MachineLearning
Sanjeev Rohilla
December 15, 2018
Coursera Practical Machine Learning Project
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).
# Libraries used for preparing and exploring the data
library(caret)## Warning: package 'caret' was built under R version 3.4.4## Loading required package: lattice## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.4.4library(rattle)## Warning: package 'rattle' was built under R version 3.4.4## 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.library(ggplot2)# Gettiig the training Dataset
TrainData <- read.csv(url("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"),header=TRUE)
dim(TrainData)## [1] 19622 160# Getting the test dataset
TestData <- read.csv(url("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"),header = TRUE)
dim(TestData)## [1] 20 160After Getting the training and test dataset. Preprocess the data for modelling and visualisation
# To check NA values are present in the training and test dataset
str(TrainData)## 'data.frame': 19622 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ user_name : Factor w/ 6 levels "adelmo","carlitos",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ 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 : Factor w/ 20 levels "02/12/2011 13:32",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ new_window : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 397 levels "","-0.016850",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_belt : Factor w/ 317 levels "","-0.021887",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt : Factor w/ 395 levels "","-0.003095",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt.1 : Factor w/ 338 levels "","-0.005928",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 4 levels "","#DIV/0!","0.00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 330 levels "","-0.02438",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_arm : Factor w/ 328 levels "","-0.00484",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_arm : Factor w/ 395 levels "","-0.01548",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_arm : Factor w/ 331 levels "","-0.00051",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_arm : Factor w/ 328 levels "","-0.00184",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_arm : Factor w/ 395 levels "","-0.00311",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 398 levels "","-0.0035","-0.0073",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_dumbbell : Factor w/ 401 levels "","-0.0163","-0.0233",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_dumbbell : Factor w/ 401 levels "","-0.0082","-0.0096",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_dumbbell : Factor w/ 402 levels "","-0.0053","-0.0084",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]From here I got that our data contains NA values in every line almost so we need to perform the cleaning operation while taking care of the NA values.Similary there would be NA values in the TestData as well.
indColToRemove <- which(colSums(is.na(TrainData) |TrainData=="")>0.9*dim(TrainData)[1])
TrainDataClean <- TrainData[,-indColToRemove]
TrainDataClean <- TrainDataClean[,-c(1:7)]
dim(TrainDataClean)## [1] 19622 53# So assuming the NA values would be present in the test dataset well. So will perform the similar operation on test dataset as well
indColToRemove <- which(colSums(is.na(TestData) |TestData=="")>0.9*dim(TestData)[1])
TestDataClean <- TestData[,-indColToRemove]
TestDataClean <- TestDataClean[,-1]
dim(TestDataClean)## [1] 20 59Let’s check randomly for the testdata are NA values present or not
str(TestDataClean$roll_arm)## num [1:20] 40.7 0 0 -109 76.1 0 0 0 -137 -82.4 ...# As we can see all teh NA values of Test Data are cared off. let's go to our next step After cleaning the data.Lets crate a partition of the dataset
set.seed(12345)
inTrain1 <- createDataPartition(TrainDataClean$classe, p=0.75, list=FALSE)
Train1 <- TrainDataClean[inTrain1,]
Test1 <- TrainDataClean[-inTrain1,]
dim(Train1)## [1] 14718 53dim(Test1)## [1] 4904 53We will check three difffernt models : “Classification Tree”,“RandomForest”,“GBM” In order to limit the effects of overfitting, and improve the efficicency of the models, we will use the *cross-validation technique. We will use 5 folds (usually, 5 or 10 can be used, but 10 folds gives higher run times with no significant increase of the accuracy)
trControl <- trainControl(method="cv", number=5)
model_CT <- train(classe~., data=Train1, method="rpart", trControl=trControl)
#print(model_CT)
fancyRpartPlot(model_CT$finalModel)
trainpred <- predict(model_CT,newdata=Test1)
confMatCT <- confusionMatrix(Test1$classe,trainpred)
# display confusion matrix and model accuracy
confMatCT$table## Reference
## Prediction A B C D E
## A 870 159 273 88 5
## B 162 530 214 43 0
## C 29 36 674 116 0
## D 46 136 429 193 0
## E 16 221 224 51 389confMatCT$overall[1]## Accuracy
## 0.5415987Here we have noticed that the outcome is not predicted well by other predictors.Let’s move to the Random Forest
model_RF <- train(classe~., data=Train1, method="rf", trControl=trControl, verbose=FALSE)
print(model_RF)## Random Forest
##
## 14718 samples
## 52 predictor
## 5 classes: 'A', 'B', 'C', 'D', 'E'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 11776, 11775, 11773, 11774, 11774
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 2 0.9918466 0.9896855
## 27 0.9923226 0.9902884
## 52 0.9906918 0.9882252
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 27.plot(model_RF,main="Accuracy of Random forest model by number of predictors")
trainpred <- predict(model_RF,newdata=Test1)
confMatRF <- confusionMatrix(Test1$classe,trainpred)
# display confusion matrix and model accuracy
confMatRF$table## Reference
## Prediction A B C D E
## A 1394 1 0 0 0
## B 6 939 4 0 0
## C 0 2 849 4 0
## D 0 0 10 794 0
## E 0 0 2 5 894confMatRF$overall[1]## Accuracy
## 0.9930669names(model_RF$finalModel)## [1] "call" "type" "predicted"
## [4] "err.rate" "confusion" "votes"
## [7] "oob.times" "classes" "importance"
## [10] "importanceSD" "localImportance" "proximity"
## [13] "ntree" "mtry" "forest"
## [16] "y" "test" "inbag"
## [19] "xNames" "problemType" "tuneValue"
## [22] "obsLevels" "param"model_RF$finalModel$classes## [1] "A" "B" "C" "D" "E"plot(model_RF$finalModel,main="Model error of Random forest model by number of trees")
# Compute the variable importance
MostImpVars <- varImp(model_RF)
MostImpVars## rf variable importance
##
## only 20 most important variables shown (out of 52)
##
## Overall
## roll_belt 100.00
## pitch_forearm 57.90
## yaw_belt 56.86
## magnet_dumbbell_z 44.51
## pitch_belt 44.02
## magnet_dumbbell_y 40.55
## roll_forearm 39.56
## accel_dumbbell_y 21.11
## magnet_dumbbell_x 17.90
## roll_dumbbell 17.68
## accel_forearm_x 17.27
## magnet_belt_z 14.44
## accel_dumbbell_z 14.16
## magnet_forearm_z 14.08
## total_accel_dumbbell 13.45
## magnet_belt_y 12.82
## accel_belt_z 12.02
## yaw_arm 11.39
## gyros_belt_z 10.48
## magnet_belt_x 10.18As we can see we have acheived the accuracy of more than 99% with 5 folds of cross validation.This seems to be perfect but let’s see what BGM offers us. ## Gradient Boosting Method
model_GBM <- train(classe~., data=Train1, method="gbm", trControl=trControl, verbose=FALSE)
print(model_GBM)## Stochastic Gradient Boosting
##
## 14718 samples
## 52 predictor
## 5 classes: 'A', 'B', 'C', 'D', 'E'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 11775, 11775, 11774, 11773, 11775
## Resampling results across tuning parameters:
##
## interaction.depth n.trees Accuracy Kappa
## 1 50 0.7547203 0.6891712
## 1 100 0.8194732 0.7714678
## 1 150 0.8541930 0.8154916
## 2 50 0.8585420 0.8207615
## 2 100 0.9064419 0.8816012
## 2 150 0.9317853 0.9136787
## 3 50 0.8959101 0.8682235
## 3 100 0.9406855 0.9249385
## 3 150 0.9605249 0.9500546
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 150,
## interaction.depth = 3, shrinkage = 0.1 and n.minobsinnode = 10.plot(model_GBM)
trainpred <- predict(model_GBM,newdata=Test1)
confMatGBM <- confusionMatrix(Test1$classe,trainpred)
confMatGBM$table## Reference
## Prediction A B C D E
## A 1378 14 3 0 0
## B 39 874 34 1 1
## C 0 20 824 10 1
## D 0 1 21 776 6
## E 3 7 8 16 867confMatGBM$overall[1]## Accuracy
## 0.9622757As we can see the accuracy obtained by “gbm” is 95% which is also great but the accuracy offered by Random Forest is higher so we will consider the Random Forest for this project. Conclusion This shows that the random forest model is the best one. We will then use it to predict the values of classe for the test data set.
FinalTestPred <- predict(model_RF,newdata=TestDataClean)
FinalTestPred## [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