Final Project Report - Practical Machine Learning Course Prediction Assignment Writeup
Using devices such as Jawbone Up, Nike FuelBand, and Fitbit it is now possible to collect a large amount of data about personal activity relatively inexpensively. These type of devices are part of the quantified self movement - a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. One thing that people regularly do is quantify how much of a particular activity they do, but they rarely quantify how well they do it. In this project, your goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. They were asked to perform barbell lifts correctly and incorrectly in 5 different ways. More information is available from the website here: http://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).
options(warn=-1)
library(caret)## Loading required package: lattice## Loading required package: ggplot2library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(rattle)## Rattle: A free graphical interface for data mining with R.
## Version 4.1.0 Copyright (c) 2006-2015 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(randomForest)## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginlibrary(knitr)set.seed(12345)
trainUrl <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
testUrl <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
training <- read.csv(url(trainUrl), na.strings=c("NA","#DIV/0!",""))
testing <- read.csv(url(testUrl), na.strings=c("NA","#DIV/0!",""))inTrain <- createDataPartition(training$classe, p=0.6, list=FALSE)
myTraining <- training[inTrain, ]
myTesting <- training[-inTrain, ]
dim(myTraining); dim(myTesting)## [1] 11776 160## [1] 7846 160nzv <- nearZeroVar(myTraining, saveMetrics=TRUE)
myTraining <- myTraining[,nzv$nzv==FALSE]
nzv<- nearZeroVar(myTesting,saveMetrics=TRUE)
myTesting <- myTesting[,nzv$nzv==FALSE]Remove the first column of the myTraining data set
myTraining <- myTraining[c(-1)]Clean variables with more than 60% NA
trainingV3 <- myTraining
for(i in 1:length(myTraining)) {
if( sum( is.na( myTraining[, i] ) ) /nrow(myTraining) >= .7) {
for(j in 1:length(trainingV3)) {
if( length( grep(names(myTraining[i]), names(trainingV3)[j]) ) == 1) {
trainingV3 <- trainingV3[ , -j]
}
}
}
}Set back to the original variable name
myTraining <- trainingV3
rm(trainingV3)Transform the myTesting and testing data sets
clean1 <- colnames(myTraining)
clean2 <- colnames(myTraining[, -58]) # remove the classe column
myTesting <- myTesting[clean1] # allow only variables in myTesting that are also in myTraining
testing <- testing[clean2] # allow only variables in testing that are also in myTraining
dim(myTesting)## [1] 7846 58dim(testing)## [1] 20 57Coerce the data into the same type
for (i in 1:length(testing) ) {
for(j in 1:length(myTraining)) {
if( length( grep(names(myTraining[i]), names(testing)[j]) ) == 1) {
class(testing[j]) <- class(myTraining[i])
}
}
}To get the same class between testing and myTraining
testing <- rbind(myTraining[2, -58] , testing)
testing <- testing[-1,]Prediction with Decision Trees
set.seed(12345)
modFitA1 <- rpart(classe ~ ., data=myTraining, method="class")
fancyRpartPlot(modFitA1)
predictionsA1 <- predict(modFitA1, myTesting, type = "class")
cmtree <- confusionMatrix(predictionsA1, myTesting$classe)
cmtree## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2150 60 7 1 0
## B 61 1260 69 64 0
## C 21 188 1269 143 4
## D 0 10 14 857 78
## E 0 0 9 221 1360
##
## Overall Statistics
##
## Accuracy : 0.8789
## 95% CI : (0.8715, 0.8861)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8468
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9633 0.8300 0.9276 0.6664 0.9431
## Specificity 0.9879 0.9693 0.9450 0.9845 0.9641
## Pos Pred Value 0.9693 0.8666 0.7809 0.8936 0.8553
## Neg Pred Value 0.9854 0.9596 0.9841 0.9377 0.9869
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2740 0.1606 0.1617 0.1092 0.1733
## Detection Prevalence 0.2827 0.1853 0.2071 0.1222 0.2027
## Balanced Accuracy 0.9756 0.8997 0.9363 0.8254 0.9536plot(cmtree$table, col = cmtree$byClass, main = paste("Decision Tree Confusion Matrix: Accuracy =", round(cmtree$overall['Accuracy'], 4)))
##Prediction with Random Forests
set.seed(12345)
modFitB1 <- randomForest(classe ~ ., data=myTraining)
predictionB1 <- predict(modFitB1, myTesting, type = "class")
cmrf <- confusionMatrix(predictionB1, myTesting$classe)
cmrf## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2231 2 0 0 0
## B 1 1516 0 0 0
## C 0 0 1367 3 0
## D 0 0 1 1282 1
## E 0 0 0 1 1441
##
## Overall Statistics
##
## Accuracy : 0.9989
## 95% CI : (0.9978, 0.9995)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9985
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9996 0.9987 0.9993 0.9969 0.9993
## Specificity 0.9996 0.9998 0.9995 0.9997 0.9998
## Pos Pred Value 0.9991 0.9993 0.9978 0.9984 0.9993
## Neg Pred Value 0.9998 0.9997 0.9998 0.9994 0.9998
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2843 0.1932 0.1742 0.1634 0.1837
## Detection Prevalence 0.2846 0.1933 0.1746 0.1637 0.1838
## Balanced Accuracy 0.9996 0.9993 0.9994 0.9983 0.9996plot(modFitB1)
plot(cmrf$table, col = cmtree$byClass, main = paste("Random Forest Confusion Matrix: Accuracy =", round(cmrf$overall['Accuracy'], 4)))
# Prediction with Generalized Boosted Regression
set.seed(12345)
fitControl <- trainControl(method = "repeatedcv",
number = 5,
repeats = 1)
gbmFit1 <- train(classe ~ ., data=myTraining, method = "gbm",
trControl = fitControl,
verbose = FALSE)## Loading required package: gbm## Loading required package: survival##
## Attaching package: 'survival'## The following object is masked from 'package:caret':
##
## cluster## Loading required package: splines## Loading required package: parallel## Loaded gbm 2.1.1## Loading required package: plyrgbmFinMod1 <- gbmFit1$finalModel
gbmPredTest <- predict(gbmFit1, newdata=myTesting)
gbmAccuracyTest <- confusionMatrix(gbmPredTest, myTesting$classe)
gbmAccuracyTest## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2231 3 0 0 0
## B 1 1512 1 0 0
## C 0 2 1361 2 0
## D 0 1 6 1275 0
## E 0 0 0 9 1442
##
## Overall Statistics
##
## Accuracy : 0.9968
## 95% CI : (0.9953, 0.9979)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.996
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9996 0.9960 0.9949 0.9914 1.0000
## Specificity 0.9995 0.9997 0.9994 0.9989 0.9986
## Pos Pred Value 0.9987 0.9987 0.9971 0.9945 0.9938
## Neg Pred Value 0.9998 0.9991 0.9989 0.9983 1.0000
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2843 0.1927 0.1735 0.1625 0.1838
## Detection Prevalence 0.2847 0.1930 0.1740 0.1634 0.1849
## Balanced Accuracy 0.9995 0.9979 0.9971 0.9952 0.9993plot(gbmFit1, ylim=c(0.9, 1))
## Random Forests gave an Accuracy in the myTesting dataset of 99.89%, which was more accurate that what I got from the Decision Trees or GBM. The expected out-of-sample error is 100-99.89 = 0.11%.
predictionB2 <- predict(modFitB1, testing, type = "class")
predictionB2## 2 31 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
## 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 EWrite the results to a text file for submission
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)
}
}