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Orange_juice_classification.R
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Orange_juice_classification.R
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##https://www.machinelearningplus.com/machine-learning/caret-package/
library(caret) #Machine learning model implementaiton
library(caTools) # train test data split
library(skimr) # Descriptive statistics
library(RANN)
orange = read.csv("C:/Users/H303937/Downloads/orange_juice_withmissing.csv")
str(orange)
head(orange)
summary(orange)
#number of missing elements
sapply(orange, function(x) sum(is.na(x)))
##spliting into train and test dataset
set.seed(100)
sample = sample.split(orange$Purchase, SplitRatio = 0.8)
train = orange[sample==TRUE,]
test = orange[sample==FALSE,]
#separating X and Y for later use
X = train[,2:18]
Y = train[1]
## Viewing descriptive statistics
skimmed = skim_to_wide(train)
skimmed[,c(1:5,9:11, 13, 15:16)]
skimmed_list = skim_to_list(train)
skimmed_list
# Preprocessing using caret
## using knn imputation for misisng values
preprocessed_data <- preProcess(train, method=c("knnImpute"))
preprocessed_data
sapply(train, function(x) sum(is.na(x)))
#predict the missing value for train
train = predict(preprocessed_data, newdata = train)
anyNA(train)
## One hot encoding
dummies_model <- dummyVars(Purchase ~ ., train)
train <- predict(dummies_model, train)
#converting into dataframe
train <- as.data.frame(train)
str(train) #Note purchase columns is not available.
## Transforming data
preprocess_range <- preProcess(train, method="range")
train <- predict(preprocess_range, newdata = train)
##Adding Purchase column
train$Purchase <- Y$Purchase
#checking min and max value column wise
apply(train, 2, function(x) c("min"=min(x), "max"= max(x)))
##featureplot
featurePlot(x = train[,0:18], y= train$Purchase, plot = "box")
featurePlot(x = train[,0:18], y=train$Purchase, plot='density')
## RFE - Recursive Feature Elimination
set.seed(100)
options(warn=-1)
subsets = c(1:5, 10,15,18)
ctrl <- rfeControl(functions = rfFuncs,
method ="repeatedcv",
repeats = 5,
verbose = FALSE)
lmProfile <- rfe(x=train[, 1:18], y=train['Purchase'],
sizes = subsets,
rfeControl = ctrl)
lmProfile
##training and tuning model
#model list available in caret
modelNames <- paste(names(getModelInfo()), collapse=',')
modelNames
modelLookup('earth')
##Applying MARS - Multivariate Adaptive Regression Splines
set.seed(100)
#train the modle
model_mars <- train(Purchase ~., data = train, metho='earth')
model_mars
plot(model_mars,main ="Model Accuracy with MARS")
## variable Importance using mars
varimp_mars <- varImp(model_mars)
plot(varimp_mars, main ="Variable importance using mars")
##Preprocessing test dataset
test1 <- predict(preprocessed_data, newdata = test)
test2 <- predict(dummies_model, newdata=test1)
test3 <- predict(preprocess_range, newdata = test2)
apply(test3, 2, function(x) c('min'=min(x), 'max'=max(x)) )
head(test3)
##Predict on testdata
preds <- predict(model_mars, test3)
##Confusion matrix
confusionMatrix(test$Purchase, preds, positive = "MM")
##Hyperparameter tuning
set.seed(100)
trainCtrl = trainControl(method = 'cv', number = 5, savePredictions = 'final',
classProbs = TRUE,
summaryFunction = twoClassSummary)
model_mars2 <- train(Purchase ~ ., data = train, method = 'earth', trControl = trainCtrl, tuneLength = 5,
metric ='ROC')
model_mars2
##Predicting on test dataset
preds = predict(model_mars2, newdata = test3)
confusionMatrix(test$Purchase, preds, positive = "MM")
## Hyper parameter tuning using tuneGrid
marsgrid <- expand.grid('nprune'= c(2,4,6,8,10), degree = c(1,2,3))
set.seed(100)
model_mars3 <- train(Purchase ~., data=train, method ='earth', tuneGrid = marsgrid,
metric = 'ROC', trControl = trainCtrl)
model_mars3
##Predicting on test set
preds = predict(model_mars3, newdata = test3)
confusionMatrix(test$Purchase, preds, positive = "MM")
## Training more models:
##ada boost
set.seed(100)
model_adaboost <- train(Purchase ~., data= train, method ='adaboost', tuneLength =2,
trControl = trainCtrl)
model_adaboost
## random forest
set.seed(100)
model_rf <- train(Purchase ~., data = train, method = 'rf', tuneLength =2,
trControl = trainCtrl)
model_rf
## xgboost DART
# set.seed(100)
# model_xgbDART <- train(Purchase ~., data = train, method='xgbDART', tuneLength=5,
# trControl = trainCtrl)
#
# model_xgbDART
## SVM
set.seed(100)
model_svmRadial <- train(Purchase ~., data = train, method='svmRadial', tuneLength= 15,
trControl = trainCtrl)
model_svmRadial
## Compare model performances
models_compare <- resamples(list(ADABOOST = model_adaboost, RF = model_rf,
MARS = model_mars3,
SVM = model_svmRadial))
summary(models_compare)
## Boxplots to compare model
scales <- list(x = list(relation ="free"), y = list(relation ="free"))
bwplot(models_compare, scales)
### Ensembling the predictions
library(caretEnsemble)
trainCtrl <- trainControl(method='repeatedcv', number = 10,repeats = 5 ,
savePredictions = TRUE, classProbs = TRUE)
algorithm_list <- c('rf','adaboost','svmRadial', 'earth')
set.seed(100)
models <- caretList(Purchase ~., data = train, methodList = algorithm_list, trControl = trainCtrl)
results <- resamples(models)
summary(results)
##boxplot of compare models
scales <- list(x = list(relation ='free'), y =list(relation ='free'))
bwplot(results, scales = scales)