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.
Class A: exactly according to the specification (proper execution)
Class B: throwing the elbows to the front (common mistake)
Class C: lifting the dumbbell only halfway (common mistake)
Class D: lowering the dumbbell only halfway (common mistake)
Class F: Throwing the hips to the front (common mistake)
The goal of this project is to predict the manner in which the exercise was performed (i.e., Class A, B, C, D, or F).
library(caret)## Warning: package 'caret' was built under R version 4.0.3## Loading required package: lattice## Loading required package: ggplot2library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(rattle)## Rattle: A free graphical interface for data science with R.
## Versión 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## Escriba 'rattle()' para agitar, sacudir y rotar sus datos.library(e1071)
library(randomForest)## Warning: package 'randomForest' was built under R version 4.0.3## 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':
##
## marginset.seed(1)train.url <-
"https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
test.url <-
"https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
path <- paste(getwd(),"/", "machine", sep="")
train.file <- file.path(path, "machine-train-data.csv")
test.file <- file.path(path, "machine-test-data.csv")if (!file.exists(train.file)) {
download.file(train.url, destfile=train.file)
}
if (!file.exists(test.file)) {
download.file(test.url, destfile=test.file)
}
train.data.raw <- read.csv(train.file, na.strings=c("NA","#DIV/0!",""))
test.data.raw <- read.csv(test.file, na.strings=c("NA","#DIV/0!","")) #Drop the first 7 columns as they're unnecessary for predicting.
train.data.clean1 <- train.data.raw[,8:length(colnames(train.data.raw))]
test.data.clean1 <- test.data.raw[,8:length(colnames(test.data.raw))]
# Drop colums with NAs
train.data.clean1 <- train.data.clean1[, colSums(is.na(train.data.clean1)) == 0]
test.data.clean1 <- test.data.clean1[, colSums(is.na(test.data.clean1)) == 0]
# Check for near zero variance predictors and drop them if necessary
nzv <- nearZeroVar(train.data.clean1,saveMetrics=TRUE)
zero.var.ind <- sum(nzv$nzv)
if ((zero.var.ind>0)) {
train.data.clean1 <- train.data.clean1[,nzv$nzv==FALSE]
}trainingRaw <- read.csv("./data/pml-training.csv")
testRaw <- read.csv("./data/pml-testing.csv")
dim(trainingRaw)## [1] 19622 160dim(testRaw)## [1] 20 160str(trainingRaw)## 'data.frame': 19622 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ user_name : chr "carlitos" "carlitos" "carlitos" "carlitos" ...
## $ raw_timestamp_part_1 : int 1323084231 1323084231 1323084231 1323084232 1323084232 1323084232 1323084232 1323084232 1323084232 1323084232 ...
## $ raw_timestamp_part_2 : int 788290 808298 820366 120339 196328 304277 368296 440390 484323 484434 ...
## $ cvtd_timestamp : chr "05/12/2011 11:23" "05/12/2011 11:23" "05/12/2011 11:23" "05/12/2011 11:23" ...
## $ new_window : chr "no" "no" "no" "no" ...
## $ num_window : int 11 11 11 12 12 12 12 12 12 12 ...
## $ roll_belt : num 1.41 1.41 1.42 1.48 1.48 1.45 1.42 1.42 1.43 1.45 ...
## $ pitch_belt : num 8.07 8.07 8.07 8.05 8.07 8.06 8.09 8.13 8.16 8.17 ...
## $ yaw_belt : num -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 -94.4 ...
## $ total_accel_belt : int 3 3 3 3 3 3 3 3 3 3 ...
## $ kurtosis_roll_belt : chr "" "" "" "" ...
## $ kurtosis_picth_belt : chr "" "" "" "" ...
## $ kurtosis_yaw_belt : chr "" "" "" "" ...
## $ skewness_roll_belt : chr "" "" "" "" ...
## $ skewness_roll_belt.1 : chr "" "" "" "" ...
## $ skewness_yaw_belt : chr "" "" "" "" ...
## $ max_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_belt : chr "" "" "" "" ...
## $ min_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_belt : chr "" "" "" "" ...
## $ amplitude_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_pitch_belt : int NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_yaw_belt : chr "" "" "" "" ...
## $ var_total_accel_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_roll_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_pitch_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_yaw_belt : num NA NA NA NA NA NA NA NA NA NA ...
## $ gyros_belt_x : num 0 0.02 0 0.02 0.02 0.02 0.02 0.02 0.02 0.03 ...
## $ gyros_belt_y : num 0 0 0 0 0.02 0 0 0 0 0 ...
## $ gyros_belt_z : num -0.02 -0.02 -0.02 -0.03 -0.02 -0.02 -0.02 -0.02 -0.02 0 ...
## $ accel_belt_x : int -21 -22 -20 -22 -21 -21 -22 -22 -20 -21 ...
## $ accel_belt_y : int 4 4 5 3 2 4 3 4 2 4 ...
## $ accel_belt_z : int 22 22 23 21 24 21 21 21 24 22 ...
## $ magnet_belt_x : int -3 -7 -2 -6 -6 0 -4 -2 1 -3 ...
## $ magnet_belt_y : int 599 608 600 604 600 603 599 603 602 609 ...
## $ magnet_belt_z : int -313 -311 -305 -310 -302 -312 -311 -313 -312 -308 ...
## $ roll_arm : num -128 -128 -128 -128 -128 -128 -128 -128 -128 -128 ...
## $ pitch_arm : num 22.5 22.5 22.5 22.1 22.1 22 21.9 21.8 21.7 21.6 ...
## $ yaw_arm : num -161 -161 -161 -161 -161 -161 -161 -161 -161 -161 ...
## $ total_accel_arm : int 34 34 34 34 34 34 34 34 34 34 ...
## $ var_accel_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ avg_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ stddev_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ var_yaw_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ gyros_arm_x : num 0 0.02 0.02 0.02 0 0.02 0 0.02 0.02 0.02 ...
## $ gyros_arm_y : num 0 -0.02 -0.02 -0.03 -0.03 -0.03 -0.03 -0.02 -0.03 -0.03 ...
## $ gyros_arm_z : num -0.02 -0.02 -0.02 0.02 0 0 0 0 -0.02 -0.02 ...
## $ accel_arm_x : int -288 -290 -289 -289 -289 -289 -289 -289 -288 -288 ...
## $ accel_arm_y : int 109 110 110 111 111 111 111 111 109 110 ...
## $ accel_arm_z : int -123 -125 -126 -123 -123 -122 -125 -124 -122 -124 ...
## $ magnet_arm_x : int -368 -369 -368 -372 -374 -369 -373 -372 -369 -376 ...
## $ magnet_arm_y : int 337 337 344 344 337 342 336 338 341 334 ...
## $ magnet_arm_z : int 516 513 513 512 506 513 509 510 518 516 ...
## $ kurtosis_roll_arm : chr "" "" "" "" ...
## $ kurtosis_picth_arm : chr "" "" "" "" ...
## $ kurtosis_yaw_arm : chr "" "" "" "" ...
## $ skewness_roll_arm : chr "" "" "" "" ...
## $ skewness_pitch_arm : chr "" "" "" "" ...
## $ skewness_yaw_arm : chr "" "" "" "" ...
## $ max_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ min_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_roll_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_pitch_arm : num NA NA NA NA NA NA NA NA NA NA ...
## $ amplitude_yaw_arm : int NA NA NA NA NA NA NA NA NA NA ...
## $ roll_dumbbell : num 13.1 13.1 12.9 13.4 13.4 ...
## $ pitch_dumbbell : num -70.5 -70.6 -70.3 -70.4 -70.4 ...
## $ yaw_dumbbell : num -84.9 -84.7 -85.1 -84.9 -84.9 ...
## $ kurtosis_roll_dumbbell : chr "" "" "" "" ...
## $ kurtosis_picth_dumbbell : chr "" "" "" "" ...
## $ kurtosis_yaw_dumbbell : chr "" "" "" "" ...
## $ skewness_roll_dumbbell : chr "" "" "" "" ...
## $ skewness_pitch_dumbbell : chr "" "" "" "" ...
## $ skewness_yaw_dumbbell : chr "" "" "" "" ...
## $ max_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_picth_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ max_yaw_dumbbell : chr "" "" "" "" ...
## $ min_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_pitch_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## $ min_yaw_dumbbell : chr "" "" "" "" ...
## $ amplitude_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]We note that the training dataset contains 19622 observations and 160 variables, and the test dataset contains 20 observations and 160 variables. The variable “classe” in the training set is the result to predict.
trainingRaw$classe <- as.factor(trainingRaw$classe)
#get cant o register for classe
summary (trainingRaw$classe)## A B C D E
## 5580 3797 3422 3216 3607#NAindex <- apply(trainingRaw,2,function(x) {sum(is.na(x))})
#trainingRaw <- trainingRaw[,which(NAindex == 0)]
#NAindex <- apply(testRaw,2,function(x) {sum(is.na(x))})
#testingRaw <- testRaw[,which(NAindex == 0)]
#str(trainingRaw)
#str(testingRaw)v <- which(lapply(trainingRaw, class) %in% "numeric")
preObj <-preProcess(trainingRaw[,v],method=c('knnImpute', 'center', 'scale'))
trainLess1 <- predict(preObj, trainingRaw[,v])
trainLess1$classe <- trainingRaw$classe
testLess1 <-predict(preObj,testRaw[,v])Split the data into set for training and one set for cross validation.
set.seed(12031987)
inTrain = createDataPartition(trainLess1$classe, p = 3/4, list=FALSE)
training = trainLess1[inTrain,]
crossValidation = trainLess1[-inTrain,]Create the Train model using RF( random Forest)
modFit <- train(classe ~., method="rf", data=training, trControl=trainControl(method='cv'), number=5, allowParallel=TRUE )save(modFit,file="fit.R")Check out the acurracy of the training set and cross-validation set
trainingPred <- predict(modFit, training)
confusionMatrix(trainingPred, training$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 4185 0 0 0 0
## B 0 2848 0 0 0
## C 0 0 2567 0 0
## D 0 0 0 2412 0
## E 0 0 0 0 2706
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9997, 1)
## No Information Rate : 0.2843
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 1.0000 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
## Prevalence 0.2843 0.1935 0.1744 0.1639 0.1839
## Detection Rate 0.2843 0.1935 0.1744 0.1639 0.1839
## Detection Prevalence 0.2843 0.1935 0.1744 0.1639 0.1839
## Balanced Accuracy 1.0000 1.0000 1.0000 1.0000 1.0000cvPred <- predict(modFit, crossValidation)
confusionMatrix(cvPred, crossValidation$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1387 5 1 0 1
## B 4 936 10 1 2
## C 0 8 833 9 4
## D 0 0 10 793 4
## E 4 0 1 1 890
##
## Overall Statistics
##
## Accuracy : 0.9867
## 95% CI : (0.9831, 0.9898)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9832
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9943 0.9863 0.9743 0.9863 0.9878
## Specificity 0.9980 0.9957 0.9948 0.9966 0.9985
## Pos Pred Value 0.9950 0.9822 0.9754 0.9827 0.9933
## Neg Pred Value 0.9977 0.9967 0.9946 0.9973 0.9973
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2828 0.1909 0.1699 0.1617 0.1815
## Detection Prevalence 0.2843 0.1943 0.1741 0.1646 0.1827
## Balanced Accuracy 0.9961 0.9910 0.9845 0.9915 0.9931We get the predictions in the actual test set
testingPred <- predict(modFit, testLess1)
testingPred## [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