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bf6747a8 · Sanghoon · bb485387 · bf6747a8
Commit bf6747a8 authored May 04, 2022 by Sanghoon
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--- 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
+{
+ "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
+}