asvin-kumar/bikedemandprediction

EE 381K - Applied Machine Learning Project

Predicting Demand for Bike-Sharing in Washington D.C.

EE 381K - Applied Machine Learning Project

Contributors: Fadeel Sher Khan, Powell Lowe, Lakshya Jagadish, Asvin Venkataramanan

Please see report for our experiments and results.

Code

1. EDA.ipynb has code for exploratory data analysis.
2. LinearReg_SVR_XGBoost_SHAP.ipynb has code for the Linear Regression, SVR, XGBoost, XGBoost (PCA) models described in the report. Code for SHAP plots is also included.
3. LSTM_SHAP.ipynb has code for the LSTM and LSTM (PCA) models described in the report. Code for SHAP plots is also included.
4. RandomForest.ipynb has code for the Random Forest and Random Forest (PCA) models described in the report.

Other Docs

1. Report.pdf has the ICML style report submitted for this project.
2. Slides.pdf and Slides.pptx are the slides the team presented in class.