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<h1>Practical Machine Learning Course Project</h1>
<h1></h1>
<h2>Synopsis:</h2>
<p>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: <a href="http://groupware.les.inf.puc-rio.br/har">http://groupware.les.inf.puc-rio.br/har</a> (see the section on the Weight Lifting Exercise Dataset). </p>
<p>The goal of this project is to predict the manner of performing unilateral dumbbell biceps curls based on data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants. The 5 possible methods include -</p>
<ul>
<li>A: exactly according to the specification </li>
<li>B: throwing the elbows to the front</li>
<li>C: lifting the dumbbell only halfway </li>
<li>D: lowering the dumbbell only halfway</li>
<li>E: throwing the hips to the front</li>
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<h4>Load libraries and setup working directory</h4>
<pre><code class="r">rm(list = ls(all = TRUE))
setwd('/media/nicolo/Data/Coursera/PracticalMachineLearning/Data/PracticalMachineLearning')
library(caret)
trainingRaw <- read.csv(file="pml-training.csv", header=TRUE, as.is = TRUE, stringsAsFactors = FALSE, sep=',', na.strings=c('NA','','#DIV/0!'))
testingRaw <- read.csv(file="pml-testing.csv", header=TRUE, as.is = TRUE, stringsAsFactors = FALSE, sep=',', na.strings=c('NA','','#DIV/0!'))
trainingRaw$classe <- as.factor(trainingRaw$classe)
</code></pre>
<h4>Examine the data</h4>
<pre><code>## 'data.frame': 19622 obs. of 160 variables:
## $ X : chr "1" "2" "3" "4" ...
## $ user_name : chr "carlitos" "carlitos" "carlitos" "carlitos" ...
## $ 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 : chr "05/12/2011 11:23" "05/12/2011 11:23" "05/12/2011 11:23" "05/12/2011 11:23" ...
## $ new_window : chr "no" "no" "no" "no" ...
## $ 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 : chr "" "" "" "" ...
## $ kurtosis_picth_belt : chr "" "" "" "" ...
## $ kurtosis_yaw_belt : chr "" "" "" "" ...
## $ skewness_roll_belt : chr "" "" "" "" ...
## $ skewness_roll_belt.1 : chr "" "" "" "" ...
## $ skewness_yaw_belt : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ kurtosis_picth_arm : chr "" "" "" "" ...
## $ kurtosis_yaw_arm : chr "" "" "" "" ...
## $ skewness_roll_arm : chr "" "" "" "" ...
## $ skewness_pitch_arm : chr "" "" "" "" ...
## $ skewness_yaw_arm : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ kurtosis_picth_dumbbell : chr "" "" "" "" ...
## $ kurtosis_yaw_dumbbell : chr "" "" "" "" ...
## $ skewness_roll_dumbbell : chr "" "" "" "" ...
## $ skewness_pitch_dumbbell : chr "" "" "" "" ...
## $ skewness_yaw_dumbbell : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ 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 : chr "" "" "" "" ...
## $ amplitude_roll_dumbbell : num NA NA NA NA NA NA NA NA NA NA ...
## [list output truncated]
</code></pre>
<h4>Cleaning variables</h4>
<p>After investigating all the variables of the sets, it's possible to see that there are a lot of values NA or useless or empty variables for the prediction. It's request to compute the prediction only on the accelerometers values of belt, forearm, arm and dumbell. So, the non-accelerometer measures are discard with the useless variables.</p>
<pre><code class="r">NAindex <- apply(trainingRaw,2,function(x) {sum(is.na(x))})
trainingRaw <- trainingRaw[,which(NAindex == 0)]
NAindex <- apply(testingRaw,2,function(x) {sum(is.na(x))})
testingRaw <- testingRaw[,which(NAindex == 0)]
</code></pre>
<h4>Preprocessing variables</h4>
<pre><code class="r">v <- which(lapply(trainingRaw, class) %in% "numeric")
preObj <-preProcess(trainingRaw[,v],method=c('knnImpute', 'center', 'scale'))
trainLess1 <- predict(preObj, trainingRaw[,v])
trainLess1$classe <- trainingRaw$classe
testLess1 <-predict(preObj,testingRaw[,v])
</code></pre>
<h4>Removing the non zero variables</h4>
<p>Removing the variables with values near zero, that means that they have not so much meaning in the predictions</p>
<pre><code class="r">nzv <- nearZeroVar(trainLess1,saveMetrics=TRUE)
trainLess1 <- trainLess1[,nzv$nzv==FALSE]
nzv <- nearZeroVar(testLess1,saveMetrics=TRUE)
testLess1 <- testLess1[,nzv$nzv==FALSE]
</code></pre>
<h4>Create cross validation set</h4>
<p>The training set is divided in two parts, one for training and the other for cross validation</p>
<pre><code class="r">set.seed(12031987)
inTrain = createDataPartition(trainLess1$classe, p = 3/4, list=FALSE)
training = trainLess1[inTrain,]
crossValidation = trainLess1[-inTrain,]
</code></pre>
<h4>Train model</h4>
<p>Train model with random forest due to its highly accuracy rate. The model is build on a training set of 28 variables from the initial 160. Cross validation is used as train control method.</p>
<pre><code class="r">modFit <- train(classe ~., method="rf", data=train, trControl=trainControl(method='cv'), number=5, allowParallel=TRUE )
</code></pre>
<pre><code>## Time difference of 13.05325 mins
## Random Forest
##
## 14718 samples
## 27 predictors
## 5 classes: 'A', 'B', 'C', 'D', 'E'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
##
## Summary of sample sizes: 13246, 13245, 13248, 13245, 13247, 13246, ...
##
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa Accuracy SD Kappa SD
## 2 0.993 0.991 0.00116 0.00147
## 14 0.992 0.99 0.0028 0.00354
## 27 0.989 0.987 0.00353 0.00446
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.
</code></pre>
<h4>Accuracy on training set and cross validation set</h4>
<p>Following the computation on the accuracy of trainig and cross validation set</p>
<p>Training set:</p>
<pre><code class="r">trainingPred <- predict(modFit, training)
confusionMatrix(trainingPred, training$classe)
</code></pre>
<pre><code>Confusion Matrix and Statistics
Reference
Prediction A B C D E
A 4185 0 0 0 0
B 0 2848 0 0 0
C 0 0 2567 0 0
D 0 0 0 2412 0
E 0 0 0 0 2706
Overall Statistics
Accuracy : 1
95% CI : (0.9997, 1)
No Information Rate : 0.2843
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 1
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: A Class: B Class: C Class: D Class: E
Sensitivity 1.0000 1.0000 1.0000 1.0000 1.0000
Specificity 1.0000 1.0000 1.0000 1.0000 1.0000
Pos Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
Neg Pred Value 1.0000 1.0000 1.0000 1.0000 1.0000
Prevalence 0.2843 0.1935 0.1744 0.1639 0.1839
Detection Rate 0.2843 0.1935 0.1744 0.1639 0.1839
Detection Prevalence 0.2843 0.1935 0.1744 0.1639 0.1839
Balanced Accuracy 1.0000 1.0000 1.0000 1.0000 1.0000
</code></pre>
<p>Cross validation set</p>
<pre><code class="r">cvPred <- predict(modFit, crossValidation)
confusionMatrix(cvPred, crossValidation$classe)
</code></pre>
<pre><code>Confusion Matrix and Statistics
Reference
Prediction A B C D E
A 1392 3 0 0 0
B 2 944 2 0 0
C 0 2 852 3 0
D 0 0 1 801 3
E 1 0 0 0 898
Overall Statistics
Accuracy : 0.9965
95% CI : (0.9945, 0.998)
No Information Rate : 0.2845
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.9956
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: A Class: B Class: C Class: D Class: E
Sensitivity 0.9978 0.9947 0.9965 0.9963 0.9967
Specificity 0.9991 0.9990 0.9988 0.9990 0.9998
Pos Pred Value 0.9978 0.9958 0.9942 0.9950 0.9989
Neg Pred Value 0.9991 0.9987 0.9993 0.9993 0.9993
Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
Detection Rate 0.2838 0.1925 0.1737 0.1633 0.1831
Detection Prevalence 0.2845 0.1933 0.1748 0.1642 0.1833
Balanced Accuracy 0.9985 0.9969 0.9976 0.9976 0.9982
</code></pre>
<h4>RESULTS</h4>
<p>Predictions on the real testing set</p>
<pre><code class="r">testingPred <- predict(modFit, testLess1)
testingPred
</code></pre>
<pre><code> [1] B A B A A E D B A A B C B A E E A B B B
</code></pre>
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