Comparison of model selection methods for Boston dataset

This repository is dedicated for learning linear regression on Boston housing data set using R

In this project, for supervised learning, I used regression and decision tree techniques to build predictive models and tested model accuracy by evaluating MSE and misclassification cost. For unsupervised learning, I performed cluster analysis on Iris dataset to identify subgroups and I used association rules to analyze transaction details in th…

Machine learning model implemented to accurately predict the housing prices in Boston suburbs.

Objective of the repository is to learn and build machine learning models using Python for data Housing Median Price ( BostonHousing).