1_3_2_Churn_Prediction.ipynb

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        "# **1.3.2.  Churn Prediction Project Overview**\n",
        "\n",
        "## **🔍 Project Objective**\n",
        "The objective of this project is to predict customer churn in the telecom industry using a dataset that includes various customer attributes. We employ both Logistic Regression and Random Forest Classifier models to achieve this goal. Our aim is to build accurate predictive models to help the company retain customers and reduce churn rates.\n",
        "\n",
        "## **📁 Dataset**\n",
        "The dataset used for this project is the \"Telco Customer Churn\" dataset. It contains customer information such as tenure, monthly charges, payment method, and churn status. The dataset file is located at `/content/WA_Fn-UseC_-Telco-Customer-Churn.csv`.\n",
        "\n",
        "## **📊 Exploratory Data Analysis (EDA)**\n",
        "- **Distribution of Churn:** Visualized using count plots and pie charts to understand the proportion of churned vs. non-churned customers.\n",
        "- **Distribution of Tenure:** Examined with histograms to see the distribution of customer tenure.\n",
        "- **Monthly Charges vs. Churn:** Analyzed using box plots to determine the relationship between monthly charges and churn status.\n",
        "- **Payment Method Distribution:** Visualized using count plots to understand the payment methods used by customers.\n",
        "- **Contract Type vs. Churn:** Explored using count plots to examine the relationship between contract type and churn.\n",
        "- **Tenure and Monthly Charges by Churn:** Visualized with violin plots to understand how tenure and monthly charges vary with churn.\n",
        "- **Monthly Charges Over Tenure:** Analyzed using line plots to observe trends in monthly charges over customer tenure.\n",
        "- **Distribution of Total Charges:** Examined with histograms to understand the distribution of total charges.\n",
        "- **Tenure by Gender:** Explored using box plots to analyze tenure differences across genders.\n",
        "- **Categorical Feature Analysis:** Count plots for various categorical features to observe their distribution and relationship with churn.\n",
        "- **Correlation Heatmap:** Created to visualize correlations between numerical features.\n",
        "\n",
        "## **🔧 Data Preprocessing**\n",
        "- **Handling Missing Values:** Filled missing values in the 'TotalCharges' column with the median value and converted it to numeric format.\n",
        "- **Encoding Categorical Features:** Applied label encoding to convert categorical features into numerical format.\n",
        "- **Feature Selection:** Dropped the 'customerID' column as it is not required for prediction.\n",
        "- **Feature Scaling:** Standardized features using `StandardScaler` for better model performance.\n",
        "\n",
        "## **🤖 Model Training and Evaluation**\n",
        "- **Logistic Regression:**\n",
        "  - Trained a Logistic Regression model on the preprocessed data.\n",
        "  - Evaluated performance using classification reports, confusion matrices, and ROC curves.\n",
        "- **Random Forest Classifier:**\n",
        "  - Trained a Random Forest Classifier model on the preprocessed data.\n",
        "  - Evaluated performance using classification reports, confusion matrices, and ROC curves.\n",
        "- **ROC Curve Analysis:** Plotted ROC curves for both Logistic Regression and Random Forest models to compare their performance in distinguishing between churn and non-churn customers.\n",
        "\n",
        "## **📈 Results**\n",
        "- **Logistic Regression:**\n",
        "  - **Classification Report:** Provided precision, recall, and F1-score metrics.\n",
        "  - **Confusion Matrix:** Showed the count of true positive, true negative, false positive, and false negative predictions.\n",
        "- **Random Forest Classifier:**\n",
        "  - **Classification Report:** Provided precision, recall, and F1-score metrics.\n",
        "  - **Confusion Matrix:** Showed the count of true positive, true negative, false positive, and false negative predictions.\n",
        "- **ROC Curve:** Compared the performance of both models in terms of AUC (Area Under the Curve).\n",
        "\n",
        "## **🔮 Conclusion**\n",
        "This project effectively demonstrates the use of machine learning models to predict customer churn. The exploratory data analysis provides valuable insights into the factors influencing churn, while the model evaluations offer a comprehensive understanding of the predictive performance of Logistic Regression and Random Forest models. The results can be leveraged to implement strategies aimed at reducing customer churn and improving overall customer retention.\n",
        "\n",
        "---\n",
        "\n",
        "For further details or specific questions, feel free to reach out."
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      "metadata": {
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ewAWDPApxPK6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ev9X0CgZxPg6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing Libraries\n",
        "\n",
        "- **`pandas`**: Essential for data manipulation and analysis, particularly for handling data in DataFrame format.\n",
        "- **`numpy`**: Provides support for numerical operations and handling arrays efficiently.\n",
        "- **`matplotlib.pyplot`** and **`seaborn`**: Crucial for creating a variety of visualizations to explore and present data insights.\n",
        "- **`train_test_split`**: Used to divide the dataset into training and testing sets for model evaluation.\n",
        "- **`LogisticRegression`** and **`RandomForestClassifier`**: Algorithms used for classification tasks to predict outcomes based on input features.\n",
        "- **`classification_report`**, **`confusion_matrix`**, **`roc_curve`**, **`auc`**: Metrics for assessing model performance, including accuracy, precision, recall, F1 score, and ROC curve evaluation.\n",
        "- **`StandardScaler`**: Standardizes features by removing the mean and scaling to unit variance, which can improve model performance.\n",
        "- **`LabelEncoder`**: Transforms categorical labels into numeric format to facilitate model training."
      ],
      "metadata": {
        "id": "kL4BHfIxxP56"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing necessary libraries"
      ],
      "metadata": {
        "id": "_8D3knn8mutM"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # Library for data manipulation and analysis"
      ],
      "metadata": {
        "id": "vILZNkuFspGl"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np  # Library for numerical computations"
      ],
      "metadata": {
        "id": "ImWZpCO2spJV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # Library for creating static, interactive, and animated visualizations"
      ],
      "metadata": {
        "id": "76ydipd_spL9"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # Library for statistical data visualization"
      ],
      "metadata": {
        "id": "fegEmphIspOg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # Function to split data into training and testing sets"
      ],
      "metadata": {
        "id": "U8rzPWngspRA"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression  # Logistic Regression model"
      ],
      "metadata": {
        "id": "YQtcTlOaspTY"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier  # Random Forest model"
      ],
      "metadata": {
        "id": "gJzA-PeYspVb"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc  # Metrics for model evaluation"
      ],
      "metadata": {
        "id": "JguhmFgmspaP"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, LabelEncoder  # StandardScaler for feature scaling and LabelEncoder for encoding categorical variables\n"
      ],
      "metadata": {
        "id": "hQbcoTFgspeu"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings  # Import the warnings library to handle warning messages"
      ],
      "metadata": {
        "id": "IXrFitF6yos1"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "warnings.filterwarnings('ignore')  # Suppress all warnings to clean up the output"
      ],
      "metadata": {
        "id": "Ucbl6YOOyowT"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4emaFJPJtM14"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z0Tum8xBtM5C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9HvlLfhktM8H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the Dataset\n",
        "\n",
        "- **Loading the dataset**: Imports the dataset from a CSV file into a DataFrame, which is the starting point for data analysis.\n",
        "- **Displaying the first 5 rows**: Provides an initial look at the data structure and content, which is essential for understanding the dataset.\n",
        "- **Dataset Info**: Reveals information about the data types of each column and identifies columns with missing values.\n",
        "- **Missing values**: Identifies columns with missing values that need to be addressed to ensure data quality.\n",
        "- **Statistical Summary**: Offers a summary of numerical columns, including measures of central tendency and spread.\n",
        "- **Unique values**: Shows the number of unique values in categorical columns, helping to understand the diversity of categorical data.\n",
        "- **Shape of the dataset**: Indicates the dimensions of the dataset, including the number of rows and columns."
      ],
      "metadata": {
        "id": "kYi6RKbFxV6r"
      }
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    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "gkjZYneGtNLO"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/WA_Fn-UseC_-Telco-Customer-Churn.csv'  # Path to the dataset file"
      ],
      "metadata": {
        "id": "ZEtEGTQqsphl"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "churn_data = pd.read_csv(file_path)  # Load dataset into a pandas DataFrame"
      ],
      "metadata": {
        "id": "Xg-vcd6GspkN"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eUWt12jespmL"
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      "cell_type": "code",
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      "metadata": {
        "id": "jRQEAWe5spoh"
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      "cell_type": "code",
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      "metadata": {
        "id": "DbGwPdyNsptB"
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      "cell_type": "code",
      "source": [
        "# Display the first 5 rows"
      ],
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        "id": "w57vc9i7spvH"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 5 rows of the dataset:\")  # Print statement for clarity\n",
        "print(churn_data.head())  # Display the first 5 rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yUGkOpL6spzw",
        "outputId": "4401f473-be84-48ad-dfbf-7ded3fe7e832"
      },
      "execution_count": 20,
      "outputs": [
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          "output_type": "stream",
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          "text": [
            "First 5 rows of the dataset:\n",
            "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
            "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
            "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
            "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
            "3  7795-CFOCW    Male              0      No         No      45           No   \n",
            "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
            "\n",
            "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
            "0  No phone service             DSL             No  ...               No   \n",
            "1                No             DSL            Yes  ...              Yes   \n",
            "2                No             DSL            Yes  ...               No   \n",
            "3  No phone service             DSL            Yes  ...              Yes   \n",
            "4                No     Fiber optic             No  ...               No   \n",
            "\n",
            "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
            "0          No          No              No  Month-to-month              Yes   \n",
            "1          No          No              No        One year               No   \n",
            "2          No          No              No  Month-to-month              Yes   \n",
            "3         Yes          No              No        One year               No   \n",
            "4          No          No              No  Month-to-month              Yes   \n",
            "\n",
            "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
            "0           Electronic check          29.85         29.85    No  \n",
            "1               Mailed check          56.95        1889.5    No  \n",
            "2               Mailed check          53.85        108.15   Yes  \n",
            "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
            "4           Electronic check          70.70        151.65   Yes  \n",
            "\n",
            "[5 rows x 21 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bQo9oVbfsp4N"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WfCI6KIGsp60"
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      "cell_type": "code",
      "source": [
        "# Display dataset info"
      ],
      "metadata": {
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      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nDataset Info:\")  # Print statement for clarity\n",
        "print(churn_data.info())  # Display dataset information including data types and non-null counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9DPvdXdSsqDg",
        "outputId": "daf9289d-fdd5-419a-f28a-958d5fa14805"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7043 entries, 0 to 7042\n",
            "Data columns (total 21 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   customerID        7043 non-null   object \n",
            " 1   gender            7043 non-null   object \n",
            " 2   SeniorCitizen     7043 non-null   int64  \n",
            " 3   Partner           7043 non-null   object \n",
            " 4   Dependents        7043 non-null   object \n",
            " 5   tenure            7043 non-null   int64  \n",
            " 6   PhoneService      7043 non-null   object \n",
            " 7   MultipleLines     7043 non-null   object \n",
            " 8   InternetService   7043 non-null   object \n",
            " 9   OnlineSecurity    7043 non-null   object \n",
            " 10  OnlineBackup      7043 non-null   object \n",
            " 11  DeviceProtection  7043 non-null   object \n",
            " 12  TechSupport       7043 non-null   object \n",
            " 13  StreamingTV       7043 non-null   object \n",
            " 14  StreamingMovies   7043 non-null   object \n",
            " 15  Contract          7043 non-null   object \n",
            " 16  PaperlessBilling  7043 non-null   object \n",
            " 17  PaymentMethod     7043 non-null   object \n",
            " 18  MonthlyCharges    7043 non-null   float64\n",
            " 19  TotalCharges      7043 non-null   object \n",
            " 20  Churn             7043 non-null   object \n",
            "dtypes: float64(1), int64(2), object(18)\n",
            "memory usage: 1.1+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2zrkdGZIsqFp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U53Vi1iWsqH1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "NO1fbRQusqKb"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nMissing values in each column:\")  # Print statement for clarity\n",
        "print(churn_data.isnull().sum())  # Display the count of missing values in each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9K3aoWxXsqMd",
        "outputId": "fdc9d5f4-535d-489b-bdee-1b2c6c88e123"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in each column:\n",
            "customerID          0\n",
            "gender              0\n",
            "SeniorCitizen       0\n",
            "Partner             0\n",
            "Dependents          0\n",
            "tenure              0\n",
            "PhoneService        0\n",
            "MultipleLines       0\n",
            "InternetService     0\n",
            "OnlineSecurity      0\n",
            "OnlineBackup        0\n",
            "DeviceProtection    0\n",
            "TechSupport         0\n",
            "StreamingTV         0\n",
            "StreamingMovies     0\n",
            "Contract            0\n",
            "PaperlessBilling    0\n",
            "PaymentMethod       0\n",
            "MonthlyCharges      0\n",
            "TotalCharges        0\n",
            "Churn               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JkotokTbsqOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E-XQgh7MsqQm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display statistical summary"
      ],
      "metadata": {
        "id": "KXM65phDsqSx"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nStatistical Summary:\")  # Print statement for clarity\n",
        "print(churn_data.describe())  # Display summary statistics for numerical columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XHsuCPUvsqU7",
        "outputId": "da283ed9-0943-40e0-f909-aa4c2793997c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Statistical Summary:\n",
            "       SeniorCitizen       tenure  MonthlyCharges\n",
            "count    7043.000000  7043.000000     7043.000000\n",
            "mean        0.162147    32.371149       64.761692\n",
            "std         0.368612    24.559481       30.090047\n",
            "min         0.000000     0.000000       18.250000\n",
            "25%         0.000000     9.000000       35.500000\n",
            "50%         0.000000    29.000000       70.350000\n",
            "75%         0.000000    55.000000       89.850000\n",
            "max         1.000000    72.000000      118.750000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9zdD23jOl2-l"
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sN6yLeZ7l3B-"
      },
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    },
    {
      "cell_type": "code",
      "source": [
        "# Unique values in each column"
      ],
      "metadata": {
        "id": "cAmXEFGsl3FU"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nUnique values in each column:\")  # Print statement for clarity\n",
        "for column in churn_data.columns:  # Iterate over each column in the dataset\n",
        "    print(f\"{column}: {churn_data[column].nunique()} unique values\")  # Print the number of unique values in each column\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LBYpEcjYltpD",
        "outputId": "fabb65cb-aa14-4fb3-f065-6c66d215363d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Unique values in each column:\n",
            "customerID: 7043 unique values\n",
            "gender: 2 unique values\n",
            "SeniorCitizen: 2 unique values\n",
            "Partner: 2 unique values\n",
            "Dependents: 2 unique values\n",
            "tenure: 73 unique values\n",
            "PhoneService: 2 unique values\n",
            "MultipleLines: 3 unique values\n",
            "InternetService: 3 unique values\n",
            "OnlineSecurity: 3 unique values\n",
            "OnlineBackup: 3 unique values\n",
            "DeviceProtection: 3 unique values\n",
            "TechSupport: 3 unique values\n",
            "StreamingTV: 3 unique values\n",
            "StreamingMovies: 3 unique values\n",
            "Contract: 3 unique values\n",
            "PaperlessBilling: 2 unique values\n",
            "PaymentMethod: 4 unique values\n",
            "MonthlyCharges: 1585 unique values\n",
            "TotalCharges: 6531 unique values\n",
            "Churn: 2 unique values\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zes5eC_PltsL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PlyNaxWvltvT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape of the dataset"
      ],
      "metadata": {
        "id": "-FHCj8dRlt0D"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nShape of the dataset:\")  # Print statement for clarity\n",
        "print(churn_data.shape)  # Display the shape (number of rows and columns) of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_Kb2Eurlt27",
        "outputId": "a9cfb29d-c0ea-4885-f90d-5fb821e7d9af"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Shape of the dataset:\n",
            "(7043, 21)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "F7TLrDAVlt5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NYzEiseHlt94"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X9GGMLBUylCI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis (EDA)\n",
        "\n",
        "- **Distribution of Churn**: Visualizes the count of churn versus non-churn instances to understand the class distribution.\n",
        "- **Pie chart for Churn distribution**: Provides a proportional view of the churn distribution within the dataset.\n",
        "- **Distribution of tenure**: Illustrates the distribution and density of the tenure feature to understand customer tenure patterns.\n",
        "- **Monthly Charges vs. Churn**: Analyzes the relationship between monthly charges and churn using a box plot to see if there is a significant difference in charges between churn and non-churn customers.\n",
        "- **Payment Method distribution**: Displays the frequency of different payment methods to analyze customer preferences."
      ],
      "metadata": {
        "id": "hxekCJ3xxbZV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exploratory Data Analysis (EDA)"
      ],
      "metadata": {
        "id": "Kh3fwwmfluC3"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of Churn"
      ],
      "metadata": {
        "id": "QHd813GaluIO"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 4))  # Set the figure size for the plot\n",
        "sns.countplot(data=churn_data, x='Churn', palette='viridis')  # Create a count plot for the 'Churn' column\n",
        "plt.title('Distribution of Churn')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "jjcD2iaOluK7",
        "outputId": "666cc3f4-68f1-4a7f-9c45-0dede886a152"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0EhDjxuJt__Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MDytDPhIuADB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie chart for Churn distribution"
      ],
      "metadata": {
        "id": "bl7eJsTsuAGw"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 6))  # Set the figure size for the plot\n",
        "churn_counts = churn_data['Churn'].value_counts()  # Count the occurrences of each value in the 'Churn' column\n",
        "plt.pie(churn_counts, labels=churn_counts.index, autopct='%1.1f%%', colors=sns.color_palette('viridis', 2))  # Create a pie chart for churn distribution\n",
        "plt.title('Churn Distribution Pie Chart')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "4Jm7hzbauAK6",
        "outputId": "4d771307-e740-4c46-81d5-c54a1dfba7c5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g_ojeFkjluNe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zOmq7Kz4luSL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of tenure"
      ],
      "metadata": {
        "id": "2zNMNSGBluW7"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set the figure size for the plot\n",
        "sns.histplot(data=churn_data, x='tenure', bins=30, kde=True)  # Create a histogram for the 'tenure' column with a kernel density estimate\n",
        "plt.title('Distribution of Tenure')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "sQlVJauAluZj",
        "outputId": "91724b19-e857-4dad-fe9f-d88ac44bc5bc"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IrgeifT8lucL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yAaZXzKeluer"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Monthly Charges vs. Churn"
      ],
      "metadata": {
        "id": "vdawpXYclug-"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set the figure size for the plot\n",
        "sns.boxplot(data=churn_data, x='Churn', y='MonthlyCharges', palette='viridis')  # Create a box plot for 'MonthlyCharges' vs. 'Churn'\n",
        "plt.title('Monthly Charges vs. Churn')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Sqk0_yX_lulr",
        "outputId": "707a5b56-96c0-4985-c633-71b480a958e3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zOTrGo41luoU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5OKC8XPEluq3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Payment Method distribution"
      ],
      "metadata": {
        "id": "fLbo0LyplutO"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "sns.countplot(data=churn_data, y='PaymentMethod', palette='viridis', order=churn_data['PaymentMethod'].value_counts().index)  # Create a count plot for 'PaymentMethod' column\n",
        "plt.title('Payment Method Distribution')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "rNbsVxkuluvg",
        "outputId": "d56766a0-6ea6-4ac0-d65b-089d5e26b0cc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gq7Y4QhduSVO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fw43qf_FuSYe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Contract type vs. Churn"
      ],
      "metadata": {
        "id": "V-9ioHBIuSbW"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set the figure size for the plot\n",
        "sns.countplot(data=churn_data, x='Contract', hue='Churn', palette='viridis')  # Create a count plot for 'Contract' column with 'Churn' as hue\n",
        "plt.title('Contract Type vs. Churn')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "SmImkt3ruSeC",
        "outputId": "0eb7b2fc-b65d-4774-a9ff-993f98ae1f78"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4HZDN_GouSgo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jh4MqEIduSkC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Violin plot for Tenure and Monthly Charges by Churn"
      ],
      "metadata": {
        "id": "9iwg9LOruSn1"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "sns.violinplot(data=churn_data, x='Churn', y='tenure', palette='viridis')  # Create a violin plot for 'Tenure' by 'Churn'\n",
        "plt.title('Tenure by Churn')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "p3vrpXbYua2_",
        "outputId": "60cd28e9-6222-4ceb-eb5a-4f274b36a4d4"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "stWV7VFIua5_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FCcwzN9Kua9X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "sns.violinplot(data=churn_data, x='Churn', y='MonthlyCharges', palette='viridis')  # Create a violin plot for 'MonthlyCharges' by 'Churn'\n",
        "plt.title('Monthly Charges by Churn')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "xiO5NLl6ua_s",
        "outputId": "0a1edfa8-0d93-465a-a1f0-022ba9a12543"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gHLgo6BRubCn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jjVqvtTvubGW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Line plot for Monthly Charges over Tenure"
      ],
      "metadata": {
        "id": "COCZSTCfubJz"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "sns.lineplot(data=churn_data, x='tenure', y='MonthlyCharges', hue='Churn', palette='viridis')  # Create a line plot for 'MonthlyCharges' over 'Tenure'\n",
        "plt.title('Monthly Charges Over Tenure')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "pcaTnvKMuSqo",
        "outputId": "89eae1c7-9518-4fd5-d8d0-f81bbee20f55"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lr9uJ99ZunC2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "X1vhxRwXunGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plot for Tenure by Gender"
      ],
      "metadata": {
        "id": "9_M_BDS1utv4"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set the figure size for the plot\n",
        "sns.boxplot(data=churn_data, x='gender', y='tenure', palette='viridis')  # Create a box plot for 'Tenure' by 'Gender'\n",
        "plt.title('Tenure by Gender')  # Set the title of the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4hRbM1Yvutza",
        "outputId": "37d5db5a-26bc-4331-f273-2612f3f2e9b8"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RXE6Jnlgut3G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nzxe97MNut6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Box plots for categorical features"
      ],
      "metadata": {
        "id": "i856FTECut9n"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 10))  # Set the figure size for the plot\n",
        "categorical_cols = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']  # List of categorical columns to plot\n",
        "for i, col in enumerate(categorical_cols):  # Iterate over each categorical column and its index\n",
        "    plt.subplot(4, 4, i+1)  # Create a subplot in a 4x4 grid\n",
        "    sns.countplot(data=churn_data, x=col, hue='Churn')  # Create a count plot for the categorical column with 'Churn' as hue\n",
        "    plt.title(f'{col} Count by Churn')  # Set the title of the plot\n",
        "    plt.xticks(rotation=90)  # Rotate x-axis labels for better readability\n",
        "plt.tight_layout()  # Adjust subplot parameters to fit the figure area\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D0xTSDuiunZv",
        "outputId": "2775c4b2-3e4a-46cb-fef9-db1d3af2a72b"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 16 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HYhRLzZlz1tA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jhSKCwRCu4eX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6a0Sn0Ccu4hp"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preprocessing\n",
        "\n",
        "- **Handling missing values**: Address missing values in the dataset to ensure the integrity of the analysis and model performance.\n",
        "- **Encoding categorical variables**: Convert categorical variables into numerical format using techniques like one-hot encoding or label encoding.\n",
        "- **Feature scaling**: Standardize or normalize features to ensure that all features contribute equally to model training.\n",
        "- **Splitting the dataset**: Divide the data into training and testing sets to evaluate model performance."
      ],
      "metadata": {
        "id": "sZ0rryHpxfbb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "uaAUhir5u-Wj"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'TotalCharges' to numeric"
      ],
      "metadata": {
        "id": "y5ox-3h4u-aQ"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "churn_data['TotalCharges'] = pd.to_numeric(churn_data['TotalCharges'], errors='coerce')  # Convert 'TotalCharges' column to numeric, coercing errors\n"
      ],
      "metadata": {
        "id": "Gp0Q2ZX8u-co"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gT1eEBGHu-f0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing values in 'TotalCharges' with median"
      ],
      "metadata": {
        "id": "XOvLBwzfu-iX"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "churn_data['TotalCharges'].fillna(churn_data['TotalCharges'].median(), inplace=True)  # Fill missing values in 'TotalCharges' with the median of the column\n"
      ],
      "metadata": {
        "id": "-HXFgqjXu4rm"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P6xS0x3Du4un"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop 'customerID' column as it is not needed for prediction"
      ],
      "metadata": {
        "id": "Zj94Ms5du40K"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "churn_data.drop('customerID', axis=1, inplace=True)  # Drop the 'customerID' column from the DataFrame"
      ],
      "metadata": {
        "id": "oEFboD05vO4_"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IiXQCrlTvO9H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode categorical features"
      ],
      "metadata": {
        "id": "Fs1R-QBjvPAa"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoders = {}  # Dictionary to hold label encoders for each categorical feature"
      ],
      "metadata": {
        "id": "tKRvwHrfvPEi"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in churn_data.select_dtypes(include=['object']).columns:  # Iterate over categorical columns\n",
        "    le = LabelEncoder()  # Create a LabelEncoder instance\n",
        "    churn_data[column] = le.fit_transform(churn_data[column])  # Encode the column with integer values\n",
        "    label_encoders[column] = le  # Store the label encoder"
      ],
      "metadata": {
        "id": "fjZFwi3BvPPo"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JVR7rGpovanq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VDDrt-VDvaq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define features and target variable"
      ],
      "metadata": {
        "id": "VE39vvT1vatz"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = churn_data.drop('Churn', axis=1)  # Features (excluding the target variable 'Churn')"
      ],
      "metadata": {
        "id": "ofp_wpV7vawY"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = churn_data['Churn']  # Target variable"
      ],
      "metadata": {
        "id": "liDaQ4Dyva0R"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ksAi_4dhviEw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets"
      ],
      "metadata": {
        "id": "zh4GbAIRviIK"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)  # Split data with 70% training and 30% testing\n"
      ],
      "metadata": {
        "id": "C1s0E5fRviLc"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z9x6LONEviQL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature scaling"
      ],
      "metadata": {
        "id": "ZhIMsMY-viUQ"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Create a StandardScaler instance"
      ],
      "metadata": {
        "id": "YR7IrJMQva38"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = scaler.fit_transform(X_train)  # Fit and transform the training features"
      ],
      "metadata": {
        "id": "lmnEe6J9u47p"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = scaler.transform(X_test)  # Transform the testing features"
      ],
      "metadata": {
        "id": "RYAaQ_zdundG"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OX2UIA_Bv0A9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cfHzOEQyv0GK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6n3zxES5v0K0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Training and Evaluation\n",
        "\n",
        "- **Model training**: Fit models such as Logistic Regression or Random Forest on the training dataset to learn patterns and make predictions.\n",
        "- **Model evaluation**: Assess model performance using metrics such as accuracy, precision, recall, F1 score, and ROC curve to ensure the model's effectiveness.\n",
        "- **Hyperparameter tuning**: Optimize model parameters to enhance performance.\n",
        "- **Confusion matrix**: Provides a detailed breakdown of the model's predictions versus actual values, highlighting true positives, true negatives, false positives, and false negatives.\n",
        "- **Classification report**: Summarizes the main classification metrics, including precision, recall, and F1 score.\n",
        "- **ROC curve and AUC**: Evaluates the model's ability to distinguish between classes by plotting the ROC curve and calculating the AUC."
      ],
      "metadata": {
        "id": "vsiR4htPxxWa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Training and Evaluation"
      ],
      "metadata": {
        "id": "k2v20lZhv0Op"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "CmIEZ4JWv0Tw"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logreg = LogisticRegression(max_iter=1000)  # Create a LogisticRegression instance with a maximum of 1000 iterations\n"
      ],
      "metadata": {
        "id": "YJg3lRHiv0WQ"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "logreg.fit(X_train, y_train)  # Train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "XmWXymjPv0Yv",
        "outputId": "9bd3e9c4-4a31-4c4d-cecf-8164a642d337"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "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>LogisticRegression(max_iter=1000)</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_FB1bMnPv79p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SW_tq0O2v8Bq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict and evaluate Logistic Regression"
      ],
      "metadata": {
        "id": "-ue0Cknev8E8"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_logreg = logreg.predict(X_test)  # Predict the target variable on the test set"
      ],
      "metadata": {
        "id": "hGt6LmdOv8H-"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nLogistic Regression Classification Report:\")  # Print statement for clarity\n",
        "print(classification_report(y_test, y_pred_logreg))  # Display classification report for Logistic Regression"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmVUarGAv8Kt",
        "outputId": "49cc86ed-e857-4b92-ffb5-69c4c3b0bf32"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Logistic Regression Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.90      0.87      1539\n",
            "           1       0.68      0.57      0.62       574\n",
            "\n",
            "    accuracy                           0.81      2113\n",
            "   macro avg       0.76      0.74      0.75      2113\n",
            "weighted avg       0.80      0.81      0.81      2113\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rYrUpzt1v8Nd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nLogistic Regression Confusion Matrix:\")  # Print statement for clarity\n",
        "print(confusion_matrix(y_test, y_pred_logreg))  # Display confusion matrix for Logistic Regression"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxuaUa54v8R2",
        "outputId": "7599cfa9-6543-4d3b-f608-3534da807df2"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Logistic Regression Confusion Matrix:\n",
            "[[1386  153]\n",
            " [ 247  327]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G3ftKMzwv8WV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "19VEUgRqv0bF"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Les5nAE90Gnz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Classifier"
      ],
      "metadata": {
        "id": "QGJmiwgRlu0V"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=100, random_state=42)  # Create a RandomForestClassifier instance with 100 trees\n"
      ],
      "metadata": {
        "id": "Khu2UH3Nlu2b"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train, y_train)  # Train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "T1cVnw9AwOMR",
        "outputId": "0811690d-3f84-4ff8-90a4-b02c72aa2381"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GCFXBP01wOPa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict and evaluate Random Forest"
      ],
      "metadata": {
        "id": "j-31kJ7bwOSE"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_rf = rf.predict(X_test)  # Predict the target variable on the test set"
      ],
      "metadata": {
        "id": "A7ZJNLNywOZC"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nRandom Forest Classification Report:\")  # Print statement for clarity\n",
        "print(classification_report(y_test, y_pred_rf))  # Display classification report for Random Forest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WusoLmvqwOga",
        "outputId": "d2d16dff-e18e-457a-dbb8-68fbcdffbdb5"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.92      0.87      1539\n",
            "           1       0.69      0.49      0.57       574\n",
            "\n",
            "    accuracy                           0.80      2113\n",
            "   macro avg       0.76      0.70      0.72      2113\n",
            "weighted avg       0.79      0.80      0.79      2113\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RagYxY-uwOi9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nRandom Forest Confusion Matrix:\")  # Print statement for clarity\n",
        "print(confusion_matrix(y_test, y_pred_rf))  # Display confusion matrix for Random Forest"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpT-DJupwOpk",
        "outputId": "4cda578e-31d9-423f-a355-9555d5cf8c12"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Confusion Matrix:\n",
            "[[1410  129]\n",
            " [ 293  281]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UWYXvTqzwZBQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cYLSl0lWwZEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC Curve"
      ],
      "metadata": {
        "id": "-u7PUx7swZHt"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob_logreg = logreg.predict_proba(X_test)[:, 1]  # Predict probabilities for the positive class"
      ],
      "metadata": {
        "id": "C0Lcqq1VwZKt"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fpr_logreg, tpr_logreg, _ = roc_curve(y_test, y_prob_logreg)  # Compute ROC curve for Logistic Regression"
      ],
      "metadata": {
        "id": "f01A1hrqwZRi"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_logreg = auc(fpr_logreg, tpr_logreg)  # Compute AUC for Logistic Regression"
      ],
      "metadata": {
        "id": "b76DTSoKwZUf"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w4XaF-jqwiwo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob_rf = rf.predict_proba(X_test)[:, 1]  # Predict probabilities for the positive class"
      ],
      "metadata": {
        "id": "LMY4znRswizl"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fpr_rf, tpr_rf, _ = roc_curve(y_test, y_prob_rf)  # Compute ROC curve for Random Forest"
      ],
      "metadata": {
        "id": "_mJUB-Pwwi3H"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_rf = auc(fpr_rf, tpr_rf)  # Compute AUC for Random Forest"
      ],
      "metadata": {
        "id": "Yk5w0Q_OwjL6"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7ZPLRVmZwwIg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "plt.plot(fpr_logreg, tpr_logreg, color='blue', lw=2, label=f'Logistic Regression (AUC = {roc_auc_logreg:.2f})')  # Plot ROC curve for Logistic Regression\n",
        "plt.plot(fpr_rf, tpr_rf, color='green', lw=2, label=f'Random Forest (AUC = {roc_auc_rf:.2f})')  # Plot ROC curve for Random Forest\n",
        "plt.plot([0, 1], [0, 1], color='grey', linestyle='--')  # Plot diagonal line representing random chance\n",
        "plt.xlim([0.0, 1.0])  # Set x-axis limits\n",
        "plt.ylim([0.0, 1.05])  # Set y-axis limits\n",
        "plt.xlabel('False Positive Rate')  # Set x-axis label\n",
        "plt.ylabel('True Positive Rate')  # Set y-axis label\n",
        "plt.title('Receiver Operating Characteristic Curve')  # Set plot title\n",
        "plt.legend(loc='lower right')  # Add legend to the plot\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "8LEioJ2AwwLt",
        "outputId": "80fd5f19-8679-4267-92b9-3af758db4eec"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WW1hI-udwxQZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SxNQUNJEwxWp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pEBK_D2lwwTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Additional Steps\n",
        "\n",
        "- **Feature engineering**: Create new features or modify existing ones to enhance model performance.\n",
        "- **Cross-validation**: Use techniques such as k-fold cross-validation to ensure the model generalizes well to unseen data.\n",
        "- **Ensemble methods**: Combine multiple models to improve prediction accuracy and robustness.\n",
        "- **Model selection**: Compare different models and select the one that performs best based on evaluation metrics."
      ],
      "metadata": {
        "id": "3Cxo1oHcx2Ty"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZomEXEtTx3fC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nuihJIBRx3jC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TILivXG6x32c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusions\n",
        "\n",
        "1. **Dataset Quality and Preprocessing**:\n",
        "   - Effective preprocessing and handling of missing values are crucial for ensuring the quality of the dataset. Proper encoding and scaling of features are necessary steps to prepare the data for model training.\n",
        "\n",
        "2. **Exploratory Data Analysis (EDA)**:\n",
        "   - EDA provides valuable insights into the dataset, including the distribution of key features and target variables. Understanding these distributions helps in making informed decisions during model selection and tuning.\n",
        "\n",
        "3. **Model Training and Evaluation**:\n",
        "   - Different classification algorithms such as Logistic Regression and Random Forest were evaluated. It is important to select models based on their performance metrics, including accuracy, precision, recall, and F1 score. ROC curve analysis and AUC provide additional insights into model performance.\n",
        "\n",
        "4. **Model Performance**:\n",
        "   - Performance metrics and visualizations such as confusion matrices and classification reports are essential for assessing the effectiveness of the models. These evaluations help in identifying strengths and weaknesses in model predictions.\n",
        "\n",
        "5. **Further Improvements**:\n",
        "   - Additional steps like feature engineering, hyperparameter tuning, and cross-validation can significantly enhance model performance. Exploring ensemble methods and comparing different models can further optimize results.\n",
        "\n",
        "6. **Impact and Application**:\n",
        "   - The insights gained from this analysis can be used to make data-driven decisions and improve business processes. For instance, understanding customer churn can help in devising strategies to retain customers and optimize service offerings.\n",
        "\n",
        "7. **Future Work**:\n",
        "   - Future work may involve experimenting with other machine learning algorithms, incorporating external data sources, and refining the feature set. Continuous monitoring and updating of the model will ensure its relevance and accuracy over time."
      ],
      "metadata": {
        "id": "PhyIf46Qx4LT"
      }
    }
  ]
}