diabetespredictor.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/",
          "height": 718
        },
        "id": "RwF90HE0pY1J",
        "outputId": "0c469452-6bcb-417a-e5ad-f9a8515a2f08"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            " [[140  17]\n",
            " [ 34  40]]\n",
            "Accuracy: 0.7792207792207793\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.89      0.85       157\n",
            "           1       0.70      0.54      0.61        74\n",
            "\n",
            "    accuracy                           0.78       231\n",
            "   macro avg       0.75      0.72      0.73       231\n",
            "weighted avg       0.77      0.78      0.77       231\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",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
        "\n",
        "# Load the dataset\n",
        "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv\"\n",
        "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
        "dataset = pd.read_csv(url, names=names)\n",
        "\n",
        "# Split dataset into features and target variable\n",
        "X = dataset.drop('class', axis=1)\n",
        "y = dataset['class']\n",
        "\n",
        "# Split dataset into training set and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
        "\n",
        "# Standardize features by removing the mean and scaling to unit variance\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Train the Random Forest classifier\n",
        "classifier = RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predict the test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "# Evaluate model\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_pred))\n",
        "\n",
        "# Visualize the feature importances\n",
        "importances = classifier.feature_importances_\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "plt.figure()\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.bar(range(X.shape[1]), importances[indices], align=\"center\")\n",
        "plt.xticks(range(X.shape[1]), X.columns[indices], rotation=90)\n",
        "plt.xlim([-1, X.shape[1]])\n",
        "plt.show()\n"
      ]
    }
  ]
}