SVM_ML_Assignment_3.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Step a: Data Preprocessing\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "\n",
        "# Load dataset\n",
        "data_url = \"/content/wdbc.data\"\n",
        "df = pd.read_csv(data_url)\n",
        "\n",
        "# Handle missing values\n",
        "# Encode categorical data if any\n",
        "# Split the data into features and target variable\n",
        "\n",
        "# Step b: Feature Selection (if necessary)\n",
        "# Explore and analyze features, perform correlation analysis, and feature selection techniques\n",
        "\n",
        "# Step c: SVM Model Training\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Instantiate and train the SVM model\n",
        "svm_model = SVC(kernel='linear', C=1.0)\n",
        "svm_model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Step d: Model Evaluation\n",
        "# Make predictions\n",
        "y_pred = svm_model.predict(X_test_scaled)\n",
        "\n",
        "# Evaluate performance\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",
        "\n",
        "# Confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Print results\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xaDwK3001piI",
        "outputId": "79531ca0-cb16-4f2c-f1df-41c6d028bbcc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9561\n",
            "Precision: 0.9714\n",
            "Recall: 0.9577\n",
            "F1 Score: 0.9645\n",
            "Confusion Matrix:\n",
            " [[41  2]\n",
            " [ 3 68]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Assuming y_pred contains your model's predictions on the test set\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHUO3uel11NY",
        "outputId": "49942f20-59cf-4e3c-8ea1-46a9cea768ed"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.95      0.94        43\n",
            "           1       0.97      0.96      0.96        71\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.95      0.96      0.95       114\n",
            "weighted avg       0.96      0.96      0.96       114\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Below codes are same but the using inbuilt Sklearn Cancer dataset**"
      ],
      "metadata": {
        "id": "SGVNdRVt5U0n"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "qtK_Fy1qRmZy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dea7c8d0-a6bb-46c2-d463-c3efe746d541"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9561\n",
            "Precision: 0.9714\n",
            "Recall: 0.9577\n",
            "F1 Score: 0.9645\n",
            "Confusion Matrix:\n",
            " [[41  2]\n",
            " [ 3 68]]\n"
          ]
        }
      ],
      "source": [
        "# Step a: Data Preprocessing\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "\n",
        "# Load breast cancer dataset from sklearn\n",
        "data = load_breast_cancer()\n",
        "features = pd.DataFrame(data.data, columns=data.feature_names)\n",
        "target = pd.Series(data.target, name=\"target\")\n",
        "\n",
        "# Step b: Feature Selection (if necessary)\n",
        "# Explore and analyze features, perform correlation analysis, and feature selection techniques\n",
        "\n",
        "# Step c: SVM Model Training\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Instantiate and train the SVM model\n",
        "svm_model = SVC(kernel='linear', C=1.0)\n",
        "svm_model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Step d: Model Evaluation\n",
        "# Make predictions\n",
        "y_pred = svm_model.predict(X_test_scaled)\n",
        "\n",
        "# Evaluate performance\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",
        "\n",
        "# Confusion matrix\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Print results\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Precision: {precision:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "# Assuming y_pred contains your model's predictions on the test set\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTymqW0aVgYg",
        "outputId": "746be0fd-ee4f-40d7-c501-b3d5d422b2c7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.95      0.94        43\n",
            "           1       0.97      0.96      0.96        71\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.95      0.96      0.95       114\n",
            "weighted avg       0.96      0.96      0.96       114\n",
            "\n"
          ]
        }
      ]
    }
  ]
}