Stacking and Blending.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24fc4d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fbb48475",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day68-stacking-and-blending/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "465c539e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
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       "      <td>145</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
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       "      <td>120</td>\n",
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       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ae58207",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['target'])\n",
    "y = df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dce293fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "041ac8b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(242, 13)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cde946b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "140849ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [\n",
    "    ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),\n",
    "    ('knn', KNeighborsClassifier(n_neighbors=10)),\n",
    "    ('gbdt',GradientBoostingClassifier())\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98eef49f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "clf = StackingClassifier(\n",
    "    estimators=estimators, \n",
    "    final_estimator=LogisticRegression(),\n",
    "    cv=10\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ba7eb3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StackingClassifier(cv=10,\n",
       "                   estimators=[('rf',\n",
       "                                RandomForestClassifier(n_estimators=10,\n",
       "                                                       random_state=42)),\n",
       "                               ('knn', KNeighborsClassifier(n_neighbors=10)),\n",
       "                               ('gbdt', GradientBoostingClassifier())],\n",
       "                   final_estimator=LogisticRegression())"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da94152d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d692dc2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "028e0996",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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