KaggleX Final Project Presentation with Isabela Yepes | Kaggle
About the project: Regression and classifier machine learning models are used to predict the next day’s rainfall. Neural network regression, random forest, and XGBoost classifiers are trained on South Florida 2015 data with 44 features including the current day’s total rainfall, temperature, wind, surface pressure, wind, vegetation levels, and more. Then models are tested on NYC and South Florida 2022 December data. Temperature, the current day’s rainfall, and surface pressure were the top three predictive features. XGBoost was the best overall model based on F1 score.
About Isabela Yepes: Isabela is an environmental engineer with a year of work experience. She has previous experience in statistical and data analysis including at the Environmental Protection Agency, and the Earth and Engineering Center of Columbia University. Her work at the latter was recently published in the Journal of Environmental Management. In addition to English, she knows Spanish and basic French. In the future, she seeks to pursue a masters in computer science to pursue innovative work with a positive impact. Outside of her career, she is a yoga instructor and loves nature.
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About KaggleX: KaggleX BIPOC mentorship program is designed to help increase representation, create career opportunities, and develop individual growth for BIPOC (Black, Indigenous, People of Color) people in the data science industry.
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