ML_11.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g44sPEt-eXj-",
        "outputId": "c91f139b-f0f4-4ec2-aa75-e5619e34175f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Accuracy: 0.9366197183098591\n",
            "Test Accuracy: 1.0\n"
          ]
        }
      ],
      "source": [
        "from sklearn.datasets import load_wine\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Load the wine dataset\n",
        "wine = load_wine()\n",
        "X = wine.data\n",
        "y = wine.target\n",
        "\n",
        "# Perform PCA to reduce dimensionality to 5 components\n",
        "pca = PCA(n_components=5)\n",
        "X_pca = pca.fit_transform(X)\n",
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Train the Gaussian Naive Bayes classifier\n",
        "model = GaussianNB()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict the classes for training and testing sets\n",
        "y_train_pred = model.predict(X_train)\n",
        "y_test_pred = model.predict(X_test)\n",
        "\n",
        "# Calculate training and testing accuracies\n",
        "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "print(\"Training Accuracy:\", train_accuracy)\n",
        "print(\"Test Accuracy:\", test_accuracy)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3F86dk5FelZ_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}