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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.

The project is the final project for NYU DS-GA 1001 Intro to Data Science

Data

Data comes from kaggle MovieLens 100K Dataset.
Also uploaded, simply download everything and run the Jupyter Notebooks.

Code

Source code
Packeges used: pandas, numpy, surprise, scikit-learn, etc.

  • data-preprocessing: some data cleaning and data visualization.

  • baseling-model: implemented several baseline models: only mean of the ratings, weighed mean, SVM, gradient boosting, etc.

  • surprise: implemented several recommender system models from packege surprise: SVD, SCD++, NMF, kNN, and performed stacking.

  • evaluation: tested model accuracy, coverage, diversity through several metrics.

Authors

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NYC Rental Listing Popularity Study (Fall 2018)

This project focus on designing a more reasonable listing mechanism that can predict the interest 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.

The project is the final project for NYU DS-GA 1007 Programming for Data Science

Data

Data comes from kaggle Two Sigma Connect: Rental Listing Inquiries.
Also uploaded, simply download everything and run the Jupyter Notebooks.

Code

Source Code
Packeges used: pandas, numpy, scikit-learn, etc.

All the code is in the 07 project Jupyter Notebook, sections are Data Importing, Data Cleaning, Model Selection and Implement, and some Data VIsualization in the end.

Authors