diff --git a/Crash Course on Naive Bayes Classification/Product Recommendations/iHealth_R_code_notebook.ipynb b/Crash Course on Naive Bayes Classification/Product Recommendations/iHealth_R_code_notebook.ipynb
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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Can Naïve Bayes models be used to provide new product recommendations for FitBits?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Prepare a clean R environment in work space.\n",
+    "rm(list=ls())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import our csv file data\n",
+    "data=read.csv(\"iHealthdata.csv\")\n",
+    "names(data)=c(\"Main Interest\",\"Current Exercise Level\", \"How Motivated\", \"Comfortable with tech Devices?\", \"Model #\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "#Use the NB classifier on test Data\n",
+    "iHealth_classifier=function(interest,exercise,motivation,comfortlevel){\n",
+    "  #interest='health'\n",
+    "  #exercise='moderate'\n",
+    "  #motivation='moderate'\n",
+    "  #comfortlevel='yes'\n",
+    "  \n",
+    "  vars=c(interest,exercise,motivation,comfortlevel)\n",
+    "  #data=read.table(\"i-01.txt\")\n",
+    "  data=read.csv(\"iHealthdata.csv\")\n",
+    "  names(data)=c(\"Main Interest\",\"Current Exercise Level\", \"How Motivated\", \"Comfortable with tech Devices?\", \"Model #\")\n",
+    "  \n",
+    "  uniquevec=unique(data$'Model #')  #Unique Model Choices\n",
+    "  val=length(uniquevec)\n",
+    "  \n",
+    "  ProbMat=matrix(0,(dim(data)[2]),val) \n",
+    "  \n",
+    "  #compute probabilities\n",
+    "  for (i in 1:(dim(ProbMat)[1])){\n",
+    "    for (j in 1:val){\n",
+    "      data_cond=subset(data,data$`Model #`==uniquevec[j])  #Model type\n",
+    "      \n",
+    "      if (i<dim(ProbMat)[1]){\n",
+    "        probval=dim(subset(data_cond,data_cond[,i]==vars[i]))[1]/dim(data_cond)[1]}  #Compute probability of model being chosen\n",
+    "      \n",
+    "      else\n",
+    "        \n",
+    "      {probval=dim(data_cond)[1]/dim(data)[1]}  #Compute marginal probabilities\n",
+    "      \n",
+    "      ProbMat[i,j]=probval\n",
+    "    }\n",
+    "    \n",
+    "  }\n",
+    "  \n",
+    "  finalvec=apply(ProbMat,2,prod)\n",
+    "  finalval=which.max(finalvec)\n",
+    "  choice=uniquevec[finalval]\n",
+    "  return(list(ProbMat=ProbMat,finalvec=finalvec,choice=choice))\n",
+    "}\n",
+    "\n",
+    "#}\n",
+    "#data_i100=subset(data,data$`Model #`=='i100')\n",
+    "\n",
+    "\n",
+    "#data_i500=subset(data,data$`Model #`=='i500')}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Executing function in a sample data set to provide new product recommendations\n",
+    "Ex1<-iHealth_classifier(interest=\"both\",exercise=\"active\",motivation =\"aggressive\",comfortlevel = \"yes\")\n",
+    "Ex2<-iHealth_classifier(interest=\"appearance\",exercise=\"sedentary\",motivation =\"moderate\",comfortlevel = \"yes\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}