Lab 4 Multiple Linear Regression and Regularization in #RapidMiner
Channel: LBSocial
Date: 17/09/2020

Objectives:
• To learn how to predict data with the multiple linear regression models and the generalized linear regression models.

Data: 
• Download the house price excels file from https://github.com/xbwei/machine_learning_in_rapidminer/blob/master/house_price.xlsx 

Steps:
1. Load the house_price data into RapidMiner.
2. Establish a Multi Linear Regression model to predict prices with areas, lot size, # of bedrooms, and # of bathrooms, report the R square of the training data and the test data. 
3. Establish Ridge (L2) models and Lasso (L1) models to predict prices with different penalties (lambdas), describe and compare how the coefficients change with different lambdas in the Ridge models and the Lasso models. 
4. Visualize the R squares of the training data and the test data with different lambdas from the Ridge models and the Lasso models.



More about RapidMiner: https://www.youtube.com/playlist?list=PLHutrxqbP1Bx-zZxtex-EgwMFsjjjwYGO

For more concise tutorials, please check the following.

Machine Learning in Rapidminer: https://www.youtube.com/playlist?list=PLHutrxqbP1BzyutPkuzTXPwAroHS9j8Vd

Machine Learning in Python: https://www.youtube.com/playlist?list=PLHutrxqbP1ByXVJyAJe5zrnNkZA11xJI0