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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bagging Algorithms\n",
+    "\n",
+    "Bootstrap Aggregation or bagging involves taking multiple samples from your training dataset (with replacement) and training a model for each sample.\n",
+    "\n",
+    "The final output prediction is averaged across the predictions of all of the sub-models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importing required Libraries\n",
+    "import pandas\n",
+    "from sklearn import model_selection\n",
+    "from sklearn.ensemble import RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Locating the dataset and creating a variable for the URL\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",
+    "dataframe = pandas.read_csv(url, names=names) #Reading the data from the URL into a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X--> [  6.    148.     72.     35.      0.     33.6     0.627  50.   ]\n",
+      "Y--> 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "array = dataframe.values #Reading the content of the dataframe\n",
+    "X = array[:,0:8] #Creating X array with first 8 elements of the Data in a row - Where X is an independent variable\n",
+    "Y = array[:,8] #Creating Y array with last element of the Data in a row- Where Y is a dependent variable\n",
+    "\n",
+    "print(\"X-->\", X[0])\n",
+    "\n",
+    "print(\"Y-->\", Y[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = 7 #No. of random numbers to be generated\n",
+    "num_trees = 100 #Create 100 random trees\n",
+    "max_features = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Random forest is an extension of bagged decision trees.\n",
+    "\n",
+    "Samples of the training dataset are taken with replacement, but the trees are constructed in a way that reduces the correlation between individual classifiers. Specifically, rather than greedily choosing the best split point in the construction of the tree, only a random subset of features are considered for each split.\n",
+    "\n",
+    "You can construct a Random Forest model for classification using the RandomForestClassifier class.\n",
+    "\n",
+    "The example below provides an example of Random Forest for classification with 100 trees and split points chosen from a random selection of 3 features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7667857142857143\n"
+     ]
+    }
+   ],
+   "source": [
+    "kfold = model_selection.KFold(n_splits=100, random_state=seed) #Cross Validation\n",
+    "model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features) #Building model for Randomforestclassifier\n",
+    "results = model_selection.cross_val_score(model, X, Y, cv=kfold) \n",
+    "print(results.mean())"
+   ]
+  }
+ ],
+ "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",
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+}