Supervised_ML_fundamentals_1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest classifier using Python with Iris dataset."
      ],
      "metadata": {
        "id": "CiodpSFGK952"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "zHTk5Z8EnhN7",
        "outputId": "ec45aa06-9d32-416c-cd3c-7429b6080235"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 96.67%\n",
            "Confusion Matrix:\n",
            "[[13  0  0]\n",
            " [ 0  7  0]\n",
            " [ 0  1  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        13\n",
            "  versicolor       0.88      1.00      0.93         7\n",
            "   virginica       1.00      0.90      0.95        10\n",
            "\n",
            "    accuracy                           0.97        30\n",
            "   macro avg       0.96      0.97      0.96        30\n",
            "weighted avg       0.97      0.97      0.97        30\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import necessary libraries\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = load_iris()\n",
        "X, y = iris.data, 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=0.2)\n",
        "\n",
        "# Create a Random Forest classifier\n",
        "clf = RandomForestClassifier(n_estimators=100)\n",
        "\n",
        "# Train the classifier on the training data\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "# Calculate the accuracy of the classifier\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "confusion = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion)\n",
        "\n",
        "classification_rep = classification_report(y_test, y_pred, target_names=iris.target_names)\n",
        "#print(\"Classification Report:\")\n",
        "print(classification_rep)\n",
        "\n",
        "#Visualizing feature importance\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "feature_importance = clf.feature_importances_\n",
        "plt.bar(iris.feature_names, feature_importance)\n",
        "plt.xlabel('Feature')\n",
        "plt.ylabel('Importance')\n",
        "plt.title('Feature Importance')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the resulting bar chart, the height of each bar represents the importance of the corresponding feature. If \"petal length\" has the highest bar, it indicates that it's the most important feature for the model's predictions. This information can be valuable for feature selection and understanding the dataset's characteristics."
      ],
      "metadata": {
        "id": "ZvMSS0alNSRd"
      }
    }
  ]
}