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https://github.com/aslisabanci/coursera-practicalmachinelearning

Repo for the course project of Coursera - Practical Machine Learning MOOC
https://github.com/aslisabanci/coursera-practicalmachinelearning

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Repo for the course project of Coursera - Practical Machine Learning MOOC

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README 

        
coursera-practicalMachineLearning

=================================



##Goal of the project

In this project, we're given a training set at



and a test set at  from the source 

These sets contains data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants who are asked to perform barbell lifts correctly and incorrectly in 5 different ways. Our goal is to predict the manner in which the people did the exercise, i.e. the "classe" variable in the training set. After training and creating a model, we will use this model to predict 20 different test cases in the test data set. We will submit our final predictions to Coursera and see how well we did :)



##Preparation for R Markdown

```{r}

library(knitr)

opts_chunk$set(echo = TRUE, message=FALSE, results = 'hold')

```



##Loading all the needed libraries first

```{r}

library(caret)

library(randomForest)

library(corrplot)

```



##Setting the seed

```{r}

set.seed(1535)

```



##Downloading testing and training data



Let's download the 2 files from the internet first.

```{r}

download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv", "/Users/aslisabanci/Documents/pml/trainingData.csv", method="curl")

download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv", "/Users/aslisabanci/Documents/pml/testingData.csv", method="curl")

```



Now we should get what's inside the downloaded training .csv file. When reading the files, we'll also specify a vector of values that should be treated as NAs.

```{r}

trainingData = read.csv("/Users/aslisabanci/Documents/pml/trainingData.csv", na.strings = c("NA", "#DIV/0!",""))

dim(trainingData)

```

We see that the training data has 19622 observations of 160 variables.



Let's do the same for our testing .csv file.

```{r}

testingData = read.csv("/Users/aslisabanci/Documents/pml/testingData.csv", na.strings = c("NA", "#DIV/0!",""))

dim(testingData)

```

We see that the testing data has 20 observations of 160 variables.



#Reducing the number of predictors

Looking at the predictors, logically the first 5 of them (id, user name and timestamps) shouldn't have anything to do with our model. So let's remove them from both the training and testing data sets.

```{r}

trainingData<-trainingData[, -c(1:5)]

testingData<-testingData[, -c(1:5)]

```

Now, we're left with 155 columns.



Another thing that draws our attention in the data is that there are many NA values. Let's remove columns which have more than 50% of "NA".

```{r}

naColumns <- colSums(is.na(trainingData)) > nrow(trainingData) * .5

trainingData <- trainingData[,!naColumns]

```

Now our training data has 55 columns.



Lastly, let's check for columns with near zero variance and remove those columns, too.

```{r}

nearZeroVarColumns <- nearZeroVar(trainingData)

trainingData <- trainingData[, -nearZeroVarColumns]

```

Now, our training data has 54 columns.



We should do the same steps on our testing data, too. 

```{r}

testingData <- testingData[,!naColumns]

testingData <- testingData[, -nearZeroVarColumns]

```

Now, our testing data also has 54 columns. We're satisfied with the cleaning part so far, and we're good to go :)



##Cross Validation

In order to avoid overfitting, it's good to do cross validation on our training data. Also, we would like to see our model's accuracy on a cross validation set, before we apply it on the final testing set. 



So we'll split our cleaned training data into two sets, named: cleanTraining and crossValidation.



```{r}

inTrain <- createDataPartition(trainingData$classe, p=0.7, list=FALSE)

cleanTraining <- trainingData[inTrain,]

crossValidation <- trainingData[-inTrain,]

```

Our clean training set has 13737 observations and our cross validation set has 5885 observations.



##Fitting a model

We'll build a random forest model because we're given a non-linear problem and we know that tree methods usually work well with high accuracy, with these kinds of problems. And, compared to a single tree model, random forest's accuracy would be better.



```{r}

model = randomForest(classe ~., data=cleanTraining)

model

```



Our OOB error rate of .34% looks promising and tells us that we can continue using this model.



Now it's time we get our predictions:

```{r}

prediction <- predict(model, crossValidation)

confMatrix <- confusionMatrix(prediction,crossValidation$classe)

outOfSampleError <- 1-sum(diag(confMatrix$table))/sum(confMatrix$table)

outOfSampleError

```

Out of sample error is calculated as 0.0022.



Let's look at our confusion matrix in detail:

```{r}

print(confMatrix)

```

Our model's accuracy is 0.9978 which is pretty good :)



#Getting final predictions on the test set

Now we can apply our prediction model to the testing data with 20 observations.

```{r}

testSetPrediction <- predict(model, testingData)

testSetPrediction

```



#Saving final predictions on to the files

We'll define the function we're given by the Coursera team, to write the predictions in separate files

```{r}

pml_write_files = function(x){

n = length(x)

for(i in 1:n){

filename = paste0("problem_id_",i,".txt")

write.table(x[i],file=filename,quote=FALSE,row.names=FALSE,col.names=FALSE)

}

}

```



Now, let's call this function with our testSetPrediction vector

```{r}

pml_write_files(testSetPrediction)

```



##Final validation

We've submitted these predictions to Coursera and our model passed the validation %100!