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.
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.
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 3.4.2library(lubridate)## Warning: package 'lubridate' was built under R version 3.4.3##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
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## datelibrary(caret)## Warning: package 'caret' was built under R version 3.4.3## Loading required package: latticelibrary(randomForest)## Warning: package 'randomForest' was built under R version 3.4.3## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
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## margin## The following object is masked from 'package:dplyr':
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## combinelibrary(rpart)## Warning: package 'rpart' was built under R version 3.4.3library(rpart.plot)## Warning: package 'rpart.plot' was built under R version 3.4.3library(corrplot)## Warning: package 'corrplot' was built under R version 3.4.3## corrplot 0.84 loadedUrlTrain <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
UrlTest <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
# download the datasets
dt_training <- read.csv(url(UrlTrain))
dt_testing <- read.csv(url(UrlTest))Remove all columns that contains NA and remove features that are not in the testing dataset. The features containing NA are the variance, mean and standard devition (SD) within each window for each feature. Since the testing dataset has no time-dependence, these values are useless and can be disregarded. We will also remove the first 7 features since they are related to the time-series or are not numeric
features <- names(dt_testing[,colSums(is.na(dt_testing)) == 0])[8:59]
# Only use features used in testing cases.
dt_training <- dt_training[,c(features,"classe")]
dt_testing <- dt_testing[,c(features,"problem_id")]
dim(dt_training)## [1] 19622 53dim(dt_testing)## [1] 20 53Following the recommendation in the course Practical Machine Learning, we will split our data into a training data set (60% of the total cases) and a testing data set (40% of the total cases; the latter should not be confused with the data in the pml-testing.csv file). This will allow us to estimate the out of sample error of our predictor.
set.seed(12345)
inTrain <- createDataPartition(dt_training$classe, p=0.6, list=FALSE)
training <- dt_training[inTrain,]
testing <- dt_training[-inTrain,]
dim(training)## [1] 11776 53modFitDT <- rpart(classe ~ ., data = training, method="class")set.seed(12345)
prediction <- predict(modFitDT, testing, type = "class")
confusionMatrix(prediction, testing$classe) ## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1879 260 30 69 66
## B 56 759 88 34 54
## C 105 340 1226 354 234
## D 155 132 23 807 57
## E 37 27 1 22 1031
##
## Overall Statistics
##
## Accuracy : 0.7267
## 95% CI : (0.7167, 0.7366)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6546
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8418 0.50000 0.8962 0.6275 0.7150
## Specificity 0.9243 0.96334 0.8405 0.9441 0.9864
## Pos Pred Value 0.8155 0.76589 0.5427 0.6874 0.9222
## Neg Pred Value 0.9363 0.88928 0.9746 0.9282 0.9389
## Prevalence 0.2845 0.19347 0.1744 0.1639 0.1838
## Detection Rate 0.2395 0.09674 0.1563 0.1029 0.1314
## Detection Prevalence 0.2937 0.12631 0.2879 0.1496 0.1425
## Balanced Accuracy 0.8831 0.73167 0.8684 0.7858 0.8507Using random forest, the out of sample error should be small. The error will be estimated using the 40% testing sample. We should expect an error estimate of < 3%.
set.seed(12345)
modFitRF <- randomForest(classe ~ ., data = training, ntree = 1000)prediction <- predict(modFitRF, testing, type = "class")
confusionMatrix(prediction, testing$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2230 9 0 0 0
## B 2 1504 6 0 0
## C 0 5 1362 17 2
## D 0 0 0 1267 5
## E 0 0 0 2 1435
##
## Overall Statistics
##
## Accuracy : 0.9939
## 95% CI : (0.9919, 0.9955)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9923
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9991 0.9908 0.9956 0.9852 0.9951
## Specificity 0.9984 0.9987 0.9963 0.9992 0.9997
## Pos Pred Value 0.9960 0.9947 0.9827 0.9961 0.9986
## Neg Pred Value 0.9996 0.9978 0.9991 0.9971 0.9989
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2842 0.1917 0.1736 0.1615 0.1829
## Detection Prevalence 0.2854 0.1927 0.1767 0.1621 0.1832
## Balanced Accuracy 0.9988 0.9948 0.9960 0.9922 0.9974predictionDT <- predict(modFitDT, dt_testing, type = "class")
predictionDT ## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## C A C A A E D D A A A C A A C E A D C B
## Levels: A B C D EpredictionRF <- predict(modFitRF, dt_testing, type = "class")
predictionRF## 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 EAs can be seen from the confusion matrix the Random Forest model is very accurate, about 99%. Because of that we could expect nearly all of the submitted test cases to be correct. It turned out they were all correct.
Prepare the 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)
}
}
pml_write_files(predictionRF)