Practical Machine Learning Week4
Vamshi Krishna
7/14/2020
Introduction
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, our goal will be to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants.
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).
The goal of our 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.
Source of Data
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
Loading the dataset and libraries required
The data for this project come from this source: http://web.archive.org/web/20161224072740/http:/groupware.les.inf.puc-rio.br/har.
Lets load the required libraries for this project and read training and testing data from the urls above and see the shape and structure of the data we are dealing with. Lets also see the head of the training data to get the idea of what data is present in it.
library(caret)## Loading required package: lattice## Loading required package: ggplot2library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(RGtk2)
library(rattle)## Rattle: A free graphical interface for data science with R.
## Version 5.3.0 Copyright (c) 2006-2018 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(randomForest)## 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':
##
## marginlibrary(gbm)## Loaded gbm 2.1.5library(e1071)
training = read.csv('https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv')
testing = read.csv('https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv')
dim(training)## [1] 19622 160dim(testing)## [1] 20 160head(training)## X user_name raw_timestamp_part_1 raw_timestamp_part_2 cvtd_timestamp
## 1 1 carlitos 1323084231 788290 05/12/2011 11:23
## 2 2 carlitos 1323084231 808298 05/12/2011 11:23
## 3 3 carlitos 1323084231 820366 05/12/2011 11:23
## 4 4 carlitos 1323084232 120339 05/12/2011 11:23
## 5 5 carlitos 1323084232 196328 05/12/2011 11:23
## 6 6 carlitos 1323084232 304277 05/12/2011 11:23
## new_window num_window roll_belt pitch_belt yaw_belt total_accel_belt
## 1 no 11 1.41 8.07 -94.4 3
## 2 no 11 1.41 8.07 -94.4 3
## 3 no 11 1.42 8.07 -94.4 3
## 4 no 12 1.48 8.05 -94.4 3
## 5 no 12 1.48 8.07 -94.4 3
## 6 no 12 1.45 8.06 -94.4 3
## kurtosis_roll_belt kurtosis_picth_belt kurtosis_yaw_belt skewness_roll_belt
## 1
## 2
## 3
## 4
## 5
## 6
## skewness_roll_belt.1 skewness_yaw_belt max_roll_belt max_picth_belt
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## max_yaw_belt min_roll_belt min_pitch_belt min_yaw_belt amplitude_roll_belt
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## amplitude_pitch_belt amplitude_yaw_belt var_total_accel_belt avg_roll_belt
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## stddev_roll_belt var_roll_belt avg_pitch_belt stddev_pitch_belt
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## var_pitch_belt avg_yaw_belt stddev_yaw_belt var_yaw_belt gyros_belt_x
## 1 NA NA NA NA 0.00
## 2 NA NA NA NA 0.02
## 3 NA NA NA NA 0.00
## 4 NA NA NA NA 0.02
## 5 NA NA NA NA 0.02
## 6 NA NA NA NA 0.02
## gyros_belt_y gyros_belt_z accel_belt_x accel_belt_y accel_belt_z
## 1 0.00 -0.02 -21 4 22
## 2 0.00 -0.02 -22 4 22
## 3 0.00 -0.02 -20 5 23
## 4 0.00 -0.03 -22 3 21
## 5 0.02 -0.02 -21 2 24
## 6 0.00 -0.02 -21 4 21
## magnet_belt_x magnet_belt_y magnet_belt_z roll_arm pitch_arm yaw_arm
## 1 -3 599 -313 -128 22.5 -161
## 2 -7 608 -311 -128 22.5 -161
## 3 -2 600 -305 -128 22.5 -161
## 4 -6 604 -310 -128 22.1 -161
## 5 -6 600 -302 -128 22.1 -161
## 6 0 603 -312 -128 22.0 -161
## total_accel_arm var_accel_arm avg_roll_arm stddev_roll_arm var_roll_arm
## 1 34 NA NA NA NA
## 2 34 NA NA NA NA
## 3 34 NA NA NA NA
## 4 34 NA NA NA NA
## 5 34 NA NA NA NA
## 6 34 NA NA NA NA
## avg_pitch_arm stddev_pitch_arm var_pitch_arm avg_yaw_arm stddev_yaw_arm
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## var_yaw_arm gyros_arm_x gyros_arm_y gyros_arm_z accel_arm_x accel_arm_y
## 1 NA 0.00 0.00 -0.02 -288 109
## 2 NA 0.02 -0.02 -0.02 -290 110
## 3 NA 0.02 -0.02 -0.02 -289 110
## 4 NA 0.02 -0.03 0.02 -289 111
## 5 NA 0.00 -0.03 0.00 -289 111
## 6 NA 0.02 -0.03 0.00 -289 111
## accel_arm_z magnet_arm_x magnet_arm_y magnet_arm_z kurtosis_roll_arm
## 1 -123 -368 337 516
## 2 -125 -369 337 513
## 3 -126 -368 344 513
## 4 -123 -372 344 512
## 5 -123 -374 337 506
## 6 -122 -369 342 513
## kurtosis_picth_arm kurtosis_yaw_arm skewness_roll_arm skewness_pitch_arm
## 1
## 2
## 3
## 4
## 5
## 6
## skewness_yaw_arm max_roll_arm max_picth_arm max_yaw_arm min_roll_arm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## min_pitch_arm min_yaw_arm amplitude_roll_arm amplitude_pitch_arm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## amplitude_yaw_arm roll_dumbbell pitch_dumbbell yaw_dumbbell
## 1 NA 13.05217 -70.49400 -84.87394
## 2 NA 13.13074 -70.63751 -84.71065
## 3 NA 12.85075 -70.27812 -85.14078
## 4 NA 13.43120 -70.39379 -84.87363
## 5 NA 13.37872 -70.42856 -84.85306
## 6 NA 13.38246 -70.81759 -84.46500
## kurtosis_roll_dumbbell kurtosis_picth_dumbbell kurtosis_yaw_dumbbell
## 1
## 2
## 3
## 4
## 5
## 6
## skewness_roll_dumbbell skewness_pitch_dumbbell skewness_yaw_dumbbell
## 1
## 2
## 3
## 4
## 5
## 6
## max_roll_dumbbell max_picth_dumbbell max_yaw_dumbbell min_roll_dumbbell
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## min_pitch_dumbbell min_yaw_dumbbell amplitude_roll_dumbbell
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## amplitude_pitch_dumbbell amplitude_yaw_dumbbell total_accel_dumbbell
## 1 NA 37
## 2 NA 37
## 3 NA 37
## 4 NA 37
## 5 NA 37
## 6 NA 37
## var_accel_dumbbell avg_roll_dumbbell stddev_roll_dumbbell var_roll_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## avg_pitch_dumbbell stddev_pitch_dumbbell var_pitch_dumbbell avg_yaw_dumbbell
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_yaw_dumbbell var_yaw_dumbbell gyros_dumbbell_x gyros_dumbbell_y
## 1 NA NA 0 -0.02
## 2 NA NA 0 -0.02
## 3 NA NA 0 -0.02
## 4 NA NA 0 -0.02
## 5 NA NA 0 -0.02
## 6 NA NA 0 -0.02
## gyros_dumbbell_z accel_dumbbell_x accel_dumbbell_y accel_dumbbell_z
## 1 0.00 -234 47 -271
## 2 0.00 -233 47 -269
## 3 0.00 -232 46 -270
## 4 -0.02 -232 48 -269
## 5 0.00 -233 48 -270
## 6 0.00 -234 48 -269
## magnet_dumbbell_x magnet_dumbbell_y magnet_dumbbell_z roll_forearm
## 1 -559 293 -65 28.4
## 2 -555 296 -64 28.3
## 3 -561 298 -63 28.3
## 4 -552 303 -60 28.1
## 5 -554 292 -68 28.0
## 6 -558 294 -66 27.9
## pitch_forearm yaw_forearm kurtosis_roll_forearm kurtosis_picth_forearm
## 1 -63.9 -153
## 2 -63.9 -153
## 3 -63.9 -152
## 4 -63.9 -152
## 5 -63.9 -152
## 6 -63.9 -152
## kurtosis_yaw_forearm skewness_roll_forearm skewness_pitch_forearm
## 1
## 2
## 3
## 4
## 5
## 6
## skewness_yaw_forearm max_roll_forearm max_picth_forearm max_yaw_forearm
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## min_roll_forearm min_pitch_forearm min_yaw_forearm amplitude_roll_forearm
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## amplitude_pitch_forearm amplitude_yaw_forearm total_accel_forearm
## 1 NA 36
## 2 NA 36
## 3 NA 36
## 4 NA 36
## 5 NA 36
## 6 NA 36
## var_accel_forearm avg_roll_forearm stddev_roll_forearm var_roll_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## avg_pitch_forearm stddev_pitch_forearm var_pitch_forearm avg_yaw_forearm
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## stddev_yaw_forearm var_yaw_forearm gyros_forearm_x gyros_forearm_y
## 1 NA NA 0.03 0.00
## 2 NA NA 0.02 0.00
## 3 NA NA 0.03 -0.02
## 4 NA NA 0.02 -0.02
## 5 NA NA 0.02 0.00
## 6 NA NA 0.02 -0.02
## gyros_forearm_z accel_forearm_x accel_forearm_y accel_forearm_z
## 1 -0.02 192 203 -215
## 2 -0.02 192 203 -216
## 3 0.00 196 204 -213
## 4 0.00 189 206 -214
## 5 -0.02 189 206 -214
## 6 -0.03 193 203 -215
## magnet_forearm_x magnet_forearm_y magnet_forearm_z classe
## 1 -17 654 476 A
## 2 -18 661 473 A
## 3 -18 658 469 A
## 4 -16 658 469 A
## 5 -17 655 473 A
## 6 -9 660 478 Astr(training)## '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]Data cleaning
Lets clean the raw data using several methods
Removing variables having nearly zero variance
Lets remove the variables which are near to the value of zero, as they wont me useful much using nearZeroVar
near_zero_var = nearZeroVar(training)
near_zero_training = training[,-near_zero_var]
near_zero_testing = testing[,-near_zero_var]
dim(near_zero_training)## [1] 19622 100dim(near_zero_testing)## [1] 20 100Removing variables having NAs
Lets also remove the variables which are NAs
na_var <- sapply(near_zero_training, function(x) mean(is.na(x))) > 0.95
na_training <- near_zero_training[,na_var == FALSE]
na_testing <- near_zero_testing[,na_var==FALSE]
dim(na_training)## [1] 19622 59dim(na_testing)## [1] 20 59Removing non-numeric variables
Lets remove first 7 rows as those contains non-numeric data in them
non_num_training <- na_training[,8:59]
non_num_testing <- na_testing[,8:59]
dim(non_num_training)## [1] 19622 52dim(non_num_testing)## [1] 20 52The final data after cleaning the raw data we get are non_num_training and non_num_testing and see their dimensions
Creating Data Partitioning
Now, lets partition the data using createDataPartition method with 60% as training data and 40% as testing data and store them in train and test dataframes and see their size
inTrain <- createDataPartition(non_num_training$classe, p=0.6, list=FALSE)
train <- non_num_training[inTrain,]
test <- non_num_training[-inTrain,]
dim(train)## [1] 11776 52dim(test)## [1] 7846 52Lets check if classe column is present in training dataset and problem_id in testing dataset (raw data)
'classe' %in% names(training)## [1] TRUE'problem_id' %in% names(testing)## [1] TRUEFitting model using Decision Tree
Lets first fit the model using decision tree on our data and predict the values of test data and create a confusion matrix and finally plot a decision tree using rpart.plot
DTree_fit <- train(classe ~ ., data = train, method="rpart")
DTree_pred <- predict(DTree_fit, test)
confusionMatrix(DTree_pred, test$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2037 600 619 565 363
## B 33 530 44 225 292
## C 128 333 553 164 335
## D 32 54 152 332 64
## E 2 1 0 0 388
##
## Overall Statistics
##
## Accuracy : 0.4894
## 95% CI : (0.4783, 0.5005)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3322
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9126 0.34914 0.40424 0.25816 0.26907
## Specificity 0.6176 0.90613 0.85181 0.95396 0.99953
## Pos Pred Value 0.4869 0.47153 0.36550 0.52366 0.99233
## Neg Pred Value 0.9468 0.85302 0.87131 0.86772 0.85862
## Prevalence 0.2845 0.19347 0.17436 0.16391 0.18379
## Detection Rate 0.2596 0.06755 0.07048 0.04231 0.04945
## Detection Prevalence 0.5333 0.14326 0.19284 0.08081 0.04983
## Balanced Accuracy 0.7651 0.62764 0.62802 0.60606 0.63430rpart.plot(DTree_fit$finalModel, roundint=FALSE)
Fitting model using Gradient Boosting
Lets now secondly fit a model using gradient boosting method and predict with training data and plot the accuracy of this model
GBM_fit <- train(classe ~ ., data = train, method = "gbm", verbose = FALSE)
GBM_fit$finalModel## A gradient boosted model with multinomial loss function.
## 150 iterations were performed.
## There were 51 predictors of which 51 had non-zero influence.GBM_pred <- predict(GBM_fit, test)
GBM_pred_conf <- confusionMatrix(GBM_pred, test$classe)
GBM_pred_conf## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2208 40 0 1 3
## B 19 1441 54 8 27
## C 2 29 1298 37 16
## D 3 1 14 1230 18
## E 0 7 2 10 1378
##
## Overall Statistics
##
## Accuracy : 0.9629
## 95% CI : (0.9585, 0.967)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9531
##
## Mcnemar's Test P-Value : 1.68e-09
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9892 0.9493 0.9488 0.9565 0.9556
## Specificity 0.9922 0.9829 0.9870 0.9945 0.9970
## Pos Pred Value 0.9805 0.9303 0.9392 0.9716 0.9864
## Neg Pred Value 0.9957 0.9878 0.9892 0.9915 0.9901
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2814 0.1837 0.1654 0.1568 0.1756
## Detection Prevalence 0.2870 0.1974 0.1761 0.1614 0.1781
## Balanced Accuracy 0.9907 0.9661 0.9679 0.9755 0.9763plot(GBM_pred_conf$table, col = GBM_pred_conf$byClass,
main = paste("Gradient Boosting - Accuracy Level =",
round(GBM_pred_conf$overall['Accuracy'], 4)))
Fitting model using Random Forest
Lastly fit a model using Random FOrest method and predict with training data and plot the accuracy of this model using confusion matrix
RF_fit <- train(classe ~ ., data = train, method = "rf", ntree = 50)
RF_pred <- predict(RF_fit, test)
RF_pred_conf <- confusionMatrix(RF_pred, test$classe)
RF_pred_conf## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 2230 11 0 0 0
## B 2 1497 9 0 0
## C 0 9 1356 15 4
## D 0 0 2 1269 6
## E 0 1 1 2 1432
##
## Overall Statistics
##
## Accuracy : 0.9921
## 95% CI : (0.9899, 0.9939)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.99
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9991 0.9862 0.9912 0.9868 0.9931
## Specificity 0.9980 0.9983 0.9957 0.9988 0.9994
## Pos Pred Value 0.9951 0.9927 0.9798 0.9937 0.9972
## Neg Pred Value 0.9996 0.9967 0.9981 0.9974 0.9984
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2842 0.1908 0.1728 0.1617 0.1825
## Detection Prevalence 0.2856 0.1922 0.1764 0.1628 0.1830
## Balanced Accuracy 0.9986 0.9922 0.9935 0.9928 0.9962plot(RF_pred_conf$table, col = RF_pred_conf$byClass,
main = paste("Random Forest Accuracy : ",
round(RF_pred_conf$overall['Accuracy'], 4)))
Conclusion and Summary
As decision tree model’s accuracy was very low, we completely ignore that model and concentrate on the other two models’ accuracy.
RF_pred_conf$overall## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9920979 0.9900035 0.9898811 0.9939363 0.2844762
## AccuracyPValue McnemarPValue
## 0.0000000 NaNGBM_pred_conf$overall## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.629110e-01 9.530679e-01 9.584902e-01 9.669836e-01 2.844762e-01
## AccuracyPValue McnemarPValue
## 0.000000e+00 1.680062e-09After looking at the overall statistics data of both the models, the random Forest model has more accuracy than the GBM model. So, we are selecting Random Forest model for final prediction of testing data.
Final modelling and prediction on actual Test data
training_data = non_num_training
testing_data = non_num_testing
RF_final_fit <- train(classe ~ ., data = training_data, method = "rf", ntree = 50)
final_pred <- predict(RF_final_fit, testing_data)
final_pred## [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