Technical Depth
Applied data science and ML — written during my Master of Data Science at Boston University.
Feature Engineering Is the Job. Everything Else Is Execution.
Why I Chose CatBoost (And What It Taught Me About Generalization)
Why My Best Model Only Explains 40% of the Variance (And Why That's Fine)
The Feature That Explained Everything (And Why It Wasn't in Your Dataset)
LightGBM Overfitting: How One Parameter Caused a 0.114 AUC Gap
Stop Wasting Trials: A Progressive Hyperparameter Search Strategy with Optuna
Why I Switched from Regression to Classification (And Why It's Not That Simple)
The Feature Engineering Trick That Improved My Model More Than Any Algorithm
