Forecasting California Housing Prices Using a Linear Regression Model

Authors

Keywords:

Machine Learning, Regression

Abstract

With the prevalence of big data, we are able to implement simple Machine Learning techniques against datasets to solve many present issues. Utilizing the California Housing Prices dataset, we can apply linear regression models to forecast districts’ future median housing prices. As a result, homebuyers could predict if they are getting a good deal on their home, investors could predict their potential return on investments and capitalize on undervalued properties, and sellers could gauge what they could potentially sell for on the open market. To find a starting base, our research began as an independent study guided by a textbook that provides hands-on machine learning training, including one that uses the California Housing Prices dataset. From here, we are able to choose the Least Square Regression Line as our performance measure in the model—which will result in a linear equation that looks for minimizing the variance between the training set’s predictions and the actual points produced on the regression line. Once we are able to train our model to make accurate predictions from the testing set, we can expand upon this research by moving beyond linear regression and investigating different Machine Learning techniques further within the textbook to model the data in more sophisticated ways.

Author Biographies

Luisely Doza, Shepherd University

Undergraduate student, Shepherd University

Jason Rafe Miller, Shepherd University

Assistant Professor of Computer Science

Department of Computer Science, Mathematics, and Engineering

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Published

2020-04-29

How to Cite

Doza, L., & Miller, J. R. (2020). Forecasting California Housing Prices Using a Linear Regression Model. Proceedings of the West Virginia Academy of Science, 92(1). Retrieved from https://pwvas.org/index.php/pwvas/article/view/737

Issue

Section

Meeting Abstracts-Poster