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installed.packages("rpart.plot")

##      Package LibPath Version Priority Depends Imports LinkingTo Suggests
##      Enhances License License_is_FOSS License_restricts_use OS_type Archs
##      MD5sum NeedsCompilation Built

library(rpart)
library(rpart.plot)
library(caret)

## Loading required package: ggplot2

## Loading required package: lattice

dataset<-read.csv("D:/Rencana Tugas Akhir/DLI.csv",sep=";")
ctrl <- trainControl(method = "cv", number = 2)
m<- train(X.Y. ~ X.X1.+ X.X2.+X.X3.+X.X4.+X.X5.+X.X6.+X.X7., data = dataset, method = "lm", trControl = ctrl)

## Warning in train.default(x, y, weights = w, ...): You are trying to do
## regression and your outcome only has two possible values Are you trying to do
## classification? If so, use a 2 level factor as your outcome column.

print(m)

## Linear Regression 
## 
## 100 samples
##   7 predictor
## 
## No pre-processing
## Resampling: Cross-Validated (2 fold) 
## Summary of sample sizes: 50, 50 
## Resampling results:
## 
##   RMSE      Rsquared    MAE      
##   0.389844  0.05015367  0.2924235
## 
## Tuning parameter 'intercept' was held constant at a value of TRUE

m$finalModel

## 
## Call:
## lm(formula = .outcome ~ ., data = dat)
## 
## Coefficients:
## (Intercept)        X.X1.        X.X2.        X.X3.        X.X4.        X.X5.  
##     0.11214     -0.07636      0.04307     -0.04678      0.16016      0.16314  
##       X.X6.        X.X7.  
##    -0.02540      0.11430

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