=\"columns\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "unique value each column in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'gender' unique values:\n",
      "['Female' 'Male']\n",
      "Column 'SeniorCitizen' unique values:\n",
      "[0 1]\n",
      "Column 'Partner' unique values:\n",
      "['Yes' 'No']\n",
      "Column 'Dependents' unique values:\n",
      "['No' 'Yes']\n",
      "Column 'tenure' unique values:\n",
      "[ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27\n",
      "  5 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68\n",
      " 32 55 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26  0\n",
      " 39]\n",
      "Column 'PhoneService' unique values:\n",
      "['No' 'Yes']\n",
      "Column 'MultipleLines' unique values:\n",
      "['No phone service' 'No' 'Yes']\n",
      "Column 'InternetService' unique values:\n",
      "['DSL' 'Fiber optic' 'No']\n",
      "Column 'OnlineSecurity' unique values:\n",
      "['No' 'Yes' 'No internet service']\n",
      "Column 'OnlineBackup' unique values:\n",
      "['Yes' 'No' 'No internet service']\n",
      "Column 'DeviceProtection' unique values:\n",
      "['No' 'Yes' 'No internet service']\n",
      "Column 'TechSupport' unique values:\n",
      "['No' 'Yes' 'No internet service']\n",
      "Column 'StreamingTV' unique values:\n",
      "['No' 'Yes' 'No internet service']\n",
      "Column 'StreamingMovies' unique values:\n",
      "['No' 'Yes' 'No internet service']\n",
      "Column 'Contract' unique values:\n",
      "['Month-to-month' 'One year' 'Two year']\n",
      "Column 'PaperlessBilling' unique values:\n",
      "['Yes' 'No']\n",
      "Column 'PaymentMethod' unique values:\n",
      "['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "Column 'MonthlyCharges' unique values:\n",
      "[29.85 56.95 53.85 ... 63.1  44.2  78.7 ]\n",
      "Column 'TotalCharges' unique values:\n",
      "['29.85' '1889.5' '108.15' ... '346.45' '306.6' '6844.5']\n",
      "Column 'Churn' unique values:\n",
      "['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    unique_values = df[column].unique()\n",
    "    print(f\"Column '{column}' unique values:\")\n",
    "    print(unique_values)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "drop unused column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"TotalCharges\"], axis=\"columns\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[yes, no] and [male, female] column encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace('No internet service', 'No', inplace=True)\n",
    "df.replace('No phone service', 'No', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "yes_no_columns = ['Partner','Dependents','PhoneService','MultipleLines','OnlineSecurity','OnlineBackup',\n",
    "                  'DeviceProtection','TechSupport','StreamingTV','StreamingMovies','PaperlessBilling','Churn']\n",
    "\n",
    "for col in yes_no_columns:\n",
    "    df[col].replace({'Yes': 1,'No': 0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"gender\"].replace({'Female':1, 'Male':0}, inplace=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "encoding for category column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>34</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>56.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>42.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>70.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
       "0       1              0        1           0       1             0   \n",
       "1       0              0        0           0      34             1   \n",
       "2       0              0        0           0       2             1   \n",
       "3       0              0        0           0      45             0   \n",
       "4       1              0        0           0       2             1   \n",
       "\n",
       "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0              0                1               0             1   \n",
       "1              0                1               1             0   \n",
       "2              0                1               1             1   \n",
       "3              0                1               1             0   \n",
       "4              0                2               0             0   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 0            0            0                0         1   \n",
       "1                 1            0            0                0         2   \n",
       "2                 0            0            0                0         1   \n",
       "3                 1            1            0                0         2   \n",
       "4                 0            0            0                0         1   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  Churn  \n",
       "0                 1              1           29.85      0  \n",
       "1                 0              2           56.95      0  \n",
       "2                 1              2           53.85      1  \n",
       "3                 0              3           42.30      0  \n",
       "4                 1              1           70.70      1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"InternetService\"] = pd.factorize(df['InternetService'])[0] + 1\n",
    "df[\"Contract\"] = pd.factorize(df['Contract'])[0] + 1\n",
    "df[\"PaymentMethod\"] = pd.factorize(df['PaymentMethod'])[0] + 1\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "data splitting into training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7554, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train test split\n",
    "x = df.drop(\"Churn\", axis=\"columns\")\n",
    "y = df[\"Churn\"]\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_resampled, y_resampled = smote.fit_resample(x, y)\n",
    "\n",
    "xtr, xte, ytr, yte = train_test_split(X_resampled, y_resampled, test_size=.27, random_state=5)\n",
    "\n",
    "xtr.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4><b> Creating sequential model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "237/237 [==============================] - 4s 5ms/step - loss: 0.5361 - accuracy: 0.7421\n",
      "Epoch 2/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4746 - accuracy: 0.7808\n",
      "Epoch 3/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4418 - accuracy: 0.8002\n",
      "Epoch 4/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4418 - accuracy: 0.7964\n",
      "Epoch 5/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4350 - accuracy: 0.8032\n",
      "Epoch 6/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4307 - accuracy: 0.8047\n",
      "Epoch 7/50\n",
      "237/237 [==============================] - 1s 6ms/step - loss: 0.4280 - accuracy: 0.8029\n",
      "Epoch 8/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4309 - accuracy: 0.8035\n",
      "Epoch 9/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4203 - accuracy: 0.8087\n",
      "Epoch 10/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4199 - accuracy: 0.8088\n",
      "Epoch 11/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4148 - accuracy: 0.8123\n",
      "Epoch 12/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4285 - accuracy: 0.8054\n",
      "Epoch 13/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4223 - accuracy: 0.8090\n",
      "Epoch 14/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4200 - accuracy: 0.8094\n",
      "Epoch 15/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4152 - accuracy: 0.8123\n",
      "Epoch 16/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4143 - accuracy: 0.8104\n",
      "Epoch 17/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4149 - accuracy: 0.8070\n",
      "Epoch 18/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4166 - accuracy: 0.8046\n",
      "Epoch 19/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4112 - accuracy: 0.8102\n",
      "Epoch 20/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4104 - accuracy: 0.8148\n",
      "Epoch 21/50\n",
      "237/237 [==============================] - 1s 6ms/step - loss: 0.4078 - accuracy: 0.8148\n",
      "Epoch 22/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.4103 - accuracy: 0.8139\n",
      "Epoch 23/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4087 - accuracy: 0.8151\n",
      "Epoch 24/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4120 - accuracy: 0.8128\n",
      "Epoch 25/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4076 - accuracy: 0.8144\n",
      "Epoch 26/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4044 - accuracy: 0.8194\n",
      "Epoch 27/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4039 - accuracy: 0.8143\n",
      "Epoch 28/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4089 - accuracy: 0.8136\n",
      "Epoch 29/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4038 - accuracy: 0.8149\n",
      "Epoch 30/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4023 - accuracy: 0.8165\n",
      "Epoch 31/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4025 - accuracy: 0.8168\n",
      "Epoch 32/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4019 - accuracy: 0.8147\n",
      "Epoch 33/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.4018 - accuracy: 0.8159\n",
      "Epoch 34/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3952 - accuracy: 0.8197\n",
      "Epoch 35/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3991 - accuracy: 0.8176\n",
      "Epoch 36/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3998 - accuracy: 0.8174\n",
      "Epoch 37/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3984 - accuracy: 0.8193\n",
      "Epoch 38/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3936 - accuracy: 0.8218\n",
      "Epoch 39/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3941 - accuracy: 0.8208\n",
      "Epoch 40/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3940 - accuracy: 0.8188\n",
      "Epoch 41/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3922 - accuracy: 0.8210\n",
      "Epoch 42/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3954 - accuracy: 0.8189\n",
      "Epoch 43/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3900 - accuracy: 0.8212\n",
      "Epoch 44/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3873 - accuracy: 0.8217\n",
      "Epoch 45/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.3930 - accuracy: 0.8171\n",
      "Epoch 46/50\n",
      "237/237 [==============================] - 1s 5ms/step - loss: 0.3871 - accuracy: 0.8247\n",
      "Epoch 47/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3856 - accuracy: 0.8192\n",
      "Epoch 48/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3884 - accuracy: 0.8210\n",
      "Epoch 49/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3859 - accuracy: 0.8223\n",
      "Epoch 50/50\n",
      "237/237 [==============================] - 1s 4ms/step - loss: 0.3861 - accuracy: 0.8216\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b03866a1f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(128, input_shape=(18,), activation=\"relu\"),\n",
    "    keras.layers.Dense(64, activation=\"relu\"),\n",
    "    keras.layers.Dense(32, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "\n",
    "model.compile(optimizer=\"adam\",\n",
    "              loss=\"binary_crossentropy\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(xtr, ytr, epochs=50)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4><b> Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88/88 [==============================] - 1s 2ms/step - loss: 0.4022 - accuracy: 0.8175\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.80      0.81      1388\n",
      "           1       0.81      0.84      0.82      1406\n",
      "\n",
      "    accuracy                           0.82      2794\n",
      "   macro avg       0.82      0.82      0.82      2794\n",
      "weighted avg       0.82      0.82      0.82      2794\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
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   "source": [
    "model.evaluate(xte, yte)\n",
    "\n",
    "yp = model.predict(xte)\n",
    "\n",
    "predict = []\n",
    "for ele in yp:\n",
    "    if ele > 0.5:\n",
    "        predict.append(1)\n",
    "    else:\n",
    "        predict.append(0)\n",
    "\n",
    "print(classification_report(yte, predict))\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(confusion_matrix(yte, predict), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"annclass.h5\")"
   ]
  }
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