Author: Tim Rappold
GitHub: timrappold
Email: tim.rappold@gmail.com
https://github.com/timrappold/Luther
This is a webscraping and linear regression project. The goal is to describe and quantify how home prices change based on zip code and features. What's the difference between a 2-bed, 1-bath and a 3-bed, 2-bath home in 94605? What about in 90056?
This project develops the code to scrape home sales data from Redfin.com, focusing on sales within the last three years in Oakland and Los Angeles. The Python module redfin.py contains all code related to scraping, using both the bs4 and selenium webscraping libraries.
In the module homepricemodels, the project develops two linear regression models using statsmodels.OLS and scikit-learn.Lasso to take two quite different approaches to multivariate regression and model selection.
The main analysis is located in homepricemodels_client.ipynb. There, the project concludes with an analysis of the above scenario (home prices in Eastmont Hills, zip code 94605 and in Ladeira Hts, zip code 90056) using the model developed with Lasso regression and a home-brewed Backward Stepwise model selection technique.