Synopsis

This report shows how you can use a prediction model to identify whether participant is doing an excercise correctly based on data from an accelerometer.

Data Loading

library(readr)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
## 
## 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(caret)
## Warning: package 'caret' was built under R version 4.1.2
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.1.2
## Loading required package: lattice
library(rattle)
## Warning: package 'rattle' was built under R version 4.1.2
## Loading required package: tibble
## Warning: package 'tibble' was built under R version 4.1.2
## 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.
#Data Loading
pml_df <- read_csv(url("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"))
## New names:
## • `` -> `...1`
## Warning: One or more parsing issues, see `problems()` for details
## Rows: 19622 Columns: 160
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (34): user_name, cvtd_timestamp, new_window, kurtosis_roll_belt, kurtos...
## dbl (126): ...1, raw_timestamp_part_1, raw_timestamp_part_2, num_window, rol...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pml_pred <- read_csv(url("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"))
## New names:
## Rows: 20 Columns: 160
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (3): user_name, cvtd_timestamp, new_window dbl (57): ...1,
## raw_timestamp_part_1, raw_timestamp_part_2, num_window, rol... lgl (100):
## kurtosis_roll_belt, kurtosis_picth_belt, kurtosis_yaw_belt, skewn...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
pml_df$classe <- as.factor(pml_df$classe)
pml_df$user_name <- as.factor(pml_df$user_name)
pml_pred$user_name <- as.factor(pml_pred$user_name)

Data Processing

## [1] 1921614
## [1] 2000
## [1] 0
## [1] 0

partioning of 60 to 40 was used.

Building the Model

## [1] 0.3848114

##Use the Prediction Analysis 

##  [1] C A C A A E E A A A C C C A E E A A A C
## Levels: A B C D E

the predicted results were correct a majority of the time.