{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Student name: Muhammad Haroon**\n",
        "# **Student ID: 2434025**\n",
        "\n",
        "**Machine Learning Activity: Feature Selection and Model Comparison**"
      ],
      "metadata": {
        "id": "4CSmT0OPP2rs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rlx-xCLIP16i",
        "outputId": "f5e9073d-85db-4958-ba98-61406266c780"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Machine Learning Activity: Feature Selection and Model Comparison\n",
            "======================================================================\n",
            "\n",
            "STEP 1: Loading and Preprocessing Dataset\n",
            "--------------------------------------------------\n",
            "Dataset Shape: (768, 9)\n",
            "\n",
            "First 5 rows:\n",
            "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
            "0            6      148             72             35        0  33.6   \n",
            "1            1       85             66             29        0  26.6   \n",
            "2            8      183             64              0        0  23.3   \n",
            "3            1       89             66             23       94  28.1   \n",
            "4            0      137             40             35      168  43.1   \n",
            "\n",
            "   DiabetesPedigreeFunction  Age  Outcome  \n",
            "0                     0.627   50        1  \n",
            "1                     0.351   31        0  \n",
            "2                     0.672   32        1  \n",
            "3                     0.167   21        0  \n",
            "4                     2.288   33        1  \n",
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n",
            "None\n",
            "\n",
            "Missing Values:\n",
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n",
            "\n",
            "Class Distribution:\n",
            "Outcome\n",
            "0    500\n",
            "1    268\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Rows with zero values in critical features:\n",
            "Glucose: 5 zeros (0.65%)\n",
            "BloodPressure: 35 zeros (4.56%)\n",
            "SkinThickness: 227 zeros (29.56%)\n",
            "Insulin: 374 zeros (48.70%)\n",
            "BMI: 11 zeros (1.43%)\n",
            "\n",
            "After handling zeros:\n",
            "Glucose: 0 zeros\n",
            "BloodPressure: 0 zeros\n",
            "SkinThickness: 0 zeros\n",
            "Insulin: 0 zeros\n",
            "BMI: 0 zeros\n",
            "\n",
            "Features: ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
            "\n",
            "Training set: 614 samples\n",
            "Testing set: 154 samples\n"
          ]
        }
      ],
      "source": [
        "# Import required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "print(\"Machine Learning Activity: Feature Selection and Model Comparison\")\n",
        "print(\"=\" * 70)\n",
        "\n",
        "# Part 1: Building a Classifier with Random Forest\n",
        "\n",
        "# 1. Load and preprocess the dataset\n",
        "print(\"\\nSTEP 1: Loading and Preprocessing Dataset\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "# Let's use the Diabetes dataset as mentioned\n",
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Dataset/Haroon/diabetes.csv')\n",
        "\n",
        "print(f\"Dataset Shape: {df.shape}\")\n",
        "print(f\"\\nFirst 5 rows:\")\n",
        "print(df.head())\n",
        "print(f\"\\nDataset Info:\")\n",
        "print(df.info())\n",
        "print(f\"\\nMissing Values:\")\n",
        "print(df.isnull().sum())\n",
        "print(f\"\\nClass Distribution:\")\n",
        "print(df['Outcome'].value_counts())\n",
        "\n",
        "# Check for zeros in features that shouldn't be zero\n",
        "zero_columns = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
        "print(f\"\\nRows with zero values in critical features:\")\n",
        "for col in zero_columns:\n",
        "    zero_count = (df[col] == 0).sum()\n",
        "    print(f\"{col}: {zero_count} zeros ({zero_count/len(df)*100:.2f}%)\")\n",
        "\n",
        "# Handle zeros by replacing with median (common approach for diabetes dataset)\n",
        "df_clean = df.copy()\n",
        "for col in zero_columns:\n",
        "    df_clean[col] = df_clean[col].replace(0, df_clean[col].median())\n",
        "\n",
        "print(f\"\\nAfter handling zeros:\")\n",
        "for col in zero_columns:\n",
        "    zero_count = (df_clean[col] == 0).sum()\n",
        "    print(f\"{col}: {zero_count} zeros\")\n",
        "\n",
        "# Prepare features and target\n",
        "X = df_clean.drop('Outcome', axis=1)\n",
        "y = df_clean['Outcome']\n",
        "feature_names = X.columns.tolist()\n",
        "\n",
        "print(f\"\\nFeatures: {feature_names}\")\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "print(f\"\\nTraining set: {X_train.shape[0]} samples\")\n",
        "print(f\"Testing set: {X_test.shape[0]} samples\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Train Random Forest Classifier\n",
        "print(\"STEP 2: Training Random Forest Classifier\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "rf_original = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_original.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions\n",
        "y_pred_rf_original = rf_original.predict(X_test)\n",
        "\n",
        "# Calculate metrics\n",
        "accuracy_rf_original = accuracy_score(y_test, y_pred_rf_original)\n",
        "precision_rf_original = precision_score(y_test, y_pred_rf_original)\n",
        "recall_rf_original = recall_score(y_test, y_pred_rf_original)\n",
        "f1_rf_original = f1_score(y_test, y_pred_rf_original)\n",
        "\n",
        "print(\"Random Forest (Original) Performance:\")\n",
        "print(f\"Accuracy:  {accuracy_rf_original:.4f}\")\n",
        "print(f\"Precision: {precision_rf_original:.4f}\")\n",
        "print(f\"Recall:    {recall_rf_original:.4f}\")\n",
        "print(f\"F1-Score:  {f1_rf_original:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPod09ZdRIFL",
        "outputId": "a4fa6429-d100-4f3a-93b6-41504e083bac"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "STEP 2: Training Random Forest Classifier\n",
            "--------------------------------------------------\n",
            "Random Forest (Original) Performance:\n",
            "Accuracy:  0.7597\n",
            "Precision: 0.6809\n",
            "Recall:    0.5926\n",
            "F1-Score:  0.6337\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Manual Feature Selection\n",
        "print(\"\\n STEP 3: Manual Feature Selection\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "# Get feature importances\n",
        "importances = rf_original.feature_importances_\n",
        "feature_importance_df = pd.DataFrame({\n",
        "    'Feature': feature_names,\n",
        "    'Importance': importances\n",
        "}).sort_values('Importance', ascending=False)\n",
        "\n",
        "print(\"Feature Importances:\")\n",
        "print(feature_importance_df)\n",
        "\n",
        "# Visualize feature importances\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=feature_importance_df, x='Importance', y='Feature', palette='viridis')\n",
        "plt.title('Random Forest - Feature Importances')\n",
        "plt.xlabel('Importance Score')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Select top 5 features\n",
        "top_features = feature_importance_df.head(5)['Feature'].tolist()\n",
        "print(f\"\\nTop 5 features selected: {top_features}\")\n",
        "\n",
        "# Retrain with selected features\n",
        "X_train_selected = X_train[top_features]\n",
        "X_test_selected = X_test[top_features]\n",
        "\n",
        "rf_selected = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_selected.fit(X_train_selected, y_train)\n",
        "\n",
        "y_pred_rf_selected = rf_selected.predict(X_test_selected)\n",
        "\n",
        "# Calculate metrics for selected features\n",
        "accuracy_rf_selected = accuracy_score(y_test, y_pred_rf_selected)\n",
        "precision_rf_selected = precision_score(y_test, y_pred_rf_selected)\n",
        "recall_rf_selected = recall_score(y_test, y_pred_rf_selected)\n",
        "f1_rf_selected = f1_score(y_test, y_pred_rf_selected)\n",
        "\n",
        "print(\"\\nRandom Forest (Feature Selected) Performance:\")\n",
        "print(f\"Accuracy:  {accuracy_rf_selected:.4f}\")\n",
        "print(f\"Precision: {precision_rf_selected:.4f}\")\n",
        "print(f\"Recall:    {recall_rf_selected:.4f}\")\n",
        "print(f\"F1-Score:  {f1_rf_selected:.4f}\")\n",
        "\n",
        "print(f\"\\nComparison - Original vs Feature Selected:\")\n",
        "print(f\"Accuracy:  {accuracy_rf_original:.4f} -> {accuracy_rf_selected:.4f} ({accuracy_rf_selected - accuracy_rf_original:+.4f})\")\n",
        "print(f\"Precision: {precision_rf_original:.4f} -> {precision_rf_selected:.4f} ({precision_rf_selected - precision_rf_original:+.4f})\")\n",
        "print(f\"Recall:    {recall_rf_original:.4f} -> {recall_rf_selected:.4f} ({recall_rf_selected - recall_rf_original:+.4f})\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "riHFnu9sRMFb",
        "outputId": "1cefb0b6-d15f-4677-8786-e608785ce5e4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " STEP 3: Manual Feature Selection\n",
            "--------------------------------------------------\n",
            "Feature Importances:\n",
            "                    Feature  Importance\n",
            "1                   Glucose    0.277013\n",
            "5                       BMI    0.163056\n",
            "6  DiabetesPedigreeFunction    0.123334\n",
            "7                       Age    0.119859\n",
            "2             BloodPressure    0.087446\n",
            "0               Pregnancies    0.082304\n",
            "4                   Insulin    0.074398\n",
            "3             SkinThickness    0.072591\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Top 5 features selected: ['Glucose', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'BloodPressure']\n",
            "\n",
            "Random Forest (Feature Selected) Performance:\n",
            "Accuracy:  0.7597\n",
            "Precision: 0.6735\n",
            "Recall:    0.6111\n",
            "F1-Score:  0.6408\n",
            "\n",
            "Comparison - Original vs Feature Selected:\n",
            "Accuracy:  0.7597 -> 0.7597 (+0.0000)\n",
            "Precision: 0.6809 -> 0.6735 (-0.0074)\n",
            "Recall:    0.5926 -> 0.6111 (+0.0185)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. PCA for Feature Selection\n",
        "print(\"\\n STEP 4: PCA for Dimensionality Reduction\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "# Standardize the data for PCA\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Apply PCA\n",
        "pca = PCA()\n",
        "X_train_pca = pca.fit_transform(X_train_scaled)\n",
        "X_test_pca = pca.transform(X_test_scaled)\n",
        "\n",
        "# Explained variance\n",
        "explained_variance = pca.explained_variance_ratio_\n",
        "cumulative_variance = np.cumsum(explained_variance)\n",
        "\n",
        "print(\"PCA Explained Variance:\")\n",
        "for i, (var, cum_var) in enumerate(zip(explained_variance, cumulative_variance)):\n",
        "    print(f\"PC{i+1}: {var:.4f} ({cum_var:.4f} cumulative)\")\n",
        "\n",
        "# Visualize explained variance\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.bar(range(1, len(explained_variance) + 1), explained_variance, alpha=0.7)\n",
        "plt.xlabel('Principal Components')\n",
        "plt.ylabel('Explained Variance Ratio')\n",
        "plt.title('PCA - Individual Explained Variance')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(range(1, len(cumulative_variance) + 1), cumulative_variance, marker='o')\n",
        "plt.xlabel('Number of Principal Components')\n",
        "plt.ylabel('Cumulative Explained Variance')\n",
        "plt.title('PCA - Cumulative Explained Variance')\n",
        "plt.axhline(y=0.95, color='r', linestyle='--', label='95% variance')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Use PCA to reduce to components that explain 95% of variance\n",
        "pca_95 = PCA(n_components=0.95)\n",
        "X_train_pca_95 = pca_95.fit_transform(X_train_scaled)\n",
        "X_test_pca_95 = pca_95.transform(X_test_scaled)\n",
        "\n",
        "print(f\"\\nOriginal features: {X_train_scaled.shape[1]}\")\n",
        "print(f\"PCA features (95% variance): {X_train_pca_95.shape[1]}\")\n",
        "\n",
        "# Train Random Forest with PCA features\n",
        "rf_pca = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rf_pca.fit(X_train_pca_95, y_train)\n",
        "\n",
        "y_pred_rf_pca = rf_pca.predict(X_test_pca_95)\n",
        "\n",
        "# Calculate metrics for PCA\n",
        "accuracy_rf_pca = accuracy_score(y_test, y_pred_rf_pca)\n",
        "precision_rf_pca = precision_score(y_test, y_pred_rf_pca)\n",
        "recall_rf_pca = recall_score(y_test, y_pred_rf_pca)\n",
        "f1_rf_pca = f1_score(y_test, y_pred_rf_pca)\n",
        "\n",
        "print(\"\\nRandom Forest (PCA) Performance:\")\n",
        "print(f\"Accuracy:  {accuracy_rf_pca:.4f}\")\n",
        "print(f\"Precision: {precision_rf_pca:.4f}\")\n",
        "print(f\"Recall:    {recall_rf_pca:.4f}\")\n",
        "print(f\"F1-Score:  {f1_rf_pca:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "XcCUwXc8RRsP",
        "outputId": "24fcdd5e-7f40-411c-ed55-aa519d722359"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " STEP 4: PCA for Dimensionality Reduction\n",
            "--------------------------------------------------\n",
            "PCA Explained Variance:\n",
            "PC1: 0.2789 (0.2789 cumulative)\n",
            "PC2: 0.2072 (0.4860 cumulative)\n",
            "PC3: 0.1353 (0.6213 cumulative)\n",
            "PC4: 0.1059 (0.7272 cumulative)\n",
            "PC5: 0.0987 (0.8259 cumulative)\n",
            "PC6: 0.0681 (0.8939 cumulative)\n",
            "PC7: 0.0582 (0.9521 cumulative)\n",
            "PC8: 0.0479 (1.0000 cumulative)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Original features: 8\n",
            "PCA features (95% variance): 7\n",
            "\n",
            "Random Forest (PCA) Performance:\n",
            "Accuracy:  0.7078\n",
            "Precision: 0.5818\n",
            "Recall:    0.5926\n",
            "F1-Score:  0.5872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Part 2: Comparing with Other Classifiers\n",
        "print(\"\\n\" + \"=\" * 70)\n",
        "print(\"PART 2: Comparing with Other Classifiers\")\n",
        "print(\"=\" * 70)\n",
        "\n",
        "# 1. Train Logistic Regression and Naïve Bayes\n",
        "print(\"\\n STEP 1: Training Additional Classifiers\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "# Logistic Regression\n",
        "print(\"Training Logistic Regression...\")\n",
        "lr = LogisticRegression(random_state=42, max_iter=1000)\n",
        "lr.fit(X_train_scaled, y_train)  # Using scaled data for better performance\n",
        "y_pred_lr = lr.predict(X_test_scaled)\n",
        "\n",
        "accuracy_lr = accuracy_score(y_test, y_pred_lr)\n",
        "precision_lr = precision_score(y_test, y_pred_lr)\n",
        "recall_lr = recall_score(y_test, y_pred_lr)\n",
        "f1_lr = f1_score(y_test, y_pred_lr)\n",
        "\n",
        "# Naïve Bayes\n",
        "print(\"Training Naïve Bayes...\")\n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train, y_train)  # Naïve Bayes doesn't require scaling\n",
        "y_pred_nb = nb.predict(X_test)\n",
        "\n",
        "accuracy_nb = accuracy_score(y_test, y_pred_nb)\n",
        "precision_nb = precision_score(y_test, y_pred_nb)\n",
        "recall_nb = recall_score(y_test, y_pred_nb)\n",
        "f1_nb = f1_score(y_test, y_pred_nb)\n",
        "\n",
        "print(\"\\nLogistic Regression Performance:\")\n",
        "print(f\"Accuracy:  {accuracy_lr:.4f}\")\n",
        "print(f\"Precision: {precision_lr:.4f}\")\n",
        "print(f\"Recall:    {recall_lr:.4f}\")\n",
        "print(f\"F1-Score:  {f1_lr:.4f}\")\n",
        "\n",
        "print(\"\\nNaïve Bayes Performance:\")\n",
        "print(f\"Accuracy:  {accuracy_nb:.4f}\")\n",
        "print(f\"Precision: {precision_nb:.4f}\")\n",
        "print(f\"Recall:    {recall_nb:.4f}\")\n",
        "print(f\"F1-Score:  {f1_nb:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8_1ZnKCRYkw",
        "outputId": "ca924d17-3f9c-4a65-b179-58a70eec150e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================================================================\n",
            "PART 2: Comparing with Other Classifiers\n",
            "======================================================================\n",
            "\n",
            " STEP 1: Training Additional Classifiers\n",
            "--------------------------------------------------\n",
            "Training Logistic Regression...\n",
            "Training Naïve Bayes...\n",
            "\n",
            "Logistic Regression Performance:\n",
            "Accuracy:  0.7078\n",
            "Precision: 0.6000\n",
            "Recall:    0.5000\n",
            "F1-Score:  0.5455\n",
            "\n",
            "Naïve Bayes Performance:\n",
            "Accuracy:  0.6948\n",
            "Precision: 0.5593\n",
            "Recall:    0.6111\n",
            "F1-Score:  0.5841\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Compare Model Performance\n",
        "print(\"\\n STEP 2: Comprehensive Model Comparison\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "# Create comparison table\n",
        "comparison_data = {\n",
        "    'Model': [\n",
        "        'Random Forest (Original)',\n",
        "        'Random Forest (Feature Selected)',\n",
        "        'Random Forest (PCA)',\n",
        "        'Logistic Regression',\n",
        "        'Naïve Bayes'\n",
        "    ],\n",
        "    'Accuracy': [\n",
        "        accuracy_rf_original,\n",
        "        accuracy_rf_selected,\n",
        "        accuracy_rf_pca,\n",
        "        accuracy_lr,\n",
        "        accuracy_nb\n",
        "    ],\n",
        "    'Precision': [\n",
        "        precision_rf_original,\n",
        "        precision_rf_selected,\n",
        "        precision_rf_pca,\n",
        "        precision_lr,\n",
        "        precision_nb\n",
        "    ],\n",
        "    'Recall': [\n",
        "        recall_rf_original,\n",
        "        recall_rf_selected,\n",
        "        recall_rf_pca,\n",
        "        recall_lr,\n",
        "        recall_nb\n",
        "    ],\n",
        "    'F1-Score': [\n",
        "        f1_rf_original,\n",
        "        f1_rf_selected,\n",
        "        f1_rf_pca,\n",
        "        f1_lr,\n",
        "        f1_nb\n",
        "    ]\n",
        "}\n",
        "\n",
        "comparison_df = pd.DataFrame(comparison_data)\n",
        "print(\"\\nModel Performance Comparison:\")\n",
        "print(comparison_df.to_string(index=False))\n",
        "\n",
        "# Visual comparison\n",
        "metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']\n",
        "models = comparison_df['Model'].tolist()\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "for i, metric in enumerate(metrics):\n",
        "    plt.subplot(2, 2, i+1)\n",
        "    values = comparison_df[metric]\n",
        "    bars = plt.bar(models, values, color=plt.cm.Set3(np.linspace(0, 1, len(models))))\n",
        "    plt.title(f'Model Comparison - {metric}')\n",
        "    plt.ylabel(metric)\n",
        "    plt.xticks(rotation=45, ha='right')\n",
        "    plt.ylim(0, 1)\n",
        "\n",
        "    # Add value labels on bars\n",
        "    for bar, value in zip(bars, values):\n",
        "        plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
        "                f'{value:.3f}', ha='center', va='bottom', fontsize=9)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Confusion Matrices\n",
        "print(\"\\nConfusion Matrices for All Models\")\n",
        "print(\"-\" * 50)\n",
        "\n",
        "models_confusion = {\n",
        "    'RF Original': y_pred_rf_original,\n",
        "    'RF Selected': y_pred_rf_selected,\n",
        "    'RF PCA': y_pred_rf_pca,\n",
        "    'Logistic Regression': y_pred_lr,\n",
        "    'Naïve Bayes': y_pred_nb\n",
        "}\n",
        "\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i, (name, y_pred) in enumerate(models_confusion.items()):\n",
        "    plt.subplot(1, 5, i+1)\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
        "    plt.title(f'Confusion Matrix - {name}')\n",
        "    plt.ylabel('Actual')\n",
        "    plt.xlabel('Predicted')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JI61OEd0RdJM",
        "outputId": "ce85a48e-7f9e-45d2-eb32-81c0983d4d71"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " STEP 2: Comprehensive Model Comparison\n",
            "--------------------------------------------------\n",
            "\n",
            "Model Performance Comparison:\n",
            "                           Model  Accuracy  Precision   Recall  F1-Score\n",
            "        Random Forest (Original)  0.759740   0.680851 0.592593  0.633663\n",
            "Random Forest (Feature Selected)  0.759740   0.673469 0.611111  0.640777\n",
            "             Random Forest (PCA)  0.707792   0.581818 0.592593  0.587156\n",
            "             Logistic Regression  0.707792   0.600000 0.500000  0.545455\n",
            "                     Naïve Bayes  0.694805   0.559322 0.611111  0.584071\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Confusion Matrices for All Models\n",
            "--------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x400 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary and Analysis\n",
        "print(\"\\n\" + \"=\" * 70)\n",
        "print(\"📋 SUMMARY AND ANALYSIS\")\n",
        "print(\"=\" * 70)\n",
        "\n",
        "# Find best model for each metric\n",
        "best_accuracy = comparison_df.loc[comparison_df['Accuracy'].idxmax()]\n",
        "best_precision = comparison_df.loc[comparison_df['Precision'].idxmax()]\n",
        "best_recall = comparison_df.loc[comparison_df['Recall'].idxmax()]\n",
        "best_f1 = comparison_df.loc[comparison_df['F1-Score'].idxmax()]\n",
        "\n",
        "print(\"\\nBest Performing Models:\")\n",
        "print(f\"Accuracy:  {best_accuracy['Model']} ({best_accuracy['Accuracy']:.4f})\")\n",
        "print(f\"Precision: {best_precision['Model']} ({best_precision['Precision']:.4f})\")\n",
        "print(f\"Recall:    {best_recall['Model']} ({best_recall['Recall']:.4f})\")\n",
        "print(f\"F1-Score:  {best_f1['Model']} ({best_f1['F1-Score']:.4f})\")\n",
        "\n",
        "print(f\"\\nKey Insights:\")\n",
        "print(f\"1. Feature Selection Impact: Original vs Feature Selected RF\")\n",
        "print(f\"   - Accuracy change: {accuracy_rf_selected - accuracy_rf_original:+.4f}\")\n",
        "print(f\"   - Number of features reduced: {X_train.shape[1]} -> {len(top_features)}\")\n",
        "\n",
        "print(f\"\\n2. PCA Impact: Original vs PCA RF\")\n",
        "print(f\"   - Accuracy change: {accuracy_rf_pca - accuracy_rf_original:+.4f}\")\n",
        "print(f\"   - Dimensions reduced: {X_train.shape[1]} -> {X_train_pca_95.shape[1]}\")\n",
        "\n",
        "print(f\"\\n3. Algorithm Comparison:\")\n",
        "print(f\"   - Best overall model: {comparison_df.loc[comparison_df['F1-Score'].idxmax()]['Model']}\")\n",
        "print(f\"   - Simple models (LR, NB) performance compared to ensemble methods\")\n",
        "\n",
        "print(f\"\\n4. Feature Importance Analysis:\")\n",
        "print(f\"   - Most important feature: {feature_importance_df.iloc[0]['Feature']} ({feature_importance_df.iloc[0]['Importance']:.4f})\")\n",
        "print(f\"   - Least important feature: {feature_importance_df.iloc[-1]['Feature']} ({feature_importance_df.iloc[-1]['Importance']:.4f})\")\n",
        "\n",
        "print(f\"Activity Completed Successfully!\")\n",
        "print(f\"Deliverables generated:\")\n",
        "print(f\"   - Complete implementation with all required models\")\n",
        "print(f\"   - Feature selection analysis (manual + PCA)\")\n",
        "print(f\"   - Comprehensive model comparison\")\n",
        "print(f\"   - Visualizations and performance metrics\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "csfvdLNDRjeG",
        "outputId": "fbaa9ea8-d44e-4986-a50c-9ff811c367c4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================================================================\n",
            "📋 SUMMARY AND ANALYSIS\n",
            "======================================================================\n",
            "\n",
            "Best Performing Models:\n",
            "Accuracy:  Random Forest (Original) (0.7597)\n",
            "Precision: Random Forest (Original) (0.6809)\n",
            "Recall:    Random Forest (Feature Selected) (0.6111)\n",
            "F1-Score:  Random Forest (Feature Selected) (0.6408)\n",
            "\n",
            "Key Insights:\n",
            "1. Feature Selection Impact: Original vs Feature Selected RF\n",
            "   - Accuracy change: +0.0000\n",
            "   - Number of features reduced: 8 -> 5\n",
            "\n",
            "2. PCA Impact: Original vs PCA RF\n",
            "   - Accuracy change: -0.0519\n",
            "   - Dimensions reduced: 8 -> 7\n",
            "\n",
            "3. Algorithm Comparison:\n",
            "   - Best overall model: Random Forest (Feature Selected)\n",
            "   - Simple models (LR, NB) performance compared to ensemble methods\n",
            "\n",
            "4. Feature Importance Analysis:\n",
            "   - Most important feature: Glucose (0.2770)\n",
            "   - Least important feature: SkinThickness (0.0726)\n",
            "Activity Completed Successfully!\n",
            "Deliverables generated:\n",
            "   - Complete implementation with all required models\n",
            "   - Feature selection analysis (manual + PCA)\n",
            "   - Comprehensive model comparison\n",
            "   - Visualizations and performance metrics\n"
          ]
        }
      ]
    }
  ]
}