Upload New File

bb485387 · Sanghoon · b398cc59 · bb485387
Commit bb485387 authored May 04, 2022 by Sanghoon
Hide whitespace changes
Inline Side-by-side

Showing with 63 additions and 0 deletions

iHealth_Code.R ...yes Classification/Product Recommendations/iHealth_Code.R +63 -0

No files found.
--- a/Crash Course on Naive Bayes Classification/Product Recommendations/iHealth_Code.R
+++ b/Crash Course on Naive Bayes Classification/Product Recommendations/iHealth_Code.R
+# Can Naive Bayes models be used to provide new product recommendations for FitBits
+
+#Prepare a clean R environment in work space.
+rm(list=ls())
+
+#Use setwd() to navigate the data directory and specify desired folder. Here we are using Rstudio Editor directory.
+setwd(dirname(rstudioapi::getSourceEditorContext()$path))
+
+#Import our csv file data
+data=read.csv("iHealthData.csv")
+names(data)=c("Main Interest","Current Exercise Level", "How Motivated", "Comfortable with tech Devices?", "Model #")
+
+
+#Use the NB classifier on test Data
+iHealth_classifier=function(interest,exercise,motivation,comfortlevel){
+  #interest='health'
+  #exercise='moderate'
+  #motivation='moderate'
+  #comfortlevel='yes'
+  
+  vars=c(interest,exercise,motivation,comfortlevel)
+  #data=read.table("i-01.txt")
+  data=read.csv("iHealthData.csv")
+  names(data)=c("Main Interest","Current Exercise Level", "How Motivated", "Comfortable with tech Devices?", "Model #")
+  
+  uniquevec=unique(data$'Model #')  #Unique Model Choices
+  val=length(uniquevec)
+  
+  ProbMat=matrix(0,(dim(data)[2]),val) 
+  
+  #compute probabilities
+  for (i in 1:(dim(ProbMat)[1])){
+    for (j in 1:val){
+      data_cond=subset(data,data$`Model #`==uniquevec[j])  #Model type
+      
+      if (i<dim(ProbMat)[1]){
+        probval=dim(subset(data_cond,data_cond[,i]==vars[i]))[1]/dim(data_cond)[1]}  #Compute probability of model being chosen
+      
+      else
+        
+      {probval=dim(data_cond)[1]/dim(data)[1]}  #Compute marginal probabilities
+      
+      ProbMat[i,j]=probval
+    }
+    
+  }
+  
+  finalvec=apply(ProbMat,2,prod)
+  finalval=which.max(finalvec)
+  choice=uniquevec[finalval]
+  return(list(ProbMat=ProbMat,finalvec=finalvec,choice=choice))
+}
+
+#}
+#data_i100=subset(data,data$`Model #`=='i100')
+
+
+#data_i500=subset(data,data$`Model #`=='i500')}
+
+#Executing function in a sample data set to provide new product recommendations
+Ex1<-iHealth_classifier(interest="both",exercise="active",motivation ="aggressive",comfortlevel = "yes")
+Ex2<-iHealth_classifier(interest="appearance",exercise="sedentary",motivation ="moderate",comfortlevel = "yes")
+