🦾💪 Machine Learning & Experimental

Description: ML applications, pedagogical walkthroughs, and speculative design.

• ClimateWins: Part I (Intro to ML) 🌦️🤖
  (June 2025)
  Comparison of supervised learning models for predicting weather conditions (Linear Regression, Artificial Neural Network, K-Nearest Neighbors, Decision Tree)

• ClimateWins: Part II (Modeling Experiments: Predicting Rain and Climate Drift) ☔️📉
  (August 2025)
  Random forests with hyperparameter optimization and time-aware train/test split, emphasizing interpretability and strategic feature engineering