datasciencecoursera/Practical_Machine_Learning_Course_Project_Submitted.Rmd at master · MrTuncay/datasciencecoursera · GitHub

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---
title: "Practical_Machine_Learning_Course_Project.Rmd"
output:
  html_notebook: default
  html_document: default
---
Prediction Assignment Writeupless

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

Loading Data, replacing empty cells and "#DIV/0!" errors by "NA" values
```{r}
pmltraining <- read.csv(
    "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
    , sep=","
    , na.strings=c("","#DIV/0!","NA")
    )
    # reading file
pmltesting <- read.csv(
    "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
    , sep=","
    , na.strings=c("","#DIV/0!","NA")
    )
    # reading file

```

Loading only complete data and ignoring first seven attributes, wich do not contain valueable data for the preciction model.
```{r}
TrainingData <- pmltraining[,complete.cases(t(pmltraining))]
TrainingData <- TrainingData[, -c(1:7)]
TestingData <- pmltesting[,complete.cases(t(pmltesting))]
TestingData <- TestingData[, -c(1:7)]
```

Splitting into 60% training data and 40% test data
```{r}
set.seed(123)
library(caret)
inTrain = createDataPartition(y=TrainingData$classe, p = 0.6,list=FALSE)
training = TrainingData[inTrain,]
testing = TrainingData[-inTrain,]
```

Two methods will be applied to model the training data.

1) Random Forrest (RF)
```{r}

controlRF <- trainControl(method="cv", number=3, verboseIter=TRUE)
modelRf <- train(classe ~ ., data=training, method="rf",trControl=controlRF)
modelRfError <- confusionMatrix(predict(modelRf, newdata=testing), testing$classe)
modelRfError
```

2) Linear Discriminant Analysis (LDA)
```{r}
modelLda <- train(classe ~ ., data=training, method="lda")
modelLdaError <- confusionMatrix(predict(modelLda, newdata=testing),  testing$classe)
modelLdaError
```

Selected the best Model

The accuracy of the 2 modeling methods above are:

Random Forest : 0.9921
LDA           : 0.7046

Random Forest will be selected.


Apply the model for prediction
```{r}
predictTestingData <- predict(modelRf, newdata=TestingData)
predictTestingData
```