Blog post: https://blog.nycdatascience.com/student-works/machine-learning/zillow-prize-competing-improve-zestimate/
Welcome to the repository for Team Zillow Pillow of NYC Data Science Academy! We participated in the Zillow Kaggle competition in August 2017, with the goal of improving Zillow's Zestimate home pricing algorithm by using machine learning techniques to predict error between Zestimates and actual sale prices in the greater Los Angeles area.
Kaggle link: https://www.kaggle.com/c/zillow-prize-1
You can find a deck summarizing our project pipeline, including data manipulation and imputation techniques, visualizations, machine learning models, findings, and areas for further improvement in the file "ZillowPillow Deck_Final.pdf".
iPython notebooks and R files used in data preprocessing visualization, and modeling can be found in the appropriate folder.
The dataset of untagged properties (no sale price provided) used to score the Kaggle competition was too large for Github. Please see the kaggle site if you would like to download it.