logistic_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMsn8oBGQJfTn0dqMCgBBe/",
      "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/ericyoc/ml_portfolio/blob/main/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "KgS1sP7s4qNL"
      },
      "outputs": [],
      "source": [
        "#logistic regression\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, classification_report, jaccard_score, log_loss\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Surpress warnings:\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "import warnings\n",
        "warnings.warn = warn"
      ],
      "metadata": {
        "id": "ME6kfnmz42d-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "3pJJZMwb43YS"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_logistic_regression(X, y, model, title):\n",
        "    plt.figure(figsize=(8, 6))\n",
        "\n",
        "    # Create a mesh grid of points\n",
        "    x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
        "    y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
        "                         np.arange(y_min, y_max, 0.02))\n",
        "\n",
        "    # Calculate the decision boundary\n",
        "    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "    Z = Z.reshape(xx.shape)\n",
        "\n",
        "    # Plot the decision boundary and the predicted probabilities\n",
        "    plt.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, edgecolors='k')\n",
        "\n",
        "    # Plot the logistic regression line\n",
        "    x_values = np.array([x_min, x_max])\n",
        "    y_values = -(model.intercept_[0] + model.coef_[0][0] * x_values) / model.coef_[0][1]\n",
        "    plt.plot(x_values, y_values, 'k-', lw=2)\n",
        "\n",
        "    plt.xlabel('Tenure (Feature 1)')\n",
        "    plt.ylabel('Age (Feature 2)')\n",
        "    plt.title(title)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "8hnSvpMN6MLJ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    # Load data\n",
        "    churn_df = pd.read_csv(\"ml_data/ChurnData.csv\")\n",
        "    print(\"Churn Dataset Head:\")\n",
        "    print(churn_df.head())\n",
        "    print()\n",
        "\n",
        "    # Data pre-processing and selection\n",
        "    churn_df = churn_df[['tenure', 'age', 'address', 'income', 'ed', 'employ', 'equip', 'callcard', 'wireless', 'churn']]\n",
        "    churn_df['churn'] = churn_df['churn'].astype('int')\n",
        "    print(\"Selected Columns:\")\n",
        "    print(churn_df.head())\n",
        "    print(\"Dataset Shape:\", churn_df.shape)\n",
        "    print()\n",
        "\n",
        "    # Define X and y using only the first two features\n",
        "    X = np.asarray(churn_df[['tenure', 'age']])\n",
        "    print(\"X (First 5 rows):\")\n",
        "    print(X[0:5])\n",
        "    print()\n",
        "\n",
        "    y = np.asarray(churn_df['churn'])\n",
        "    print(\"y (First 5 elements):\")\n",
        "    print(y[0:5])\n",
        "    print()\n",
        "\n",
        "    # Normalize the dataset\n",
        "    X = preprocessing.StandardScaler().fit(X).transform(X)\n",
        "    print(\"Normalized X (First 5 rows):\")\n",
        "    print(X[0:5])\n",
        "    print()\n",
        "\n",
        "    # Train and test dataset split\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
        "    print('Train Set Shape:', X_train.shape, y_train.shape)\n",
        "    print('Test Set Shape:', X_test.shape, y_test.shape)\n",
        "    print()\n",
        "\n",
        "    # Modeling: Logistic Regression\n",
        "    LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train, y_train)\n",
        "    print(\"Logistic Regression Model:\")\n",
        "    print(LR)\n",
        "    print()\n",
        "\n",
        "    # Predict\n",
        "    yhat = LR.predict(X_test)\n",
        "    print(\"Predicted labels:\")\n",
        "    print(yhat)\n",
        "    print()\n",
        "\n",
        "    yhat_prob = LR.predict_proba(X_test)\n",
        "    print(\"Predicted Probabilities:\")\n",
        "    print(yhat_prob)\n",
        "    print()\n",
        "\n",
        "    # Plot logistic regression\n",
        "    plot_logistic_regression(X_train, y_train, LR, 'Figure 1: Logistic Regression')\n",
        "    print(\"Explanation:\")\n",
        "    print(\"The plot shows the decision boundary of the logistic regression model trained on the 'tenure' and 'age' features.\")\n",
        "    print(\"The x-axis represents the 'tenure' feature (Feature 1), and the y-axis represents the 'age' feature (Feature 2).\")\n",
        "    print(\"The background color represents the predicted probabilities, and the scatter points represent the actual customer churn status.\")\n",
        "    print(\"The black line represents the logistic regression decision boundary.\")\n",
        "    print()\n",
        "\n",
        "    # Evaluation\n",
        "    print(\"Jaccard Score:\", jaccard_score(y_test, yhat, pos_label=0))\n",
        "    print()\n",
        "\n",
        "    # Confusion matrix\n",
        "    cnf_matrix = confusion_matrix(y_test, yhat, labels=[1, 0])\n",
        "    np.set_printoptions(precision=2)\n",
        "\n",
        "    # Plot non-normalized confusion matrix\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.title(\"Figure 2: Confusion Matrix\")\n",
        "    plot_confusion_matrix(cnf_matrix, classes=['churn=1', 'churn=0'], normalize=False, title='Confusion Matrix')\n",
        "    plt.show()\n",
        "    print(\"Explanation:\")\n",
        "    print(\"The confusion matrix shows the model's performance in terms of true positives, true negatives, false positives, and false negatives.\")\n",
        "    print(\"It helps evaluate the model's ability to correctly classify customer churn.\")\n",
        "    print()\n",
        "\n",
        "    print(\"Classification Report:\")\n",
        "    print(classification_report(y_test, yhat))\n",
        "    print()\n",
        "\n",
        "    print(\"Log Loss:\", log_loss(y_test, yhat_prob))\n",
        "    print()\n",
        "\n",
        "    # Build Logistic Regression model with different solver and regularization values\n",
        "    LR2 = LogisticRegression(C=0.01, solver='sag').fit(X_train, y_train)\n",
        "    yhat_prob2 = LR2.predict_proba(X_test)\n",
        "    print(\"LogLoss (LR2): %.2f\" % log_loss(y_test, yhat_prob2))"
      ],
      "metadata": {
        "id": "rSyJwPqJ45oa"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7X6wP2Bu5QU8",
        "outputId": "946808fa-29bf-4705-95c7-08aa3528d0bc"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Churn Dataset Head:\n",
            "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
            "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
            "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
            "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
            "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
            "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
            "\n",
            "   longmon  ...  pager  internet  callwait  confer  ebill  loglong  logtoll  \\\n",
            "0     4.40  ...    1.0       0.0       1.0     1.0    0.0    1.482    3.033   \n",
            "1     9.45  ...    0.0       0.0       0.0     0.0    0.0    2.246    3.240   \n",
            "2     6.30  ...    0.0       0.0       0.0     1.0    0.0    1.841    3.240   \n",
            "3     6.05  ...    1.0       1.0       1.0     1.0    1.0    1.800    3.807   \n",
            "4     7.10  ...    0.0       0.0       1.0     1.0    0.0    1.960    3.091   \n",
            "\n",
            "   lninc  custcat  churn  \n",
            "0  4.913      4.0    1.0  \n",
            "1  3.497      1.0    1.0  \n",
            "2  3.401      3.0    0.0  \n",
            "3  4.331      4.0    0.0  \n",
            "4  4.382      3.0    0.0  \n",
            "\n",
            "[5 rows x 28 columns]\n",
            "\n",
            "Selected Columns:\n",
            "   tenure   age  address  income   ed  employ  equip  callcard  wireless  \\\n",
            "0    11.0  33.0      7.0   136.0  5.0     5.0    0.0       1.0       1.0   \n",
            "1    33.0  33.0     12.0    33.0  2.0     0.0    0.0       0.0       0.0   \n",
            "2    23.0  30.0      9.0    30.0  1.0     2.0    0.0       0.0       0.0   \n",
            "3    38.0  35.0      5.0    76.0  2.0    10.0    1.0       1.0       1.0   \n",
            "4     7.0  35.0     14.0    80.0  2.0    15.0    0.0       1.0       0.0   \n",
            "\n",
            "   churn  \n",
            "0      1  \n",
            "1      1  \n",
            "2      0  \n",
            "3      0  \n",
            "4      0  \n",
            "Dataset Shape: (200, 10)\n",
            "\n",
            "X (First 5 rows):\n",
            "[[11. 33.]\n",
            " [33. 33.]\n",
            " [23. 30.]\n",
            " [38. 35.]\n",
            " [ 7. 35.]]\n",
            "\n",
            "y (First 5 elements):\n",
            "[1 1 0 0 0]\n",
            "\n",
            "Normalized X (First 5 rows):\n",
            "[[-1.14 -0.63]\n",
            " [-0.12 -0.63]\n",
            " [-0.58 -0.86]\n",
            " [ 0.12 -0.47]\n",
            " [-1.32 -0.47]]\n",
            "\n",
            "Train Set Shape: (160, 2) (160,)\n",
            "Test Set Shape: (40, 2) (40,)\n",
            "\n",
            "Logistic Regression Model:\n",
            "LogisticRegression(C=0.01, solver='liblinear')\n",
            "\n",
            "Predicted labels:\n",
            "[0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1\n",
            " 1 0 0]\n",
            "\n",
            "Predicted Probabilities:\n",
            "[[0.59 0.41]\n",
            " [0.58 0.42]\n",
            " [0.53 0.47]\n",
            " [0.59 0.41]\n",
            " [0.54 0.46]\n",
            " [0.52 0.48]\n",
            " [0.52 0.48]\n",
            " [0.56 0.44]\n",
            " [0.48 0.52]\n",
            " [0.6  0.4 ]\n",
            " [0.62 0.38]\n",
            " [0.63 0.37]\n",
            " [0.46 0.54]\n",
            " [0.5  0.5 ]\n",
            " [0.58 0.42]\n",
            " [0.54 0.46]\n",
            " [0.59 0.41]\n",
            " [0.58 0.42]\n",
            " [0.57 0.43]\n",
            " [0.52 0.48]\n",
            " [0.57 0.43]\n",
            " [0.61 0.39]\n",
            " [0.62 0.38]\n",
            " [0.59 0.41]\n",
            " [0.55 0.45]\n",
            " [0.64 0.36]\n",
            " [0.55 0.45]\n",
            " [0.58 0.42]\n",
            " [0.5  0.5 ]\n",
            " [0.65 0.35]\n",
            " [0.64 0.36]\n",
            " [0.59 0.41]\n",
            " [0.5  0.5 ]\n",
            " [0.64 0.36]\n",
            " [0.59 0.41]\n",
            " [0.58 0.42]\n",
            " [0.47 0.53]\n",
            " [0.5  0.5 ]\n",
            " [0.66 0.34]\n",
            " [0.57 0.43]]\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explanation:\n",
            "The plot shows the decision boundary of the logistic regression model trained on the 'tenure' and 'age' features.\n",
            "The x-axis represents the 'tenure' feature (Feature 1), and the y-axis represents the 'age' feature (Feature 2).\n",
            "The background color represents the predicted probabilities, and the scatter points represent the actual customer churn status.\n",
            "The black line represents the logistic regression decision boundary.\n",
            "\n",
            "Jaccard Score: 0.6388888888888888\n",
            "\n",
            "Confusion matrix, without normalization\n",
            "[[ 4 11]\n",
            " [ 2 23]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Explanation:\n",
            "The confusion matrix shows the model's performance in terms of true positives, true negatives, false positives, and false negatives.\n",
            "It helps evaluate the model's ability to correctly classify customer churn.\n",
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.92      0.78        25\n",
            "           1       0.67      0.27      0.38        15\n",
            "\n",
            "    accuracy                           0.68        40\n",
            "   macro avg       0.67      0.59      0.58        40\n",
            "weighted avg       0.67      0.68      0.63        40\n",
            "\n",
            "\n",
            "Log Loss: 0.6225698473729026\n",
            "\n",
            "LogLoss (LR2): 0.64\n"
          ]
        }
      ]
    }
  ]
}