{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Perform a practical SVM algorithm"
      ],
      "metadata": {
        "id": "qLjOlWvs1xfA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "\n",
        "# Load a sample dataset (e.g., the Iris dataset)\n",
        "iris = datasets.load_iris()\n",
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Create an SVM classifier\n",
        "svm_classifier = SVC(kernel='linear') # You can change the kernel as needed\n",
        "\n",
        "# Train the classifier\n",
        "svm_classifier.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test set\n",
        "y_pred = svm_classifier.predict(X_test)\n",
        "\n",
        "# Evaluate the classifier\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "report = classification_report(y_test, y_pred)\n",
        "\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "print(\"Classification Report:\\n\", report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4l1cI4V1yid",
        "outputId": "51b848db-6c11-4db9-b66f-cccf6f08c0bb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        19\n",
            "           1       1.00      1.00      1.00        13\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "73fadf5f",
        "outputId": "88a45c83-97e3-4653-c394-f5b667138992"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Generate the confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=iris.target_names, yticklabels=iris.target_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}