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Wendy Hou's Projects

ccm-site icon ccm-site

NYU PSYCH-GA 3405.002 / DS-GS 3001.006 : Computational cognitive modeling

covid-19-infection-rate-prediction icon covid-19-infection-rate-prediction

The goal of this project is to predict the infection rate of U.S. counties that are missing from Johns Hopkins' COVID-19 daily reports based on their county feature data, including but not limited to population, education, public and private healthcare information, and popular transportation methods.

end-to-end-slu icon end-to-end-slu

PyTorch code for end-to-end spoken language understanding (SLU) with ASR-based transfer learning

flexible-input-slu icon flexible-input-slu

This setup allows to train end-to-end neural models for spoken language understanding (SLU).

recsys2019_deeplearning_evaluation icon recsys2019_deeplearning_evaluation

This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

resume icon resume

This is Wangrui (Wendy) Hou's resume. Wendy is a Master's student in Data Science at New York University.

vae_cf icon vae_cf

Variational autoencoders for collaborative filtering

yelpvegas icon yelpvegas

The purpose of this data mining project is to examine how restaurants can improve their Yelp profile to become more “successful” on Yelp in Las Vegas, Nevada. Different from the traditional approaches to this dataset, our methodology defines “success” as a binary variable through an exploratory analysis of the restaurants’ review counts and ratings on Yelp. Feature variables include categories and attributes that Yelp users can use to select which restaurant to visit. For this project, we ran Decision Tree, Random Forest, and Logistic Regression to explore key features associated with “success” and obtain recommendations for restaurants to improve their Yelp profile. Final results indicate that determinants of success vary by cuisine type.

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