churn_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNqouFyvXV6newWEVT4BxTn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DevNandini02/CodeClauseInternship_Churn_Prediction/blob/main/churn_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l4-WIqDJ0m1j"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/Telecom_customer churn.csv')"
      ],
      "metadata": {
        "id": "oER1PKA03cGX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.expand_frame_repr', False)\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJ76U3mO4fav",
        "outputId": "74df788f-6319-403c-ac1e-85fb31abcef3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       rev_Mean  mou_Mean  totmrc_Mean  da_Mean  ovrmou_Mean  ovrrev_Mean  vceovr_Mean  datovr_Mean  roam_Mean  change_mou  ...  forgntvl  ethnic  kid0_2  kid3_5  kid6_10  kid11_15  kid16_17  creditcd  eqpdays  Customer_ID\n",
            "0       23.9975    219.25       22.500   0.2475         0.00       0.0000       0.0000       0.0000     0.0000     -157.25  ...       0.0       N       U       U        U         U         U         Y    361.0    1000001.0\n",
            "1       57.4925    482.75       37.425   0.2475        22.75       9.1000       9.1000       0.0000     0.0000      532.25  ...       0.0       Z       U       U        U         U         U         Y    240.0    1000002.0\n",
            "2       16.9900     10.25       16.990   0.0000         0.00       0.0000       0.0000       0.0000     0.0000       -4.25  ...       0.0       N       U       Y        U         U         U         Y   1504.0    1000003.0\n",
            "3       38.0000      7.50       38.000   0.0000         0.00       0.0000       0.0000       0.0000     0.0000       -1.50  ...       0.0       U       Y       U        U         U         U         Y   1812.0    1000004.0\n",
            "4       55.2300    570.50       71.980   0.0000         0.00       0.0000       0.0000       0.0000     0.0000       38.50  ...       0.0       I       U       U        U         U         U         Y    434.0    1000005.0\n",
            "...         ...       ...          ...      ...          ...          ...          ...          ...        ...         ...  ...       ...     ...     ...     ...      ...       ...       ...       ...      ...          ...\n",
            "60784   42.4650    529.75       39.990   2.4750         0.00       0.0000       0.0000       0.0000     0.0000       42.25  ...       0.0       S       U       U        U         U         U         Y    403.0    1060785.0\n",
            "60785   53.1850    261.25       29.990   0.2475        46.25      16.1875      16.1875       0.0000     1.2525      231.75  ...       0.0       U       U       U        U         U         U         N    317.0    1060786.0\n",
            "60786   18.0250    520.50       21.495   0.0000         0.25       0.0975       0.0000       0.0975     0.0000     -520.50  ...       0.0       S       U       U        U         U         U         N    425.0    1060787.0\n",
            "60787   33.3150    203.75       39.990   0.0000         9.50       3.3250       3.3250       0.0000     0.0000      -56.75  ...       0.0       S       U       U        U         U         U         N    486.0    1060788.0\n",
            "60788   79.7575    609.75       39.990   0.2475       139.00      48.7300      47.9500       0.7800     0.1600       68.25  ...       NaN     NaN     NaN     NaN      NaN       NaN       NaN       NaN      NaN          NaN\n",
            "\n",
            "[60789 rows x 100 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_names = data.columns\n",
        "print(column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2PfcK-a44fj",
        "outputId": "78ddf4f1-6834-4e89-af23-fa2301741162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['rev_Mean', 'mou_Mean', 'totmrc_Mean', 'da_Mean', 'ovrmou_Mean',\n",
            "       'ovrrev_Mean', 'vceovr_Mean', 'datovr_Mean', 'roam_Mean', 'change_mou',\n",
            "       'change_rev', 'drop_vce_Mean', 'drop_dat_Mean', 'blck_vce_Mean',\n",
            "       'blck_dat_Mean', 'unan_vce_Mean', 'unan_dat_Mean', 'plcd_vce_Mean',\n",
            "       'plcd_dat_Mean', 'recv_vce_Mean', 'recv_sms_Mean', 'comp_vce_Mean',\n",
            "       'comp_dat_Mean', 'custcare_Mean', 'ccrndmou_Mean', 'cc_mou_Mean',\n",
            "       'inonemin_Mean', 'threeway_Mean', 'mou_cvce_Mean', 'mou_cdat_Mean',\n",
            "       'mou_rvce_Mean', 'owylis_vce_Mean', 'mouowylisv_Mean',\n",
            "       'iwylis_vce_Mean', 'mouiwylisv_Mean', 'peak_vce_Mean', 'peak_dat_Mean',\n",
            "       'mou_peav_Mean', 'mou_pead_Mean', 'opk_vce_Mean', 'opk_dat_Mean',\n",
            "       'mou_opkv_Mean', 'mou_opkd_Mean', 'drop_blk_Mean', 'attempt_Mean',\n",
            "       'complete_Mean', 'callfwdv_Mean', 'callwait_Mean', 'churn', 'months',\n",
            "       'uniqsubs', 'actvsubs', 'new_cell', 'crclscod', 'asl_flag', 'totcalls',\n",
            "       'totmou', 'totrev', 'adjrev', 'adjmou', 'adjqty', 'avgrev', 'avgmou',\n",
            "       'avgqty', 'avg3mou', 'avg3qty', 'avg3rev', 'avg6mou', 'avg6qty',\n",
            "       'avg6rev', 'prizm_social_one', 'area', 'dualband', 'refurb_new',\n",
            "       'hnd_price', 'phones', 'models', 'hnd_webcap', 'truck', 'rv', 'ownrent',\n",
            "       'lor', 'dwlltype', 'marital', 'adults', 'infobase', 'income',\n",
            "       'numbcars', 'HHstatin', 'dwllsize', 'forgntvl', 'ethnic', 'kid0_2',\n",
            "       'kid3_5', 'kid6_10', 'kid11_15', 'kid16_17', 'creditcd', 'eqpdays',\n",
            "       'Customer_ID'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_columns = data.select_dtypes(include=['number']).columns.tolist()\n",
        "\n",
        "# 'numerical_columns' now contains the names of all numerical columns in our DataFrame\n",
        "print(numerical_columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmJJuWJd51_Q",
        "outputId": "cea4479b-8d68-4c7f-eda6-c15530ffc73d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['rev_Mean', 'mou_Mean', 'totmrc_Mean', 'da_Mean', 'ovrmou_Mean', 'ovrrev_Mean', 'vceovr_Mean', 'datovr_Mean', 'roam_Mean', 'change_mou', 'change_rev', 'drop_vce_Mean', 'drop_dat_Mean', 'blck_vce_Mean', 'blck_dat_Mean', 'unan_vce_Mean', 'unan_dat_Mean', 'plcd_vce_Mean', 'plcd_dat_Mean', 'recv_vce_Mean', 'recv_sms_Mean', 'comp_vce_Mean', 'comp_dat_Mean', 'custcare_Mean', 'ccrndmou_Mean', 'cc_mou_Mean', 'inonemin_Mean', 'threeway_Mean', 'mou_cvce_Mean', 'mou_cdat_Mean', 'mou_rvce_Mean', 'owylis_vce_Mean', 'mouowylisv_Mean', 'iwylis_vce_Mean', 'mouiwylisv_Mean', 'peak_vce_Mean', 'peak_dat_Mean', 'mou_peav_Mean', 'mou_pead_Mean', 'opk_vce_Mean', 'opk_dat_Mean', 'mou_opkv_Mean', 'mou_opkd_Mean', 'drop_blk_Mean', 'attempt_Mean', 'complete_Mean', 'callfwdv_Mean', 'callwait_Mean', 'churn', 'months', 'uniqsubs', 'actvsubs', 'totcalls', 'totmou', 'totrev', 'adjrev', 'adjmou', 'adjqty', 'avgrev', 'avgmou', 'avgqty', 'avg3mou', 'avg3qty', 'avg3rev', 'avg6mou', 'avg6qty', 'avg6rev', 'hnd_price', 'phones', 'models', 'truck', 'rv', 'lor', 'adults', 'income', 'numbcars', 'forgntvl', 'eqpdays', 'Customer_ID']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with missing values\n",
        "data.dropna(inplace=True)\n",
        "\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "data['crclscod'] = label_encoder.fit_transform(data['crclscod'])\n",
        "\n",
        "scaler = StandardScaler()\n",
        "numerical_features = ['rev_Mean', 'mou_Mean', 'totmrc_Mean', 'da_Mean', 'ovrmou_Mean','ovrrev_Mean','vceovr_Mean', 'datovr_Mean', 'roam_Mean', 'change_mou', 'change_rev', 'drop_vce_Mean', 'drop_dat_Mean', 'blck_vce_Mean', 'blck_dat_Mean', 'unan_vce_Mean', 'unan_dat_Mean', 'plcd_vce_Mean', 'plcd_dat_Mean', 'recv_vce_Mean', 'recv_sms_Mean', 'comp_vce_Mean', 'comp_dat_Mean', 'custcare_Mean', 'ccrndmou_Mean', 'cc_mou_Mean', 'inonemin_Mean', 'threeway_Mean', 'mou_cvce_Mean', 'mou_cdat_Mean', 'mou_rvce_Mean', 'owylis_vce_Mean', 'mouowylisv_Mean', 'iwylis_vce_Mean', 'mouiwylisv_Mean', 'peak_vce_Mean', 'peak_dat_Mean', 'mou_peav_Mean', 'mou_pead_Mean', 'opk_vce_Mean', 'opk_dat_Mean', 'mou_opkv_Mean', 'mou_opkd_Mean', 'drop_blk_Mean', 'attempt_Mean', 'complete_Mean', 'callfwdv_Mean', 'callwait_Mean', 'churn', 'months', 'uniqsubs', 'actvsubs', 'totcalls', 'totmou', 'totrev', 'adjrev', 'adjmou', 'adjqty', 'avgrev', 'avgmou', 'avgqty', 'avg3mou', 'avg3qty', 'avg3rev', 'avg6mou', 'avg6qty', 'avg6rev', 'hnd_price', 'phones', 'models', 'truck', 'rv', 'lor', 'adults', 'income', 'numbcars', 'forgntvl', 'eqpdays', 'Customer_ID']  # List all numerical columns\n",
        "data[numerical_features] = scaler.fit_transform(data[numerical_features])\n",
        "\n",
        "\n",
        "X = data.drop(columns=['churn'])\n",
        "y = data['churn']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "aKlXEuYB6GTW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in X_train.columns:\n",
        "    try:\n",
        "        X_train[column] = X_train[column].astype(float)\n",
        "    except ValueError as e:\n",
        "        print(f\"Error in column '{column}': {e}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bvs3hj4T-XQ6",
        "outputId": "ce75a7c8-2a90-4fa7-b973-19a18bb076ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error in column 'new_cell': could not convert string to float: 'U'\n",
            "Error in column 'asl_flag': could not convert string to float: 'N'\n",
            "Error in column 'area': could not convert string to float: 'TENNESSEE AREA'\n",
            "Error in column 'dualband': could not convert string to float: 'Y'\n",
            "Error in column 'refurb_new': could not convert string to float: 'N'\n",
            "Error in column 'hnd_webcap': could not convert string to float: 'WC'\n",
            "Error in column 'ownrent': could not convert string to float: 'O'\n",
            "Error in column 'dwlltype': could not convert string to float: 'S'\n",
            "Error in column 'infobase': could not convert string to float: 'M'\n",
            "Error in column 'HHstatin': could not convert string to float: 'I'\n",
            "Error in column 'dwllsize': could not convert string to float: 'A'\n",
            "Error in column 'kid0_2': could not convert string to float: 'U'\n",
            "Error in column 'kid3_5': could not convert string to float: 'U'\n",
            "Error in column 'kid6_10': could not convert string to float: 'Y'\n",
            "Error in column 'kid11_15': could not convert string to float: 'U'\n",
            "Error in column 'kid16_17': could not convert string to float: 'U'\n",
            "Error in column 'creditcd': could not convert string to float: 'Y'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the problematic columns from your dataset\n",
        "columns_to_drop = ['new_cell', 'asl_flag', 'area', 'dualband', 'refurb_new', 'hnd_webcap', 'ownrent', 'dwlltype', 'infobase', 'HHstatin', 'dwllsize', 'kid0_2', 'kid3_5', 'kid6_10', 'kid11_15', 'kid16_17', 'creditcd']\n",
        "X_train = X_train.drop(columns=columns_to_drop)\n",
        "X_test = X_test.drop(columns=columns_to_drop)\n"
      ],
      "metadata": {
        "id": "59ZheQVY-nvK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#for continuous error in Y_train\n",
        "print(y_train.dtype)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtBz8chv-5Od",
        "outputId": "0df94102-4ae7-44e6-fa73-9829cc7a75c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert it to integers\n",
        "y_train = y_train.astype(int)"
      ],
      "metadata": {
        "id": "TQ0ta_hG_LKG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Selection and Training\n",
        "# Logistic Regression\n",
        "model = LogisticRegression(random_state=42, max_iter=1000)\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "zFmOPi1-7d-p",
        "outputId": "5714b3eb-0b85-4bea-cb85-070f211b71f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#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, 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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation\n",
        "y_pred = model.predict(X_test)\n",
        "y_proba = model.predict_proba(X_test)[:, 1]  # Probability of churn"
      ],
      "metadata": {
        "id": "FXDJ_vP1Ammm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test.dtype)\n",
        "print(y_pred.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25AVo0z0AYWx",
        "outputId": "376ef359-cbc3-46e9-b6e8-ddb5a604a336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n",
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "threshold = 0.5\n",
        "\n",
        "# Create a function to convert a single value to binary\n",
        "def convert_to_binary(value):\n",
        "    if value >= threshold:\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "# Use the function to convert all values in y_test to binary\n",
        "y_test = np.array([convert_to_binary(value) for value in y_test])\n"
      ],
      "metadata": {
        "id": "2x19rDtEEbuy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW4HUkMtE37p",
        "outputId": "f575f11a-f9cb-450d-b457-9e6156576994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation\n",
        "y_pred = model.predict(X_test)\n",
        "y_proba = model.predict_proba(X_test)[:, 1]  # Probability of churn\n",
        "\n",
        "# Calculate evaluation metrics\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1 = f1_score(y_test, y_pred)\n",
        "roc_auc = roc_auc_score(y_test, y_proba)\n",
        "\n",
        "# Print evaluation metrics\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "print(f\"Precision: {precision:.2f}\")\n",
        "print(f\"Recall: {recall:.2f}\")\n",
        "print(f\"F1 Score: {f1:.2f}\")\n",
        "print(f\"AUC-ROC Score: {roc_auc:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r0Sdbw3p_ilA",
        "outputId": "da52c120-95a5-4b11-b0bb-2f7696f6505e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.59\n",
            "Precision: 0.57\n",
            "Recall: 0.56\n",
            "F1 Score: 0.56\n",
            "AUC-ROC Score: 0.63\n"
          ]
        }
      ]
    }
  ]
}