practical ml, R - rextester

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library(randomForest)
library(caret)
URL1 <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
URL2 <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
filename1 <- "pml-training.csv"
filename2 <- "pml-testing.csv"
download.file(url=URL1, destfile=filename1,method="curl")
download.file(url=URL2, destfile=filename2,method="curl")
training <- read.csv("pml-training.csv",row.names=1,na.strings = "")
testing <- read.csv("pml-testing.csv",row.names=1,na.strings = "NA")
nsv <- nearZeroVar(training,saveMetrics=TRUE)
training <- training[,!nsv$nzv]
testing <- testing[,!nsv$nzv]

# Remove variables with missing values
training_filter_na <- training[,(colSums(is.na(training)) == 0)]
testing_filter_na <- testing[,(colSums(is.na(testing)) == 0)]

# Remove unnecessary columns
colRm_train <- c("user_name","raw_timestamp_part_1","raw_timestamp_part_2","cvtd_timestamp","num_window")
colRm_test <- c("user_name","raw_timestamp_part_1","raw_timestamp_part_2","cvtd_timestamp","num_window","problem_id")
training_colRm <- training_filter_na[,!(names(training_filter_na) %in% colRm_train)]
testing_colRm <- testing_filter_na[,!(names(testing_filter_na) %in% colRm_test)]
dim(training_colRm)
dim(testing_colRm)
inTrain <- createDataPartition(y=training$classe, p=0.7, list=FALSE)
training_clean <- training_colRm[inTrain,]
validation_clean <- training_colRm[-inTrain,]
cor <- abs(sapply(colnames(training_clean[, -ncol(training)]), function(x) cor(as.numeric(training_clean[, x]), as.numeric(training_clean$classe), method = "spearman")))
set.seed(1234)
# Fit rf model
rfFit <- train(classe ~ ., method = "rf", data = training_clean, importance = T, trControl = trainControl(method = "cv", number = 4))
validation_pred <- predict(rfFit, newdata=validation_clean)
# Check model performance
confusionMatrix(validation_pred,validation_clean$classe)

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