Machine-Learning-Project
Abhijeet
Synopsis
This Report is a course project of the Practical Machine Learning Course of Data Science Specialization by Johns Hopkins University on Coursera. Aim of this project is to choose and apply machine learning algorithm to the 20 test cases in the course.The data for this project come from this source: http://groupware.les.inf.puc-rio.br/har
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, the goal is to use the 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
Loading the Data
- Loading the
trainandtestdata.
trainingData<-read.csv(url("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"),header = TRUE)
testingData<-read.csv(url("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"),header = TRUE)Exploring the Data
str(trainingData)## 'data.frame': 19622 obs. of 160 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ 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]- The set has 19622 observation and 160 variables. Many columns seems
to contain numerous empty
""andNAelements. Before analysis, it will be wise to clear the data for easier analysis.
Data Cleaning
- Removing columns with more than 90% NA’s or empty elements to clean the data for fitting the model.
#training Data
EmptyCols<-which(colSums(is.na(trainingData)| trainingData=="")>0.9*dim(trainingData)[1])
trainingData<-trainingData[,-EmptyCols]
# removing unwanted columns
trainingData<-trainingData[,-c(1:7)]
# testing Data
EmptyCols<-which(colSums(is.na(testingData)| testingData=="")>0.9*dim(testingData)[1])
testingData<-testingData[,-EmptyCols]
# removing unwanted columns
testingData<-testingData[,-c(1:7)]dim(trainingData)## [1] 19622 53dim(testingData)## [1] 20 53Data Splitting
- Preparing the data for training and testing by splitting 70% as
trainingand 30% astestingdataset.
suppressMessages(library(caret)); ## Warning: package 'caret' was built under R version 4.3.2inTrain <- createDataPartition(trainingData$classe,
p=0.7, list=FALSE)
# Training Data
training <- trainingData[inTrain,]
# Test Data
testing <- trainingData[-inTrain,]
dim(training)## [1] 13737 53Model Selection
- To predict the outcome, we will use three different methods to model regression.
- The Random Forest Model
rf, Gradient Boosting Modelgbmand Decision Tree Modelrpartalgorithms are used for accuracy comparison. - k-fold cross validation method is used to reduce
risk of overfitting.Number of folds are chosen to be
5to reduce the training time.
Random Forests Model
- fitting the training data to Random Forest Model
set.seed(33833)
# cross validation
train.control<-trainControl(method = "cv",number=5)
# Random Forest
model_rf<-train(classe~.,method="rf",data=training,trControl =train.control,verbose=FALSE)Validating the model on test Data
RF_predict<-predict(model_rf,newdata=testing);
RF_cm<- confusionMatrix(RF_predict,as.factor(testing$classe))
RF_cm## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1674 6 0 0 0
## B 0 1131 3 0 0
## C 0 2 1022 18 0
## D 0 0 1 945 0
## E 0 0 0 1 1082
##
## Overall Statistics
##
## Accuracy : 0.9947
## 95% CI : (0.9925, 0.9964)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9933
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9930 0.9961 0.9803 1.0000
## Specificity 0.9986 0.9994 0.9959 0.9998 0.9998
## Pos Pred Value 0.9964 0.9974 0.9808 0.9989 0.9991
## Neg Pred Value 1.0000 0.9983 0.9992 0.9962 1.0000
## Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Detection Rate 0.2845 0.1922 0.1737 0.1606 0.1839
## Detection Prevalence 0.2855 0.1927 0.1771 0.1607 0.1840
## Balanced Accuracy 0.9993 0.9962 0.9960 0.9900 0.9999# model accuracy
RF_cm$overall['Accuracy']## Accuracy
## 0.9947324Gradient Boosting Model
- fitting the training data to Gradient Boosting Model
model_gbm<-train(classe~.,method="gbm",data=training,trControl = train.control,verbose=FALSE)Validating the model accuracy on test Data
gbm_predict<-predict(model_gbm,newdata=testing);
gbm_cm<- confusionMatrix(as.factor(testing$classe),gbm_predict)
gbm_cm## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1648 23 3 0 0
## B 40 1064 28 3 4
## C 0 29 987 7 3
## D 1 1 36 923 3
## E 1 15 12 14 1040
##
## Overall Statistics
##
## Accuracy : 0.9621
## 95% CI : (0.9569, 0.9668)
## No Information Rate : 0.2872
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9521
##
## Mcnemar's Test P-Value : 3.99e-07
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9751 0.9399 0.9259 0.9747 0.9905
## Specificity 0.9938 0.9842 0.9919 0.9917 0.9913
## Pos Pred Value 0.9845 0.9342 0.9620 0.9575 0.9612
## Neg Pred Value 0.9900 0.9857 0.9837 0.9951 0.9979
## Prevalence 0.2872 0.1924 0.1811 0.1609 0.1784
## Detection Rate 0.2800 0.1808 0.1677 0.1568 0.1767
## Detection Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Balanced Accuracy 0.9845 0.9621 0.9589 0.9832 0.9909# model accuracy
gbm_cm$overall['Accuracy']## Accuracy
## 0.9621071Decision Tree Model
model_rpart<-train(classe~., method="rpart", data=training,trControl = train.control)- plotting the Decision Tree.
suppressMessages(library(rattle))## Warning: package 'rattle' was built under R version 4.3.2fancyRpartPlot(model_rpart$finalModel)
Validating the model accuracy on test Data.
rpart_predict<-predict(model_rpart,testing)
rpart_cm<- confusionMatrix(as.factor(testing$classe),rpart_predict)
# model accuracy
rpart_cm$overall['Accuracy']## Accuracy
## 0.4992353Comparison of results
- Accuracies:
print(paste0("Random Forest model accuracy: ",round(RF_cm$overall['Accuracy'],3)))## [1] "Random Forest model accuracy: 0.995"print(paste0("Gradient Boosting Model accuracy: ",round(gbm_cm$overall['Accuracy'],3)))## [1] "Gradient Boosting Model accuracy: 0.962"print(paste0("Decision Tree Model accuracy: ",round(rpart_cm$overall['Accuracy'],3)))## [1] "Decision Tree Model accuracy: 0.499"- The accuracy obtained form the Random Forest Model
rfprediction is 0.995 which better than the Gradient Boosting Modelgbmand Decision Tree Modelrpartprediction with the accuracy of 0.968 and 0.501 respectively.
Implimenting the best model
- After comparing the accuracy rate of three models, The accuracy obtained from ’Random Forest model` is best.
- Implementing the Random Forest model to the
testDatadata.
validation<-predict(model_rf,testingData)
validation## [1] B A B A A E D B A A B C B A E E A B B B
## Levels: A B C D E