Netflix Recommender System Project (Fall 2018)
The purpose of this project is to propose several viable models by introducing concepts, analyzing advantages and disadvantages, evaluating performance, and understanding the recommendation dynamics.
NYC Rental Listing Popularity Study (Fall 2018)
This project focus on designing a more reasonable listing mechanism that can predict the inter-
est level (high, medium, low) of rental apartments based on their numerical and descriptive information. We approached it using logistic regression, random forest, and kNN. The results show that random forest has the best performance in predicting the popularity of rental apartments.