ML Course Project

Fitness Prediction

Background

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).

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

The data for this project come from this source: http://web.archive.org/web/20161224072740/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.

Submission

The goal of your 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. You should create a report describing how you built your model, how you used cross validation, what you think the expected out of sample error is, and why you made the choices you did. You will also use your prediction model to predict 20 different test cases.

0.0 Data Processing

For the purposes of reproducibility, the seed will set to 999.

set.seed = 999

#Packages
library(ggplot2)
library(caret)

## Loading required package: lattice

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(lattice)
library(rpart)
library(rpart.plot)
library(RColorBrewer)
library(rattle)

## Loading required package: tibble

## Loading required package: bitops

## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

library(Rcpp)
library(randomForest)

## randomForest 4.7-1.1

## 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:dplyr':
## 
##     combine

## The following object is masked from 'package:ggplot2':
## 
##     margin

#Load in data

url_train <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
url_test <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"

train_data <- read.csv(url_train)
test_data <- read.csv(url_test)

Exploratory Analysis

head(train_data)


group_class <- train_data %>%
    group_by(classe) %>% 
    summarise(counts = n())

barplot <- ggplot(group_class, aes(x = classe, y = counts)) +
    geom_bar(stat = "identity", fill = "blue") + 
    labs(title = "Classe Counts",
         x = "Classe",
         y = "Counts")
barplot

Cleaning the data

Here we remove columns where the variance is close to zero

NZV <- nearZeroVar(train_data, saveMetrics=TRUE)

NZV_2 <- NZV %>% filter(nzv == TRUE)

#Remove columns where NZV = True

NZV_col_remove <- names(train_data) %in% c("new_window", "kurtosis_roll_belt", "kurtosis_picth_belt",
                                           "kurtosis_yaw_belt", "skewness_roll_belt", "skewness_roll_belt.1", "skewness_yaw_belt",
                                           "max_yaw_belt", "min_yaw_belt", "amplitude_yaw_belt", "avg_roll_arm", "stddev_roll_arm",
                                           "var_roll_arm", "avg_pitch_arm", "stddev_pitch_arm", "var_pitch_arm", "avg_yaw_arm",
                                           "stddev_yaw_arm", "var_yaw_arm", "kurtosis_roll_arm", "kurtosis_picth_arm",
                                           "kurtosis_yaw_arm", "skewness_roll_arm", "skewness_pitch_arm", "skewness_yaw_arm",
                                           "max_roll_arm", "min_roll_arm", "min_pitch_arm", "amplitude_roll_arm", "amplitude_pitch_arm",
                                           "kurtosis_roll_dumbbell", "kurtosis_picth_dumbbell", "kurtosis_yaw_dumbbell", "skewness_roll_dumbbell",
                                           "skewness_pitch_dumbbell", "skewness_yaw_dumbbell", "max_yaw_dumbbell", "min_yaw_dumbbell",
                                           "amplitude_yaw_dumbbell", "kurtosis_roll_forearm", "kurtosis_picth_forearm", "kurtosis_yaw_forearm",
                                           "skewness_roll_forearm", "skewness_pitch_forearm", "skewness_yaw_forearm", "max_roll_forearm",
                                           "max_yaw_forearm", "min_roll_forearm", "min_yaw_forearm", "amplitude_roll_forearm",
                                           "amplitude_yaw_forearm", "avg_roll_forearm", "stddev_roll_forearm", "var_roll_forearm",
                                           "avg_pitch_forearm", "stddev_pitch_forearm", "var_pitch_forearm", "avg_yaw_forearm",
                                           "stddev_yaw_forearm", "var_yaw_forearm")

train_data <- train_data[!NZV_col_remove]
test_data <- test_data[!NZV_col_remove]

partition the data with 70:30 split

inTrain <- caret::createDataPartition(train_data$classe,p=0.70,list=FALSE);
training <- train_data[inTrain, ]
testing <- train_data[-inTrain, ]

dim(training)

## [1] 13737   100

dim(testing)

## [1] 5885  100

Train the model

modFitA1 <- rpart(classe ~ ., data=training, method="class")
fancyRpartPlot(modFitA1)                   

predictionsA1 <- predict(modFitA1, data = testing, type = "class")