Peer-graded Assignment: Prediction Assignment Writeup
June 22, 2017
K.S.S SIVA TEJA
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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://groupware.les.inf.puc-rio.br/har (see the section on the Weight Lifting Exercise Dataset).
Data
The training data for this project is here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv
The test data are available here:
https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv
library(caret); library(rpart);library(ggplot2);library(randomForest)## Loading required package: lattice## Loading required package: ggplot2## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginLoading Data into R
Check the training and testing data, identifying the missing data, “NA” and “#DIV/0!” as “NA” everywhere.
url.training <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
url.testing <- "https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
training <- read.csv(url(url.training), na.strings = c("NA", "", "#DIV0!"))
testing <- read.csv(url(url.testing), na.strings = c("NA", "", "#DIV0!"))We need to define the same columns
sameColumsName <- colnames(training) == colnames(testing)
colnames(training)[sameColumsName==FALSE]## [1] "classe"The “classe” is not included in the testing data.
Cleaning data
training<-training[,colSums(is.na(training)) == 0]
testing <-testing[,colSums(is.na(testing)) == 0]Checking the column names
head(colnames(training))## [1] "X" "user_name" "raw_timestamp_part_1"
## [4] "raw_timestamp_part_2" "cvtd_timestamp" "new_window"The first 7 variables of the training data were deleted, because they are irrelevant to the prediction.
training <- training[,8:dim(training)[2]]
testing <- testing[,8:dim(testing)[2]]Training, testing & validation data
The training dataset was separated into three parts: tranining part (60%), testing part (20%), and validation part (20%)
set.seed(123)
Seeddata1 <- createDataPartition(y = training$classe, p = 0.8, list = F)
Seeddata2 <- training[Seeddata1,]
validation <- training[-Seeddata1,]
Training_data1 <- createDataPartition(y = Seeddata2$classe, p = 0.75, list = F)
training_data2 <- Seeddata2[Training_data1,]
testing_data <- Seeddata2[-Training_data1,]Data exploration
qplot(classe,fill = "4",data=training_data2,main="Distribution of Classes")
Findout the predictors
names(training_data2[,-53])## [1] "roll_belt" "pitch_belt" "yaw_belt"
## [4] "total_accel_belt" "gyros_belt_x" "gyros_belt_y"
## [7] "gyros_belt_z" "accel_belt_x" "accel_belt_y"
## [10] "accel_belt_z" "magnet_belt_x" "magnet_belt_y"
## [13] "magnet_belt_z" "roll_arm" "pitch_arm"
## [16] "yaw_arm" "total_accel_arm" "gyros_arm_x"
## [19] "gyros_arm_y" "gyros_arm_z" "accel_arm_x"
## [22] "accel_arm_y" "accel_arm_z" "magnet_arm_x"
## [25] "magnet_arm_y" "magnet_arm_z" "roll_dumbbell"
## [28] "pitch_dumbbell" "yaw_dumbbell" "total_accel_dumbbell"
## [31] "gyros_dumbbell_x" "gyros_dumbbell_y" "gyros_dumbbell_z"
## [34] "accel_dumbbell_x" "accel_dumbbell_y" "accel_dumbbell_z"
## [37] "magnet_dumbbell_x" "magnet_dumbbell_y" "magnet_dumbbell_z"
## [40] "roll_forearm" "pitch_forearm" "yaw_forearm"
## [43] "total_accel_forearm" "gyros_forearm_x" "gyros_forearm_y"
## [46] "gyros_forearm_z" "accel_forearm_x" "accel_forearm_y"
## [49] "accel_forearm_z" "magnet_forearm_x" "magnet_forearm_y"
## [52] "magnet_forearm_z"Prediction model (Classification Tree model)
model_tree <- rpart(classe ~ ., data=training_data2, method="class")
prediction_tree <- predict(model_tree, testing_data, type="class")
class_tree <- confusionMatrix(prediction_tree, testing_data$classe)
class_tree## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 944 111 7 53 42
## B 53 492 51 59 53
## C 49 98 447 54 51
## D 47 33 178 468 70
## E 23 25 1 9 505
##
## Overall Statistics
##
## Accuracy : 0.728
## 95% CI : (0.7138, 0.7419)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6559
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8459 0.6482 0.6535 0.7278 0.7004
## Specificity 0.9241 0.9317 0.9222 0.9000 0.9819
## Pos Pred Value 0.8159 0.6949 0.6395 0.5879 0.8970
## Neg Pred Value 0.9378 0.9170 0.9265 0.9440 0.9357
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2406 0.1254 0.1139 0.1193 0.1287
## Detection Prevalence 0.2949 0.1805 0.1782 0.2029 0.1435
## Balanced Accuracy 0.8850 0.7900 0.7879 0.8139 0.8412Checking the model_tree
library(rpart.plot)
rpart.plot(model_tree)
Random forest model
forest_model <- randomForest(classe ~ ., data=training_data2, method="class")
prediction_forest <- predict(forest_model, testing_data, type="class")
random_forest <- confusionMatrix(prediction_forest, testing_data$classe)
random_forest## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1116 2 0 0 0
## B 0 753 5 0 0
## C 0 3 677 9 0
## D 0 1 2 634 2
## E 0 0 0 0 719
##
## Overall Statistics
##
## Accuracy : 0.9939
## 95% CI : (0.9909, 0.9961)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9923
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9921 0.9898 0.9860 0.9972
## Specificity 0.9993 0.9984 0.9963 0.9985 1.0000
## Pos Pred Value 0.9982 0.9934 0.9826 0.9922 1.0000
## Neg Pred Value 1.0000 0.9981 0.9978 0.9973 0.9994
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2845 0.1919 0.1726 0.1616 0.1833
## Detection Prevalence 0.2850 0.1932 0.1756 0.1629 0.1833
## Balanced Accuracy 0.9996 0.9953 0.9930 0.9922 0.9986Final prediction
prediction1 <- predict(forest_model, newdata=testing_data)
confusionMatrix(prediction1, testing_data$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1116 2 0 0 0
## B 0 753 5 0 0
## C 0 3 677 9 0
## D 0 1 2 634 2
## E 0 0 0 0 719
##
## Overall Statistics
##
## Accuracy : 0.9939
## 95% CI : (0.9909, 0.9961)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9923
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9921 0.9898 0.9860 0.9972
## Specificity 0.9993 0.9984 0.9963 0.9985 1.0000
## Pos Pred Value 0.9982 0.9934 0.9826 0.9922 1.0000
## Neg Pred Value 1.0000 0.9981 0.9978 0.9973 0.9994
## Prevalence 0.2845 0.1935 0.1744 0.1639 0.1838
## Detection Rate 0.2845 0.1919 0.1726 0.1616 0.1833
## Detection Prevalence 0.2850 0.1932 0.1756 0.1629 0.1833
## Balanced Accuracy 0.9996 0.9953 0.9930 0.9922 0.9986The Random Forest is a better predictive model than the Decision Tree, which has accuracy of(99.91%).
Conclusions
In this study, the characteristics of predictors for both traning and testing datasets are reduced. The training dataset is splitted into subtraining and validation parts to construct a predictive model and evaluate its accuracy. Decision Tree and Random Forest are applied.The Random Forest is a much better predictive model than the Decision Tree, which has a larger accuracy (99.91%).