PracticalMachine/Writeup.R at master · shrikant2002/PracticalMachine · GitHub

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install.packages("caret")
install.packages("lattice")
#install.packages("randomForest")
#install.packages("reshape2")
#install.packages("corrplot")
#install.packages("psych")


library(caret)
library(lattice)
library(psych)
library(ggplot2)
library(reshape2)
library(randomForest)
library(corrplot)
train<-read.csv(paste0("./pml-training.csv"),stringsAsFactor=FALSE,skip = 0,fill=NA,comment.char="#")
test<-read.csv(paste0("pml-testing.csv"))

dim(train)

var<-names(train)[apply(train,2,function(x) table(is.na(x))[1]==19622)]
train2<-train[,var]
test2<-test[,var[-length(var)]]


var2<-melt(apply(train2,2,function(x) sum(ifelse(x=="",1,0)))==0)
select.var<-rownames(var2)[var2$value==TRUE]
train3<-train2[,select.var]
test3<-test2[,select.var[-length(select.var)]]
train4<-train3[,names(train3[-c(1:7,length(train3))])]      # only considering numeric variable from HAR sensor
test4<-test3[,names(test3[-c(1:7)])]


correlations <- cor(train4)                                 # finding correlations
corrplot(correlations,order = "hclust",tl.cex = .5)


highCorr <- findCorrelation(correlations, cutoff = .75)     # finding variables with high correlation
predictor <- train4[, -highCorr]                            # dataframe of train predictors
filtered.test4 <- test4[, -highCorr]                        # dataframe of test predictors
classe<-train3$classe                                       # target variable
trainData<-cbind(classe,predictor)                          # training dataset ready for prediction


rfModel <- randomForest(classe ~ .,data = trainData,importance = TRUE,ntrees = 10)
print(rfModel)


confusion<-rfModel$confusion
sensitivity<-(confusion[2,2]/(confusion[2,2]+confusion[2,1]))*100
specificity<-(confusion[1,1]/(confusion[1,1]+confusion[1,2]))*100
overall_error<-rfModel$err.rate[length(rfModel$err.rate[,1]),1]*100
overall_accuracy<-1-overall_error

out.test<-predict(rfModel,filtered.test4)


setwd(paste0(file.loc,"submission files"))
answers<- as.vector(out.test)

pml_write_files = function(x){
  n = length(x)
  for(i in 1:n){
    filename = paste0("problem_id_",i,".txt")
    write.table(x[i],file=filename,quote=FALSE,row.names=FALSE,col.names=FALSE)
  }
}

pml_write_files(answers)