Using Ridge, Bayesian, Lasso, Elastic Net, and OLS regression model for prediction

Estimating the sale prices of houses is one of the basic projects to have on your Data Science CV. By finishing this article, you will be able to predict continuous variables using various types of linear regression algorithm.

**Why linear regression? Linear regression is an algorithm used to predict values that are continuous in nature. It became more popular because it is the best algorithm to start with if you are a newbie to ML.**

To predict the sale prices we are going to use the following linear regression algorithms: Ordinal Least Square (OLS) algorithm, Ridge regression algorithm, Lasso regression algorithm, Bayesian regression algorithm, and lastly Elastic Net regression algorithm. These algorithms can be feasibly implemented in python with the use of the scikit-learn package.

Finally, we conclude which model is best suitable for the given case by evaluating each of them using the evaluation metrics provided by the scikit-learn package.

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.