download.file(url = "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv",

destfile = "pml-training.csv")

download.file(url = "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv",

destfile = "pml-testing.csv")

pmlTraining <- read.csv("pml-training.csv")

pmlTesting <- read.csv("pml-testing.csv")

dim(pmlTraining)

## [1] 19622 160

dim(pmlTesting)

## [1] 20 160

View(pmlTraining)

ProcessData <- function (dataframe){

col <- vector(mode = "numeric")

for(i in 1:ncol(dataframe))

{

if(colnames(dataframe)[i]=='classe'){next;}

total <- length(dataframe[,i]);

NAs <- length(dataframe[is.na(dataframe[,i]),i]);

empty <- length(dataframe[dataframe[,i]=='',i])

notNumber <- length(dataframe[!is.numeric(dataframe[,i]),i]);

if(round(NAs/total)==1 | round(empty/total)==1 | round(notNumber/total)==1){

col <- rbind(col, i)

}

}

process <- dataframe[,as.numeric(col)*(-1)]

for(i in 1:(ncol(process)-1)){ process[,i] <- as.numeric(process[,i]); }

process[,c(1,2,3,4)*(-1)]

}

cleanedData <- ProcessData(pmlTraining)

dim(cleanedData)

## [1] 19622 53

library(caret)

inTrain <- createDataPartition(y = cleanedData$classe, p = 0.7, list = FALSE)

trainSet <- cleanedData[inTrain,]

validationSet <- cleanedData[-inTrain,]

dim(trainSet); dim(validationSet)

## [1] 13737 53

## [1] 5885 53

library(caret)

fit <- train(classe ~ ., data = trainSet, method = 'rf')

library(randomForest)

fit <- randomForest(classe ~ ., data = trainSet)

predictResult <- predict(fit, validationSet[,-53])

confusionMatrix(validationSet[,53], predictResult)

## Confusion Matrix and Statistics

##

## Reference

## Prediction A B C D E

## A 1673 1 0 0 0

## B 8 1128 3 0 0

## C 0 8 1014 4 0

## D 0 0 8 956 0

## E 0 0 0 1 1081

##

## Overall Statistics

##

## Accuracy : 0.994

## 95% CI : (0.992, 0.996)

## No Information Rate : 0.286

## P-Value [Acc > NIR] : <2e-16

##

## Kappa : 0.993

## Mcnemar's Test P-Value : NA

##

## Statistics by Class:

##

## Class: A Class: B Class: C Class: D Class: E

## Sensitivity 0.995 0.992 0.989 0.995 1.000

## Specificity 1.000 0.998 0.998 0.998 1.000

## Pos Pred Value 0.999 0.990 0.988 0.992 0.999

## Neg Pred Value 0.998 0.998 0.998 0.999 1.000

## Prevalence 0.286 0.193 0.174 0.163 0.184

## Detection Rate 0.284 0.192 0.172 0.162 0.184

## Detection Prevalence 0.284 0.194 0.174 0.164 0.184

## Balanced Accuracy 0.998 0.995 0.993 0.997 1.000

cleanedTest <- ProcessData(pmlTesting)

predict(fit, cleanedTest)

## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

## B A B A A E D B A A B C B A E E A B B B

## Levels: A B C D E