2021 Data Science Competition: Post-COVID CA Property Price Prediction


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In this project we want to check how the property price market has different Property prices in California have increased significantly over the last 10 years and continue to rise in 2021. According to Gomez, a notable writer from Opendoor.com, the increase in property prices is mainly because of these factors: location of the house, home size and how wide the useable space is, condition of the house, the economy, and if the house is located near the market. In this study, we would like to take a closer look at which factors are mainly responsible for the increase in housing prices. We created 3 regression models: random forest regression, linear regression and gradient boosting regression and compared the results to determine which model has the best performance in predicting house prices. Our findings concluded that the gradient boosting regression has the best accuracy in predicting the prices.

Language: Python
Are you a contestant for RMDS 2021 Data Science Competition? Yes, I am a contestant from other
Collaborators: Sheryl Natasha Eda (seda@sfu.ca)
Type: Real Estate
Release Date: Jun 13, 2021
Last Updated: Jun 13, 2021

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