customer_churn_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5f47c22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edd5b331",
   "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>Usage</th>\n",
       "      <th>Contract_Length</th>\n",
       "      <th>Monthly_Charges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.454012</td>\n",
       "      <td>3</td>\n",
       "      <td>99.372641</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>95.071431</td>\n",
       "      <td>2</td>\n",
       "      <td>88.113997</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.199394</td>\n",
       "      <td>3</td>\n",
       "      <td>36.680841</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59.865848</td>\n",
       "      <td>3</td>\n",
       "      <td>94.447617</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.601864</td>\n",
       "      <td>1</td>\n",
       "      <td>29.309312</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Usage  Contract_Length  Monthly_Charges  Churn\n",
       "0  37.454012                3        99.372641      1\n",
       "1  95.071431                2        88.113997      1\n",
       "2  73.199394                3        36.680841      1\n",
       "3  59.865848                3        94.447617      1\n",
       "4  15.601864                1        29.309312      1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Set a random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate synthetic data\n",
    "num_samples = 1000\n",
    "\n",
    "# Create features (e.g., usage patterns, contract details, etc.)\n",
    "usage = np.random.uniform(0, 100, num_samples)\n",
    "contract_length = np.random.choice([1, 2, 3], num_samples)\n",
    "monthly_charges = np.random.uniform(20, 100, num_samples)\n",
    "\n",
    "# Create a binary target variable (churn) - 0 for no churn, 1 for churn\n",
    "churn_probabilities = 1 / (1 + np.exp(-(0.1 * usage + 0.2 * contract_length + 0.3 * monthly_charges)))\n",
    "churn_labels = np.random.binomial(1, churn_probabilities)\n",
    "\n",
    "# Create a DataFrame to hold the synthetic data\n",
    "data = pd.DataFrame({\n",
    "    'Usage': usage,\n",
    "    'Contract_Length': contract_length,\n",
    "    'Monthly_Charges': monthly_charges,\n",
    "    'Churn': churn_labels\n",
    "})\n",
    "\n",
    "# Explore the synthetic data\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef1084b0",
   "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>Usage</th>\n",
       "      <th>Contract_Length</th>\n",
       "      <th>Monthly_Charges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>49.025655</td>\n",
       "      <td>1.975000</td>\n",
       "      <td>59.994199</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.213736</td>\n",
       "      <td>0.810575</td>\n",
       "      <td>23.112784</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.463202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.010775</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.597327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.730222</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.680738</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>60.127614</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74.431959</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.284913</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.971767</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>99.867801</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Usage  Contract_Length  Monthly_Charges   Churn\n",
       "count  1000.000000      1000.000000      1000.000000  1000.0\n",
       "mean     49.025655         1.975000        59.994199     1.0\n",
       "std      29.213736         0.810575        23.112784     0.0\n",
       "min       0.463202         1.000000        20.010775     1.0\n",
       "25%      23.597327         1.000000        40.730222     1.0\n",
       "50%      49.680738         2.000000        60.127614     1.0\n",
       "75%      74.431959         3.000000        80.284913     1.0\n",
       "max      99.971767         3.000000        99.867801     1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display basic statistics of the data\n",
    "data.describe()\n",
    "\n",
    "# Create visualizations to understand the data\n",
    "# Example: data['Churn'].value_counts().plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66212d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Churn'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c6b034b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check for missing values\n",
    "missing_values = data.isnull().sum()\n",
    "\n",
    "# Fill missing values with the mean\n",
    "data = data.fillna(data.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d7281cba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming 'Contract_Length' is a categorical variable\n",
    "data = pd.get_dummies(data, columns=['Contract_Length'], drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d2fb8ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "data[['Usage', 'Monthly_Charges']] = scaler.fit_transform(data[['Usage', 'Monthly_Charges']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f74cb7ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Churn', axis=1)  # Features\n",
    "y = data['Churn']  # Target variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3498246f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb44a122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the model \n",
    "clf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Train (fit) the model on the training data\n",
    "clf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "422e5d2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the testing data\n",
    "y_pred = clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3beb3101",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       200\n",
      "\n",
      "    accuracy                           1.00       200\n",
      "   macro avg       1.00      1.00      1.00       200\n",
      "weighted avg       1.00      1.00      1.00       200\n",
      "\n",
      "Confusion Matrix:\n",
      "[[200]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "\n",
    "# Generate a classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(\"Classification Report:\")\n",
    "print(report)\n",
    "\n",
    "# Create a confusion matrix\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6625378",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}