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.