project01

Importing libraries

library(mlbench) # Contains several benchmark data sets (especially the Boston Housing dataset) library(caret) # Package for machine learning algorithms / CARET stands for Classification And REgression Training

Importing the Boston Housing data set

data(BostonHousing)

head(BostonHousing)

Check to see if there are missing data?

sum(is.na(BostonHousing))

To achieve reproducible model; set the random seed number

set.seed(100)

Performs stratified random split of the data set

TrainingIndex <- createDataPartition(BostonHousing$medv, p=0.8, list = FALSE) TrainingSet <- BostonHousing[TrainingIndex,] # Training Set TestingSet <- BostonHousing[-TrainingIndex,] # Test Set

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Build Training model

Model <- train(medv ~ ., data = TrainingSet, method = "lm", na.action = na.omit, preProcess=c("scale","center"), trControl= trainControl(method="none") )

Apply model for prediction

Model.training <-predict(Model, TrainingSet) # Apply model to make prediction on Training set Model.testing <-predict(Model, TestingSet) # Apply model to make prediction on Testing set

Model performance (Displays scatter plot and performance metrics)

Scatter plot of Training set

plot(TrainingSet$medv,Model.training, col = "blue" )
plot(TestingSet$medv,Model.testing, col = "blue" )