Cecilia Cruz-Ram, MD DPCOM 05/06/2020
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://web.archive.org/web/20161224072740/http:/groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).
In this project, the goal is 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.
The goal is to predict the manner in which the exercise was done.
A. Free Up Memory
B. Setwd
C. Load Data
## Loading required package: lattice
## Loading required package: ggplot2
## Rattle: A free graphical interface for data science with R.
## Version 5.2.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:rattle':
##
## importance
## The following object is masked from 'package:ggplot2':
##
## margin
## corrplot 0.84 loaded
# Set the URL for the download
UrlTrain <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
UrlTest <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
# Download Datasets
training <- read.csv(url(UrlTrain))
testing <- read.csv(url(UrlTest))
# Create a partition with the training dataset
inTrain <- createDataPartition(training$classe, p=0.7, list=FALSE)
TrainSet <- training[inTrain, ]
TestSet <- training[-inTrain, ]
dim(TrainSet)## [1] 13737 160
## [1] 5885 160
Data sets consists of 5885 values with 160 variables.
D. Clean and Pre-process Data
Remove values with Near Zero Variance (NZV).
## [1] 13737 104
## [1] 5885 104
Remove values with NA.
AllNA <- sapply(TrainSet, function(x) mean(is.na(x))) > 0.95
TrainSet <- TrainSet[, AllNA==FALSE]
TestSet <- TestSet[, AllNA==FALSE]
dim(TrainSet)## [1] 13737 59
## [1] 5885 59
Remove identification only variables (columns 1 to 5)
## [1] 13737 54
## [1] 5885 54
The number of variables has been reduced to 54.
E. Analyze Data
corMatrix <- cor(TrainSet[, -54])
corrplot(corMatrix, order = "FPC", method = "circle", type = "lower",
tl.cex = 0.35, tl.col = rgb(0, 0, 0))
The highly correlated variables are shown in dark colors in the graph above.
F. Prediction Modelling
Three methods will be applied to model the regressions (in the Train dataset) and the one with higher accuracy when applied to the Test dataset will be used for the quiz predictions. The methods are: Random Forests, Decision Tree and Generalized Boosted Model.
A Confusion Matrix is plotted at the end of each analysis to better visualize the accuracy of the models.
# Model fit
set.seed(12345)
controlRF <- trainControl(method = "cv", number = 3, verboseIter = FALSE)
modFitRandForest <- train(classe ~ ., data = TrainSet, method = "rf",
trControl = controlRF)
modFitRandForest$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 27
##
## OOB estimate of error rate: 0.23%
## Confusion matrix:
## A B C D E class.error
## A 3904 2 0 0 0 0.0005120328
## B 6 2647 4 1 0 0.0041384500
## C 0 5 2391 0 0 0.0020868114
## D 0 0 9 2243 0 0.0039964476
## E 0 0 0 5 2520 0.0019801980
# Prediction on Test Dataset
predictRandForest <- predict(modFitRandForest, newdata = TestSet)
confMatRandForest <- confusionMatrix(predictRandForest, TestSet$classe)
confMatRandForest## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1674 1 0 0 0
## B 0 1138 2 0 0
## C 0 0 1024 2 0
## D 0 0 0 962 1
## E 0 0 0 0 1081
##
## Overall Statistics
##
## Accuracy : 0.999
## 95% CI : (0.9978, 0.9996)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9987
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9991 0.9981 0.9979 0.9991
## Specificity 0.9998 0.9996 0.9996 0.9998 1.0000
## Pos Pred Value 0.9994 0.9982 0.9981 0.9990 1.0000
## Neg Pred Value 1.0000 0.9998 0.9996 0.9996 0.9998
## Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Detection Rate 0.2845 0.1934 0.1740 0.1635 0.1837
## Detection Prevalence 0.2846 0.1937 0.1743 0.1636 0.1837
## Balanced Accuracy 0.9999 0.9994 0.9988 0.9989 0.9995
# Plot Matrix Results
plot(confMatRandForest$table, col = confMatRandForest$byClass,
main = paste("Random Forest Accuracy =",
round(confMatRandForest$overall['Accuracy'], 3)))
# Model fit
set.seed(12345)
modFitDecTree <- rpart(classe ~ ., data = TrainSet, method = "class")
fancyRpartPlot(modFitDecTree)
# Prediction on Test Dataset
predictDecTree <- predict(modFitDecTree, newdata = TestSet, type = "class")
confMatDecTree <- confusionMatrix(predictDecTree, TestSet$classe)
confMatDecTree## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1502 201 59 66 74
## B 58 660 37 64 114
## C 4 66 815 129 72
## D 90 148 54 648 126
## E 20 64 61 57 696
##
## Overall Statistics
##
## Accuracy : 0.7342
## 95% CI : (0.7228, 0.7455)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6625
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8973 0.5795 0.7943 0.6722 0.6433
## Specificity 0.9050 0.9425 0.9442 0.9151 0.9579
## Pos Pred Value 0.7897 0.7074 0.7505 0.6079 0.7751
## Neg Pred Value 0.9568 0.9033 0.9560 0.9344 0.9226
## Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Detection Rate 0.2552 0.1121 0.1385 0.1101 0.1183
## Detection Prevalence 0.3232 0.1585 0.1845 0.1811 0.1526
## Balanced Accuracy 0.9011 0.7610 0.8693 0.7936 0.8006
# Plot Matrix Results
plot(confMatDecTree$table, col = confMatDecTree$byClass,
main = paste("Decision Tree Accuracy =",
round(confMatDecTree$overall['Accuracy'], 3)))
# Model fit
set.seed(12345)
controlGBM <- trainControl(method = "repeatedcv", number = 5, repeats = 1)
modFitGBM <- train(classe ~ ., data = TrainSet, method = "gbm",
trControl = controlGBM, verbose = FALSE)
modFitGBM$finalModel## A gradient boosted model with multinomial loss function.
## 150 iterations were performed.
## There were 53 predictors of which 53 had non-zero influence.
# Prediction on Test Dataset
predictGBM <- predict(modFitGBM, newdata=TestSet)
confMatGBM <- confusionMatrix(predictGBM, TestSet$classe)
confMatGBM## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1668 12 0 1 0
## B 6 1115 12 1 3
## C 0 12 1012 21 0
## D 0 0 2 941 6
## E 0 0 0 0 1073
##
## Overall Statistics
##
## Accuracy : 0.9871
## 95% CI : (0.9839, 0.9898)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9837
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9964 0.9789 0.9864 0.9761 0.9917
## Specificity 0.9969 0.9954 0.9932 0.9984 1.0000
## Pos Pred Value 0.9923 0.9807 0.9684 0.9916 1.0000
## Neg Pred Value 0.9986 0.9949 0.9971 0.9953 0.9981
## Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Detection Rate 0.2834 0.1895 0.1720 0.1599 0.1823
## Detection Prevalence 0.2856 0.1932 0.1776 0.1613 0.1823
## Balanced Accuracy 0.9967 0.9871 0.9898 0.9873 0.9958
# Plot Matrix Results
plot(confMatGBM$table, col = confMatGBM$byClass,
main = paste("GBM Accuracy =", round(confMatGBM$overall['Accuracy'], 3)))
G. Model Application to Test Data
The accuracy of the 3 regression modelling methods are:
Random Forest : 0.999
Decision Tree : 0.734
GBM : 0.987
The Random Forest model will be applied to predict the 20 quiz results (testing dataset).
## [1] 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
Velloso, E.; Bulling, A.; Gellersen, H.; Ugulino, W.; Fuks, H. Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of 4th International Conference in Cooperation with SIGCHI (Augmented Human ’13) . Stuttgart, Germany: ACM SIGCHI, 2013.
http://groupware.les.inf.puc-rio.br/har, visited 2015/12/25