References

Agonafir, C., Lakhankar, T., Khanbilvardi, R., Krakauer, N., Radell, D., & Devineni, N. (2022). A machine learning approach to evaluate the spatial variability of New York City’s 311 street flooding complaints. Computers, Environment and Urban Systems, 97, 101854.
Agonafir, C., Pabon, A. R., Lakhankar, T., Khanbilvardi, R., & Devineni, N. (2022). Understanding New York City street flooding through 311 complaints. Journal of Hydrology, 605, 127300.
(ASA), A. S. A. (2018). Ethical guidelines for statistical practice.
Bansal, R. (2024). SQL using python.
Breiman, L., Friedman, J. H., Olshen, R., & Stone, C. J. (1984). Classification and regression trees. Wadsworth.
Cafferky, B. (2019). Master using SQL with python: Lesson 1 - using SQL with pandas.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785
Computing Machinery (ACM), A. for. (2018). Code of ethics and professional conduct.
Cone, M. (2025). Markdown cheat sheet | markdown guide. https://www.markdownguide.org/cheat-sheet/
Congress, U. S. (1990). Americans with disabilities act of 1990 (ADA).
Dervieux, C. (2025). Markdown-basics. https://quarto.org/docs/authoring/markdown-basics.html
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232.
Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer.
Health, U. S. D. of, & Services, H. (1996). Health insurance portability and accountability act of 1996 (HIPAA).
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 3146–3154.
MacFarlane, J. (2006). Pandoc user’s guide. https://pandoc.org/MANUAL.html#pandocs-markdown
MacFarlane, J. (2019). GitHub flavored markdown spec. https://github.github.com/gfm/
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 6638–6648.
Protection of Human Subjects of Biomedical, N. C. for the, & Research, B. (1979). The belmont report: Ethical principles and guidelines for the protection of human subjects of research.
Przybyla, M. (2024). How to use SQL in python.
Team, F. D. S. D. (2019). Federal data strategy 2020 action plan.
Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc.
W3Schools. (2025). Python MySQL.