aikit-sklearn-demo.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fdb4026",
   "metadata": {},
   "source": [
    "# Intel extension for Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64f66056",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#Intel(R) Extension for Scikit-learn dynamically patches scikit-learn estimators to use oneDAL as the underlying solver\n",
    "from sklearnex import patch_sklearn\n",
    "patch_sklearn()\n",
    "\n",
    "# Import datasets, Naive Bayes classifier and performance metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d9ec649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset: https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "201f71e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('heart_2020_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "677a7989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(319795, 18)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e172d821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     292422\n",
       "Yes     27373\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HeartDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b017a4fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['HeartDisease'] = df['HeartDisease'].map({'Yes': 1, 'No': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "727f6777",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = df.select_dtypes(include='object').columns.tolist()\n",
    "\n",
    "df = pd.get_dummies(df, columns=cat_cols)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11567964",
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = df.drop('HeartDisease', axis=1), df['HeartDisease']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44a2a022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest Classifier Accuracy: 0.9088669026504397\n"
     ]
    }
   ],
   "source": [
    "reg = RandomForestClassifier(random_state=42)\n",
    "reg.fit(X_train, y_train)\n",
    "preds = reg.predict(X_test)\n",
    "\n",
    "print(\"RandomForest Classifier Accuracy:\",accuracy_score(y_test, preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2de0922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN Classifier Accuracy: 0.9064903876221091\n"
     ]
    }
   ],
   "source": [
    "reg = KNeighborsClassifier()\n",
    "reg.fit(X_train, y_train)\n",
    "preds = reg.predict(X_test)\n",
    "\n",
    "print(\"KNN Classifier Accuracy:\",accuracy_score(y_test, preds))"
   ]
  }
 ],
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