Naive_Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Naive Bayes is a supervised machine learning algorithm that excels at classification tasks. It works by calculating the probability of a data point belonging to a particular class based on Bayes' theorem. In disease prediction, it estimates the likelihood of someone having a disease given their symptoms (attributes). While it assumes conditional independence between features (symptoms), it often performs well in practice, especially for large datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "data = pd.read_csv(\"Dataset_cleaned.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = data.drop(\"TenYearCHD\", axis=1)\n",
    "target = data[\"TenYearCHD\"]\n",
    "# Split data into training and testing sets for model evaluation\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Naive Bayes classifier\n",
    "clf = GaussianNB()\n",
    "# Define hyperparameter grid for var_smoothing\n",
    "param_grid = {\n",
    "    'var_smoothing': [1e-9, 1e-8, 1e-7, 1e-6, 1e-5],\n",
    "    'priors': [None, [0.1, 0.9]]\n",
    "}\n",
    "# Perform GridSearchCV for hyperparameter tuning with accuracy as the scoring metric\n",
    "grid_search = GridSearchCV(clf, param_grid, scoring='accuracy')\n",
    "\n",
    "# Train the model with different hyperparameter combinations\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Access the best model and hyperparameters\n",
    "best_model = grid_search.best_estimator_\n",
    "best_params = grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = best_model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate model performance (e.g., accuracy, precision, recall)\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "precision = precision_score(y_test, predictions)\n",
    "recall = recall_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8169398907103825\n",
      "Precision: 0.4032258064516129\n",
      "Recall: 0.20491803278688525\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\", accuracy)\n",
    "print(\"Precision:\", precision)\n",
    "print(\"Recall:\", recall)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}