Machine Learning

In this project, we experimented with ARIMA, C-LSTM and CNN to perform anomaly detection on web traffic data. Experiments demonstrate that ARIMA performs differently on different types of anomalies as ARIMA focuses only on local data rather than a full picture. On the other hand, C-LSTM outperforms CNN, reaching a recall rate of 79.1%.

Trained alternating least squares (ALS) model via PySpark on the Million Song Dataset to get latent representation for users and songs. Used PCA to reduce dimension and visualize music genres.

Bilibili is the most popular creator-driven content video play platform in China. To help vloggers better understand drivers of popular videos, we predicted the number of views for new videos based on video titles and labels, vlogger’s and previous video’s information using models include Ridge, Random Forest, XGBoost. We found features like video length, video posting frequency and time also play an important role in video popularity except previous video plays.

Suggested new store locations using K-means and DBSCAN clustering with parsed geolocation data including public transport locations, E-commerce delivery addresses, competitors' store locations.