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).
The training data for this project are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv
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.
What you should submit
The goal of your project is to predict the manner in which they did the exercise. This is the “classe” variable in the training set. You may use any of the other variables to predict with. You should create a report describing how you built your model, how you used cross validation, what you think the expected out of sample error is, and why you made the choices you did. You will also use your prediction model to predict 20 different test cases.
Reproducibility
Due to security concerns with the exchange of R code, your code will not be run during the evaluation by your classmates. Please be sure that if they download the repo, they will be able to view the compiled HTML version of your analysis. [Available here]
Performed steps:
* Get the system ready loading dependencies and the data
* Remove excel division error strings #DIV/0! and deal with NA values
library(Hmisc)## Warning: package 'Hmisc' was built under R version 3.1.3## Loading required package: grid
## Loading required package: lattice
## Loading required package: survival
## Loading required package: splines
## Loading required package: Formula## Warning: package 'Formula' was built under R version 3.1.3## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.1.3##
## Attaching package: 'Hmisc'
##
## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, unitslibrary(caret)## Warning: package 'caret' was built under R version 3.1.3##
## Attaching package: 'caret'
##
## The following object is masked from 'package:survival':
##
## clusterlibrary(randomForest)## Warning: package 'randomForest' was built under R version 3.1.3## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:Hmisc':
##
## combinelibrary(foreach)## Warning: package 'foreach' was built under R version 3.1.3library(doParallel)## Warning: package 'doParallel' was built under R version 3.1.3## Loading required package: iterators## Warning: package 'iterators' was built under R version 3.1.3## Loading required package: parallelset.seed(1980)
options(warn=-1)
training_data <- read.csv("pml-training.csv", na.strings=c("#DIV/0!") )
evaluation_data <- read.csv("pml-testing.csv", na.strings=c("#DIV/0!") )Set columns from the eighth to the end to be numeric.
for(i in c(8:ncol(training_data)-1)) {training_data[,i] = as.numeric(as.character(training_data[,i]))}
for(i in c(8:ncol(evaluation_data)-1)) {evaluation_data[,i] = as.numeric(as.character(evaluation_data[,i]))}There are some almost-totally blank columns in the data that do not contribute well to the prediction, so the feature dataset is made to only include complete columns. Also removing user name, timestamps and windows.
Determine and display out feature set.
feature_set <- colnames(training_data[colSums(is.na(training_data)) == 0])[-(1:7)]
model_data <- training_data[feature_set]
feature_set## [1] "roll_belt" "pitch_belt" "yaw_belt"
## [4] "total_accel_belt" "gyros_belt_x" "gyros_belt_y"
## [7] "gyros_belt_z" "accel_belt_x" "accel_belt_y"
## [10] "accel_belt_z" "magnet_belt_x" "magnet_belt_y"
## [13] "magnet_belt_z" "roll_arm" "pitch_arm"
## [16] "yaw_arm" "total_accel_arm" "gyros_arm_x"
## [19] "gyros_arm_y" "gyros_arm_z" "accel_arm_x"
## [22] "accel_arm_y" "accel_arm_z" "magnet_arm_x"
## [25] "magnet_arm_y" "magnet_arm_z" "roll_dumbbell"
## [28] "pitch_dumbbell" "yaw_dumbbell" "total_accel_dumbbell"
## [31] "gyros_dumbbell_x" "gyros_dumbbell_y" "gyros_dumbbell_z"
## [34] "accel_dumbbell_x" "accel_dumbbell_y" "accel_dumbbell_z"
## [37] "magnet_dumbbell_x" "magnet_dumbbell_y" "magnet_dumbbell_z"
## [40] "roll_forearm" "pitch_forearm" "yaw_forearm"
## [43] "total_accel_forearm" "gyros_forearm_x" "gyros_forearm_y"
## [46] "gyros_forearm_z" "accel_forearm_x" "accel_forearm_y"
## [49] "accel_forearm_z" "magnet_forearm_x" "magnet_forearm_y"
## [52] "magnet_forearm_z" "classe"The model data is now built built from the feature set.
idx <- createDataPartition(y=model_data$classe, p=0.75, list=FALSE )
training <- model_data[idx,]
testing <- model_data[-idx,]Build 5 random forests with 150 trees each using parallel processing to build this model.
registerDoParallel()
x <- training[-ncol(training)]
y <- training$classe
rf <- foreach(ntree=rep(150, 6), .combine=randomForest::combine, .packages='randomForest') %dopar% {
randomForest(x, y, ntree=ntree)
}Provide error reports for both training and test data.
predictions1 <- predict(rf, newdata=training)
confusionMatrix(predictions1,training$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 4185 0 0 0 0
## B 0 2848 0 0 0
## C 0 0 2567 0 0
## D 0 0 0 2412 0
## E 0 0 0 0 2706
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9997, 1)
## No Information Rate : 0.2843
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 1.0000 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
## Prevalence 0.2843 0.1935 0.1744 0.1639 0.1839
## Detection Rate 0.2843 0.1935 0.1744 0.1639 0.1839
## Detection Prevalence 0.2843 0.1935 0.1744 0.1639 0.1839
## Balanced Accuracy 1.0000 1.0000 1.0000 1.0000 1.0000predictions2 <- predict(rf, newdata=testing)
confusionMatrix(predictions2,testing$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1393 7 0 0 0
## B 2 941 3 0 0
## C 0 1 851 14 0
## D 0 0 1 790 3
## E 0 0 0 0 898
##
## Overall Statistics
##
## Accuracy : 0.9937
## 95% CI : (0.991, 0.9957)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.992
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9986 0.9916 0.9953 0.9826 0.9967
## Specificity 0.9980 0.9987 0.9963 0.9990 1.0000
## Pos Pred Value 0.9950 0.9947 0.9827 0.9950 1.0000
## Neg Pred Value 0.9994 0.9980 0.9990 0.9966 0.9993
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2841 0.1919 0.1735 0.1611 0.1831
## Detection Prevalence 0.2855 0.1929 0.1766 0.1619 0.1831
## Balanced Accuracy 0.9983 0.9952 0.9958 0.9908 0.9983The confusion matrix shows a fairly accurate model. The test data was around 99% accurate so it was expected that most of the submitted test cases were correct. In the end all of them showed to be correct.
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)
}
}
x <- evaluation_data
x <- x[feature_set[feature_set!='classe']]
answers <- predict(rf, newdata=x)
answers## 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 Epml_write_files(answers)