Practical Machine Learning
Dai Yichao (IVAN)
6/28/2020
Introduction:
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, our goal is to use data from accelerometers on the belt, forearm, arm, and dumbell of 6 participants to predict the activities of the human.
Here is some reference about this project. You can check it further understand what is the project is doing here.
Data download and reading:
First, we need to download the dataset from the Training Data Link and the Testing Data Link. And then read the data into R.
if(!file.exists(paste0(getwd(),"/pml-training.csv"))){
download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv",
destfile = "./Data/training.csv", method = "curl")}
if(!file.exists(paste0(getwd(),"/pml-testing.csv"))){
download.file("https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv",
destfile = "./Data/test.csv",method = "curl")}
trainingDat = read.csv("pml-training.csv")
testingDat = read.csv("pml-testing.csv")Packages needs:
library(ggplot2)
library(caret)## Loading required package: latticelibrary(randomForest)## randomForest 4.6-14## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## marginlibrary(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:randomForest':
##
## combineCleaning Data
We can first remove the first variables, which does not have any predictor power.
trainingDat = trainingDat[,-c(1:5)]We observe that some of variable have alot of missig value. As a result, we should not use these variable as the predictor variables. These variables lost almost 100% values, it does not a good idea to use these variables as preditors.
idex = (colSums(is.na(trainingDat))/19622)<0.2
full_variable = names(trainingDat)[idex]
trainingDatt = trainingDat[,full_variable]
sum(colSums(is.na(trainingDatt)))## [1] 0Also, there are some variables have lots of space and does not have any useful value. As a result, we should also remove these value too. Through the following fuction, we can observe that these variables almost are factor variables.We should first exclude the response variables.
ALso, the number of window and new window are just some methods, we should also exclude them.
str(trainingDatt)## 'data.frame': 19622 obs. of 88 variables:
## $ new_window : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 397 levels "","-0.016850",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_belt : Factor w/ 317 levels "","-0.021887",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt : Factor w/ 395 levels "","-0.003095",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_belt.1 : Factor w/ 338 levels "","-0.005928",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_belt : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_yaw_belt : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_yaw_belt : Factor w/ 68 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_yaw_belt : Factor w/ 4 levels "","#DIV/0!","0.00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 ...
## $ 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 : Factor w/ 330 levels "","-0.02438",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_arm : Factor w/ 328 levels "","-0.00484",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_arm : Factor w/ 395 levels "","-0.01548",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_arm : Factor w/ 331 levels "","-0.00051",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_arm : Factor w/ 328 levels "","-0.00184",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_arm : Factor w/ 395 levels "","-0.00311",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ 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 : Factor w/ 398 levels "","-0.0035","-0.0073",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_dumbbell: Factor w/ 401 levels "","-0.0163","-0.0233",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_dumbbell : Factor w/ 401 levels "","-0.0082","-0.0096",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_dumbbell: Factor w/ 402 levels "","-0.0053","-0.0084",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_dumbbell : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_yaw_dumbbell : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_yaw_dumbbell : Factor w/ 73 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_yaw_dumbbell : Factor w/ 3 levels "","#DIV/0!","0.00": 1 1 1 1 1 1 1 1 1 1 ...
## $ total_accel_dumbbell : int 37 37 37 37 37 37 37 37 37 37 ...
## $ gyros_dumbbell_x : num 0 0 0 0 0 0 0 0 0 0 ...
## $ gyros_dumbbell_y : num -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 -0.02 ...
## $ gyros_dumbbell_z : num 0 0 0 -0.02 0 0 0 0 0 0 ...
## $ accel_dumbbell_x : int -234 -233 -232 -232 -233 -234 -232 -234 -232 -235 ...
## $ accel_dumbbell_y : int 47 47 46 48 48 48 47 46 47 48 ...
## $ accel_dumbbell_z : int -271 -269 -270 -269 -270 -269 -270 -272 -269 -270 ...
## $ magnet_dumbbell_x : int -559 -555 -561 -552 -554 -558 -551 -555 -549 -558 ...
## $ magnet_dumbbell_y : int 293 296 298 303 292 294 295 300 292 291 ...
## $ magnet_dumbbell_z : num -65 -64 -63 -60 -68 -66 -70 -74 -65 -69 ...
## $ roll_forearm : num 28.4 28.3 28.3 28.1 28 27.9 27.9 27.8 27.7 27.7 ...
## $ pitch_forearm : num -63.9 -63.9 -63.9 -63.9 -63.9 -63.9 -63.9 -63.8 -63.8 -63.8 ...
## $ yaw_forearm : num -153 -153 -152 -152 -152 -152 -152 -152 -152 -152 ...
## $ kurtosis_roll_forearm : Factor w/ 322 levels "","-0.0227","-0.0359",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_picth_forearm : Factor w/ 323 levels "","-0.0073","-0.0442",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ kurtosis_yaw_forearm : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_roll_forearm : Factor w/ 323 levels "","-0.0004","-0.0013",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_pitch_forearm : Factor w/ 319 levels "","-0.0113","-0.0131",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ skewness_yaw_forearm : Factor w/ 2 levels "","#DIV/0!": 1 1 1 1 1 1 1 1 1 1 ...
## $ max_yaw_forearm : Factor w/ 45 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ min_yaw_forearm : Factor w/ 45 levels "","-0.1","-0.2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ amplitude_yaw_forearm : Factor w/ 3 levels "","#DIV/0!","0.00": 1 1 1 1 1 1 1 1 1 1 ...
## $ total_accel_forearm : int 36 36 36 36 36 36 36 36 36 36 ...
## $ gyros_forearm_x : num 0.03 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.03 0.02 ...
## $ gyros_forearm_y : num 0 0 -0.02 -0.02 0 -0.02 0 -0.02 0 0 ...
## $ gyros_forearm_z : num -0.02 -0.02 0 0 -0.02 -0.03 -0.02 0 -0.02 -0.02 ...
## $ accel_forearm_x : int 192 192 196 189 189 193 195 193 193 190 ...
## $ accel_forearm_y : int 203 203 204 206 206 203 205 205 204 205 ...
## $ accel_forearm_z : int -215 -216 -213 -214 -214 -215 -215 -213 -214 -215 ...
## $ magnet_forearm_x : int -17 -18 -18 -16 -17 -9 -18 -9 -16 -22 ...
## $ magnet_forearm_y : num 654 661 658 658 655 660 659 660 653 656 ...
## $ magnet_forearm_z : num 476 473 469 469 473 478 470 474 476 473 ...
## $ classe : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...p = rep(1,88)
for(i in 1:88){
p[i] = !is.factor(trainingDatt[,i])}
p[1]=1
p[88]=1
p = (p==1)
trainingDatt = trainingDatt[,p]
trainingDatt = trainingDatt[,-c(1,2)]Data Exploratory anaysis
now, we have cleaned up the trainig dataset, we should do some data analysis:
table(trainingDatt$classe)##
## A B C D E
## 5580 3797 3422 3216 3607x = ggplot(data = trainingDatt,aes(x =gyros_belt_x))+geom_density()+
labs(title=c("Gyros_belt_x in six differet Huma activites"))+facet_grid(.~classe)
y = ggplot(data = trainingDatt,aes(x =gyros_belt_y))+geom_density()+
labs(title=c("Gyros_belt_y in six differet Huma activites"))+facet_grid(.~classe)
z = ggplot(data = trainingDatt,aes(x =gyros_belt_y))+geom_density()+
labs(title=c("Gyros_belt_z in six differet Huma activites"))+facet_grid(.~classe)
grid.arrange(x,y,z)
Here are part of the comparison about the Gyros_belt i 3 direction, which we can see the slightly different among these directions.
Data Patition
We will do the Data Patition for the trainingDatt data set, which 70% of the data will goes to the trainig set ad other data will goes to the testing set.
inTrain = createDataPartition(trainingDatt$classe,p = 0.7,list = FALSE)
Training = trainingDatt[inTrain,]
Testing = trainingDatt[-inTrain,]Model Fittig
In this case, we will directly use the accurate method random forest, and Boostig to fit the model:
set.seed(125)
controlRF <- trainControl(method="cv", number=3, verboseIter=FALSE)
controlGBM <- trainControl(method = "repeatedcv", number = 5, repeats = 1)
modfitRF = train(classe ~ ., method = "rf", data = Training,trControl=controlRF)modfitGBM <- train(classe~.,method="gbm",trControl = controlGBM,,data=Training,verbose = FALSE)Now, we have fit the model, we need to check the accuracy of each model
predRF <- predict(modfitRF, newdata=Testing[,-53])
predGBM <- predict(modfitGBM, newdata=Testing[,-53])
confusionMatrix(predRF, Testing$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1674 2 0 0 0
## B 0 1130 4 0 0
## C 0 7 1021 16 0
## D 0 0 1 948 1
## E 0 0 0 0 1081
##
## Overall Statistics
##
## Accuracy : 0.9947
## 95% CI : (0.9925, 0.9964)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9933
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 1.0000 0.9921 0.9951 0.9834 0.9991
## Specificity 0.9995 0.9992 0.9953 0.9996 1.0000
## Pos Pred Value 0.9988 0.9965 0.9780 0.9979 1.0000
## Neg Pred Value 1.0000 0.9981 0.9990 0.9968 0.9998
## Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Detection Rate 0.2845 0.1920 0.1735 0.1611 0.1837
## Detection Prevalence 0.2848 0.1927 0.1774 0.1614 0.1837
## Balanced Accuracy 0.9998 0.9956 0.9952 0.9915 0.9995confusionMatrix(predGBM, Testing$classe)## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1656 34 0 1 5
## B 14 1073 29 5 15
## C 3 31 983 31 8
## D 1 1 12 920 14
## E 0 0 2 7 1040
##
## Overall Statistics
##
## Accuracy : 0.9638
## 95% CI : (0.9587, 0.9684)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9542
##
## Mcnemar's Test P-Value : 5.256e-07
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9892 0.9421 0.9581 0.9544 0.9612
## Specificity 0.9905 0.9867 0.9850 0.9943 0.9981
## Pos Pred Value 0.9764 0.9445 0.9309 0.9705 0.9914
## Neg Pred Value 0.9957 0.9861 0.9911 0.9911 0.9913
## Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
## Detection Rate 0.2814 0.1823 0.1670 0.1563 0.1767
## Detection Prevalence 0.2882 0.1930 0.1794 0.1611 0.1782
## Balanced Accuracy 0.9899 0.9644 0.9715 0.9743 0.9797Compared the result, we can find the method random forest is better. As a result, we should this model as the final model to do the final test.
Using the model to predict:
first, we should clean the test data set:
gg = names(Training)
gg = gg[1:52]
testingDat = testingDat[,gg]
pred <- predict(modfitRF, newdata=testingDat)
data.frame(index = 1:20, predictios = pred)## index predictios
## 1 1 B
## 2 2 A
## 3 3 B
## 4 4 A
## 5 5 A
## 6 6 E
## 7 7 D
## 8 8 B
## 9 9 A
## 10 10 A
## 11 11 B
## 12 12 C
## 13 13 B
## 14 14 A
## 15 15 E
## 16 16 E
## 17 17 A
## 18 18 B
## 19 19 B
## 20 20 B