Exp 2.4 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bb4e49b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6      148             72             35        0  33.6   \n",
      "1            1       85             66             29        0  26.6   \n",
      "2            8      183             64              0        0  23.3   \n",
      "3            1       89             66             23       94  28.1   \n",
      "4            0      137             40             35      168  43.1   \n",
      "\n",
      "   DiabetesPedigreeFunction  Age  Outcome  \n",
      "0                     0.627   50        1  \n",
      "1                     0.351   31        0  \n",
      "2                     0.672   32        1  \n",
      "3                     0.167   21        0  \n",
      "4                     2.288   33        1  \n",
      "\n",
      "Logistic Regression Performance:\n",
      "Accuracy: 0.7662\n",
      "Precision: 0.7111\n",
      "Recall: 0.5818\n",
      "F1 Score: 0.6400\n",
      "\n",
      "Decision Tree Performance:\n",
      "Accuracy: 0.7468\n",
      "Precision: 0.6250\n",
      "Recall: 0.7273\n",
      "F1 Score: 0.6723\n",
      "\n",
      "Random Forest Performance:\n",
      "Accuracy: 0.7273\n",
      "Precision: 0.6102\n",
      "Recall: 0.6545\n",
      "F1 Score: 0.6316\n",
      "\n",
      "SVM Performance:\n",
      "Accuracy: 0.7468\n",
      "Precision: 0.6600\n",
      "Recall: 0.6000\n",
      "F1 Score: 0.6286\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.tree import export_text\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file_path = \"diabetes.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "print(df.head())\n",
    "\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "df[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']] = imputer.fit_transform(df[['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']])\n",
    "\n",
    "# Scaling numerical features\n",
    "scaler = MinMaxScaler()\n",
    "df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']] = scaler.fit_transform(df[['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']])\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X = df.drop('Outcome', axis=1)\n",
    "y = df['Outcome']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define and train models\n",
    "models = {\n",
    "    'Logistic Regression': LogisticRegression(),\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'SVM': SVC()\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\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",
    "    print(f\"\\n{name} Performance:\")\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",
    "\n",
    "\n",
    "rf_model = models['Random Forest']\n",
    "feature_importances = rf_model.feature_importances_\n",
    "\n",
    "feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': feature_importances})\n",
    "feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\n",
    "plt.title('Feature Importance')\n",
    "plt.xlabel('Importance')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "643ab88e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}