Final Project for Practical Machine Learning
Chris
April 11, 2017
Introduction
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).
Data comes from http://groupware.les.inf.puc-rio.br/har
Source of Data
The training data for this project are available here:
The test data are available here:
The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har. If you use the document you create for this class for any purpose please cite them as they have been very generous in allowing their data to be used for this kind of assignment.
##Load relevant Libraries
library(caret)
library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(rattle)
library(randomForest)Downloading Data set
set.seed(12345)
trainingset <-"http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
testingset<-"http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
train <- read.csv(url(trainingset),na.strings=c("NA","#DIV/0!",""))
testing <- read.csv(url(testingset),na.strings=c("NA","#DIV/0!",""))Susbet training and test sets and assessing structure
Train <- createDataPartition(train$classe, p=0.6, list=FALSE)
myTraining <- train[Train, ]
myTesting <- train[-Train, ]
dim(myTraining); dim(myTesting)## [1] 11776 160## [1] 7846 160Cleaning up structure of traing set removing any non zero variance variables that are useless predictors.
n <- nearZeroVar(myTraining, saveMetrics=TRUE)
myTraining <- myTraining[,n$nzv==FALSE]
n<- nearZeroVar(myTesting,saveMetrics=TRUE)
myTesting <- myTesting[,n$nzv==FALSE]
myTraining <- myTraining[c(-1)]Eliminate training set data that has data that has more than 60% NA in it.
trainingV3 <- myTraining
for(i in 1:length(myTraining)) {
if( sum( is.na( myTraining[, i] ) ) /nrow(myTraining) >= .7) {
for(j in 1:length(trainingV3)) {
if( length( grep(names(myTraining[i]), names(trainingV3)[j]) ) == 1) {
trainingV3 <- trainingV3[ , -j]
}
}
}
}
myTraining <- trainingV3
rm(trainingV3)clean1 <- colnames(myTraining) ##Make sure predictors in training and testing are the same
clean2 <- colnames(myTraining[,-58])###Isolate classe column
myTesting <- myTesting[clean1]###Enusre only columns in data are the same as the training
testing <- testing[clean2]
dim(myTesting)## [1] 7846 58dim(testing)## [1] 20 57Make sure class in testing set is the same as that in thre training set
for (i in 1:length(testing) ) {
for(j in 1:length(myTraining)) {
if( length( grep(names(myTraining[i]), names(testing)[j]) ) == 1) {
class(testing[j]) <- class(myTraining[i])
}
}
}
testing <- rbind(myTraining[2, -58] , testing)
testing <- testing[-1,]Prediction RUles using Decision Trees
set.seed(12345)
modA <- rpart(classe ~ ., data=myTraining, method="class")
table(myTraining$classe)##
## A B C D E
## 3348 2279 2054 1930 2165fancyRpartPlot(modA)
###Confusion matrix of Random Forest analysis
A1 <- predict(modA, myTesting, type = "class")
tree <- confusionMatrix(A1, myTesting$classe)
tree## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2150 60 7 1 0
## B 61 1260 69 64 0
## C 21 188 1269 143 4
## D 0 10 14 857 78
## E 0 0 9 221 1360
##
## Overall Statistics
##
## Accuracy : 0.8789
## 95% CI : (0.8715, 0.8861)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8468
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9633 0.8300 0.9276 0.6664 0.9431
## Specificity 0.9879 0.9693 0.9450 0.9845 0.9641
## Pos Pred Value 0.9693 0.8666 0.7809 0.8936 0.8553
## Neg Pred Value 0.9854 0.9596 0.9841 0.9377 0.9869
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2740 0.1606 0.1617 0.1092 0.1733
## Detection Prevalence 0.2827 0.1853 0.2071 0.1222 0.2027
## Balanced Accuracy 0.9756 0.8997 0.9363 0.8254 0.9536Plot table of random forest predictions using the classe as a reference.
plot(tree$table, col = tree$byClass, main = paste("Analysis by Decision Tree: Accuracy =", round(tree$overall['Accuracy'], 4)))
###Prediction of Data using Random Forest
set.seed(12345)
modFitB1 <- randomForest(classe ~ ., data=myTraining)
predB1 <- predict(modFitB1, myTesting, type = "class")
cmf <- confusionMatrix(predB1, myTesting$classe)
cmf## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2231 2 0 0 0
## B 1 1516 0 0 0
## C 0 0 1367 3 0
## D 0 0 1 1282 1
## E 0 0 0 1 1441
##
## Overall Statistics
##
## Accuracy : 0.9989
## 95% CI : (0.9978, 0.9995)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9985
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9996 0.9987 0.9993 0.9969 0.9993
## Specificity 0.9996 0.9998 0.9995 0.9997 0.9998
## Pos Pred Value 0.9991 0.9993 0.9978 0.9984 0.9993
## Neg Pred Value 0.9998 0.9997 0.9998 0.9994 0.9998
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2843 0.1932 0.1742 0.1634 0.1837
## Detection Prevalence 0.2846 0.1933 0.1746 0.1637 0.1838
## Balanced Accuracy 0.9996 0.9993 0.9994 0.9983 0.9996Plot of of Random FOrest analysis of class vs other predictors
plot(modFitB1)
###Plot of Confusion matrix using random forest
plot(cmf$table, col = cmf$byClass, main = paste("Random Forest Confusion Matrix: Accuracy =", round(cmf$overall['Accuracy'], 4)))
###The results show that the random forest prediction method is more accurate than the the decision tree method and so the predicition analysis for the test samples will be assessed using the Random forest method.
Prediction Using Boosting Method
set.seed(12345)
fitControl <- trainControl(method = "repeatedcv",
number = 5,
repeats = 1)
gbmFit1 <- train(classe ~ ., data=myTraining, method = "gbm",
trControl = fitControl,
verbose = FALSE)
gbmFinMod1 <- 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 4 0 0 0
## B 1 1512 1 0 0
## C 0 2 1361 3 0
## D 0 0 6 1274 1
## E 0 0 0 9 1441
##
## Overall Statistics
##
## Accuracy : 0.9966
## 95% CI : (0.995, 0.9977)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9956
## 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 0.9907 0.9993
## Specificity 0.9993 0.9997 0.9992 0.9989 0.9986
## Pos Pred Value 0.9982 0.9987 0.9963 0.9945 0.9938
## Neg Pred Value 0.9998 0.9991 0.9989 0.9982 0.9998
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2843 0.1927 0.1735 0.1624 0.1837
## Detection Prevalence 0.2849 0.1930 0.1741 0.1633 0.1848
## Balanced Accuracy 0.9994 0.9979 0.9971 0.9948 0.9990Plot comparing accuracy of boosting method vs. the boosting iterations. The greater iterations had more accuracy.
plot(gbmFit1, ylim=c(0.9, 1))
Since Accuracy of test samples is the highest with the Random Forest method, this will be used to predict the classe output of the test sample.
B2 <- predict(modFitB1, testing, type = "class")
B2## 1 2 31 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 EFor loop is generated to print the classe output intop txt files.
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(B2)