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<p>This is the Writeup for the Course Project of the “Practical Machine Learning” course on Coursera.</p>
<p>Six young health participants were asked to perform one set of 10 repetitions of the Unilateral Dumbbell Biceps Curl in five different fashions (the correct way, class A, and in ways corresponding to common mistakes (classes B-E)): </p>
<ul>
<li>exactly according to the specification (Class A)</li>
<li>throwing the elbows to the front (Class B)</li>
<li>lifting the dumbbell only halfway (Class C)</li>
<li>lowering the dumbbell only halfway (Class D)</li>
<li>throwing the hips to the front (Class E).</li>
</ul>
<p>In this project, my goal was to use data from accelerometers on the belt, forearm, arm, and dumbell of these participants to predict “which” activity was performed at a specific point in time.</p>
<p>Data source: <a href="http://groupware.les.inf.puc-rio.br/har">http://groupware.les.inf.puc-rio.br/har</a>, section on the Weight Lifting Exercise Dataset.
Velloso, E.; Bulling, A.; Gellersen, H.; Ugulino, W.; Fuks, H. Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of 4th International Conference in Cooperation with SIGCHI (Augmented Human '13) . Stuttgart, Germany: ACM SIGCHI, 2013</p>
<pre><code class="r">#setwd("~/Coursera_Data_science/Course8_Machine_Learning")
training<-read.csv("pml-training.csv",header=TRUE,na.string = c("", "NA"))
#str(training)
</code></pre>
<p>The dataset contains 160 variables, but a lot of columns have many empty fields. Preprocessing the dataset can consist in only keeping the complete cases.</p>
<pre><code class="r">library(ggplot2)
library(caret)
library(rattle)
</code></pre>
<pre><code>## Error in library(rattle): there is no package called 'rattle'
</code></pre>
<pre><code class="r">set.seed(4528)
#keep only the variables that have no NAs
training<-training[,complete.cases(t(training))==TRUE]
## Remove variables that have newar zero variance
nsv<-nearZeroVar(training,saveMetrics=TRUE)
training<-training[,c(nsv$nzv==FALSE)]
# Remove the first columns which dont contain measurements but only metadata
training<-training[,7:59]
# Split the data into training and testing
inTrain<-createDataPartition(y=training$classe,p=0.75,list=FALSE)
train<-training[inTrain,]
test<-training[-inTrain,]
dim(train)
</code></pre>
<pre><code>## [1] 14718 53
</code></pre>
<pre><code class="r">dim(test)
</code></pre>
<pre><code>## [1] 4904 53
</code></pre>
<pre><code class="r">### set trainControl parameter for cross validation
CV<-trainControl(method="cv",number=10)
</code></pre>
<pre><code class="r">fit_rpart<-train(classe ~., method="rpart",data=train,trControl=CV)
pred_rpart<-predict(fit_rpart,test)
confusionMatrix(pred_rpart,test$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1277 400 400 372 133
## B 11 251 13 117 43
## C 103 298 442 315 307
## D 0 0 0 0 0
## E 4 0 0 0 418
##
## Overall Statistics
##
## Accuracy : 0.4869
## 95% CI : (0.4729, 0.501)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3295
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9154 0.26449 0.51696 0.0000 0.46393
## Specificity 0.6281 0.95348 0.74735 1.0000 0.99900
## Pos Pred Value 0.4946 0.57701 0.30171 NaN 0.99052
## Neg Pred Value 0.9492 0.84381 0.87991 0.8361 0.89224
## Prevalence 0.2845 0.19352 0.17435 0.1639 0.18373
## Detection Rate 0.2604 0.05118 0.09013 0.0000 0.08524
## Detection Prevalence 0.5265 0.08870 0.29874 0.0000 0.08605
## Balanced Accuracy 0.7718 0.60898 0.63215 0.5000 0.73146
</code></pre>
<pre><code class="r">accuracy_rpart<-confusionMatrix(pred_rpart,test$classe)$overall[1]
fancyRpartPlot(fit_rpart)
</code></pre>
<pre><code>## Error in eval(expr, envir, enclos): could not find function "fancyRpartPlot"
</code></pre>
<pre><code class="r">fit_lda<-train(classe ~., method="lda",data=train,trControl=CV)
pred_lda<-predict(fit_lda,test)
confusionMatrix(pred_lda,test$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1142 141 77 57 24
## B 32 606 92 26 159
## C 106 127 563 86 92
## D 105 30 104 601 96
## E 10 45 19 34 530
##
## Overall Statistics
##
## Accuracy : 0.7019
## 95% CI : (0.6889, 0.7147)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6229
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8186 0.6386 0.6585 0.7475 0.5882
## Specificity 0.9148 0.9219 0.8985 0.9183 0.9730
## Pos Pred Value 0.7925 0.6623 0.5780 0.6421 0.8307
## Neg Pred Value 0.9269 0.9140 0.9257 0.9488 0.9130
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2329 0.1236 0.1148 0.1226 0.1081
## Detection Prevalence 0.2938 0.1866 0.1986 0.1909 0.1301
## Balanced Accuracy 0.8667 0.7802 0.7785 0.8329 0.7806
</code></pre>
<pre><code class="r">accuracy_lda<-confusionMatrix(pred_lda,test$classe)$overall[1]
fit_gbm<-train(classe ~., method="gbm",data=train,verbose=FALSE,trControl=CV)
pred_gbm<-predict(fit_gbm,test)
confusionMatrix(pred_gbm,test$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1372 29 1 2 0
## B 20 894 24 5 6
## C 2 24 817 22 6
## D 1 2 13 770 10
## E 0 0 0 5 879
##
## Overall Statistics
##
## Accuracy : 0.9649
## 95% CI : (0.9594, 0.9699)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9556
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9835 0.9420 0.9556 0.9577 0.9756
## Specificity 0.9909 0.9861 0.9867 0.9937 0.9988
## Pos Pred Value 0.9772 0.9420 0.9380 0.9673 0.9943
## Neg Pred Value 0.9934 0.9861 0.9906 0.9917 0.9945
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2798 0.1823 0.1666 0.1570 0.1792
## Detection Prevalence 0.2863 0.1935 0.1776 0.1623 0.1803
## Balanced Accuracy 0.9872 0.9641 0.9711 0.9757 0.9872
</code></pre>
<pre><code class="r">accuracy_gbm<-confusionMatrix(pred_gbm,test$classe)$overall[1]
fit_rf<-train(classe ~., method="rf",data=train,verbose=FALSE,trControl=CV)
pred_rf<-predict(fit_rf,test)
confusionMatrix(pred_rf,test$classe)
</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1394 6 0 0 0
## B 1 942 4 1 0
## C 0 1 846 9 3
## D 0 0 5 792 2
## E 0 0 0 2 896
##
## Overall Statistics
##
## Accuracy : 0.9931
## 95% CI : (0.9903, 0.9952)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9912
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9993 0.9926 0.9895 0.9851 0.9945
## Specificity 0.9983 0.9985 0.9968 0.9983 0.9995
## Pos Pred Value 0.9957 0.9937 0.9849 0.9912 0.9978
## Neg Pred Value 0.9997 0.9982 0.9978 0.9971 0.9988
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2843 0.1921 0.1725 0.1615 0.1827
## Detection Prevalence 0.2855 0.1933 0.1752 0.1629 0.1831
## Balanced Accuracy 0.9988 0.9956 0.9931 0.9917 0.9970
</code></pre>
<pre><code class="r">accuracy_rf<-confusionMatrix(pred_rf,test$classe)$overall[1]
</code></pre>
<p>The out of sample error ranges between 'r (1-accuracy_rpart)*100'% for rpart to 'r (1-accuracy_rf)*100'% for random forest.
Now that the models have been trainined and seem to perform really well on the cross validation,
it is time to apply to the prediction of the 20 samples. </p>
<pre><code class="r">testing<-read.csv("pml-testing.csv",header=TRUE,na.string = c("", "NA"))
testing<-testing[,colnames(testing) %in% colnames(training)]
test_pred_rpart<-predict(fit_rpart,testing,type="prob")
test_pred_lda<-predict(fit_lda,testing,type="prob")
test_pred_gbm<-predict(fit_gbm,testing,type="prob")
test_pred_rf<-predict(fit_rf, testing,type="prob")
# We can have the final prediction weighted by the accuracy in the cross validation.
preds<-round(test_pred_rpart*accuracy_rpart+test_pred_lda*accuracy_lda+test_pred_gbm*accuracy_gbm
+test_pred_rf*accuracy_rf,2)
preds
</code></pre>
<pre><code>## A B C D E
## 1 0.16 1.68 0.78 0.36 0.17
## 2 2.71 0.21 0.10 0.08 0.04
## 3 0.46 2.08 0.31 0.12 0.17
## 4 2.21 0.11 0.61 0.18 0.04
## 5 2.43 0.15 0.42 0.10 0.05
## 6 0.04 0.20 0.53 0.29 2.08
## 7 0.06 0.16 0.41 2.35 0.17
## 8 0.31 1.40 0.37 0.88 0.19
## 9 3.13 0.01 0.00 0.00 0.00
## 10 2.59 0.17 0.23 0.11 0.04
## 11 0.19 1.76 0.46 0.58 0.15
## 12 0.75 0.24 1.74 0.19 0.22
## 13 0.07 2.31 0.21 0.13 0.42
## 14 3.11 0.01 0.02 0.00 0.00
## 15 0.05 0.40 0.23 0.16 2.32
## 16 0.59 0.29 0.28 0.19 1.80
## 17 2.45 0.12 0.33 0.09 0.16
## 18 0.28 2.26 0.12 0.35 0.14
## 19 0.61 2.12 0.18 0.17 0.07
## 20 0.04 2.72 0.15 0.10 0.13
</code></pre>
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