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library(rsconnect)
library(rattle)## Loading required package: tibble## Loading required package: bitops## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.library(ggplot2)
library(caret)## Loading required package: latticelibrary(rpart)
library(rpart.plot)
library(corrplot)## corrplot 0.92 loadedlibrary(corrplot)
library(RColorBrewer)
library(lattice)
library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin## The following object is masked from 'package:rattle':
##
## importancelibrary(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(gbm)## Loaded gbm 2.1.8.1set.seed(222)
url_train <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv"
url_quiz <- "http://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv"
data_train <- read.csv(url(url_train), strip.white = TRUE, na.strings = c("NA",""))
data_quiz <- read.csv(url(url_quiz), strip.white = TRUE, na.strings = c("NA",""))
dim(data_train)## [1] 19622 160dim(data_quiz)## [1] 20 160in_train <- createDataPartition(data_train$classe, p = 0.75, list = FALSE)
train_set <- data_train[in_train,]
test_set <- data_train[-in_train,]
dim(train_set)## [1] 14718 160dim(test_set)## [1] 4904 160nzv_var <- nearZeroVar(train_set)
train_set <- train_set[,-nzv_var]
test_set <- test_set[,-nzv_var]
dim(train_set)## [1] 14718 120dim(test_set)## [1] 4904 120na_var<- sapply(train_set,function(x) mean(is.na(x))) > 0.95
train_set <- train_set[,na_var ==FALSE]
test_set <- test_set[,na_var ==FALSE]
dim(train_set)## [1] 14718 59dim(test_set)## [1] 4904 59train_set <- train_set[,-(1:5)]
test_set <- test_set [,-(1:5)]
dim(train_set)## [1] 14718 54dim(test_set)## [1] 4904 54corr_matrix <- cor(train_set[,-54])
corrplot(corr_matrix,order = "FPC",method = "circle",type = "lower",
t1.cex = 0.6, t1.col = rgb(0,0,0))## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "t1.cex" is not a graphical parameter## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "t1.col" is not a graphical parameter## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "t1.cex" is not a graphical parameter## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "t1.col" is not a graphical parameter## Warning in title(title, ...): "t1.cex" is not a graphical parameter## Warning in title(title, ...): "t1.col" is not a graphical parameter
###Decision Tree Model:
set.seed(2222)
fit_decision_tree <- rpart(classe ~ ., data = train_set, method="class")
fancyRpartPlot(fit_decision_tree)
predict_decision_tree <- predict(fit_decision_tree, newdata = test_set, type="class")
conf_matrix_decision_tree <- confusionMatrix(predict_decision_tree, factor(test_set$classe))
conf_matrix_decision_tree## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1238 218 37 76 36
## B 41 547 28 30 19
## C 8 53 688 114 38
## D 70 91 50 518 111
## E 38 40 52 66 697
##
## Overall Statistics
##
## Accuracy : 0.752
## 95% CI : (0.7397, 0.7641)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.685
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.8875 0.5764 0.8047 0.6443 0.7736
## Specificity 0.8954 0.9702 0.9474 0.9215 0.9510
## Pos Pred Value 0.7713 0.8226 0.7636 0.6167 0.7805
## Neg Pred Value 0.9524 0.9052 0.9583 0.9296 0.9491
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2524 0.1115 0.1403 0.1056 0.1421
## Detection Prevalence 0.3273 0.1356 0.1837 0.1713 0.1821
## Balanced Accuracy 0.8914 0.7733 0.8760 0.7829 0.8623plot(conf_matrix_decision_tree$table, col = conf_matrix_decision_tree$byClass,
main = paste("Decision Tree Model: Predictive Accuracy =",
round(conf_matrix_decision_tree$overall['Accuracy'], 4)))
set.seed(2222)
ctrl_GBM <- trainControl(method = "repeatedcv", number = 5, repeats = 2)
fit_GBM <- train(classe ~ ., data = train_set, method = "gbm",
trControl = ctrl_GBM, verbose = FALSE)
fit_GBM$finalModel## A gradient boosted model with multinomial loss function.
## 150 iterations were performed.
## There were 53 predictors of which 52 had non-zero influence.predict_GBM <- predict(fit_GBM, newdata = test_set)
conf_matrix_GBM <- confusionMatrix(predict_GBM, factor(test_set$classe))
conf_matrix_GBM## Confusion Matrix and Statistics
##
## Reference
## Prediction A B C D E
## A 1393 7 0 1 0
## B 2 929 3 4 2
## C 0 11 842 12 1
## D 0 2 10 784 9
## E 0 0 0 3 889
##
## Overall Statistics
##
## Accuracy : 0.9863
## 95% CI : (0.9827, 0.9894)
## No Information Rate : 0.2845
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9827
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: A Class: B Class: C Class: D Class: E
## Sensitivity 0.9986 0.9789 0.9848 0.9751 0.9867
## Specificity 0.9977 0.9972 0.9941 0.9949 0.9993
## Pos Pred Value 0.9943 0.9883 0.9723 0.9739 0.9966
## Neg Pred Value 0.9994 0.9950 0.9968 0.9951 0.9970
## Prevalence 0.2845 0.1935 0.1743 0.1639 0.1837
## Detection Rate 0.2841 0.1894 0.1717 0.1599 0.1813
## Detection Prevalence 0.2857 0.1917 0.1766 0.1642 0.1819
## Balanced Accuracy 0.9981 0.9881 0.9894 0.9850 0.9930set.seed(2222)
ctrl_RF <- trainControl(method = "repeatedcv", number = 5, repeats = 2)
fit_RF <- train(classe ~ ., data = train_set, method = "rf",
trControl = ctrl_RF, verbose = FALSE)
fit_RF$finalModel##
## Call:
## randomForest(x = x, y = y, mtry = param$mtry, verbose = FALSE)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 27
##
## OOB estimate of error rate: 0.24%
## Confusion matrix:
## A B C D E class.error
## A 4183 1 0 0 1 0.0004778973
## B 8 2836 3 1 0 0.0042134831
## C 0 6 2561 0 0 0.0023373588
## D 0 0 7 2404 1 0.0033167496
## E 0 1 0 7 2698 0.0029563932predict_quiz <- as.data.frame(predict(fit_RF, newdata = data_quiz))
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