@adityand

Episode 17: Phase 6: Final Evaluation & Series Finale

Apr 27 · 5 min read · Act 1 — The Full Picture Five phases in, eight models trained, one question left: which model do you actually deploy, and can it explain its decisions? The full comparison table tells the first part o

Episode 16: Phase 5: LightGBM Didn't Win the Way I Expected. It Won Differently.

Apr 26 · 3 min read · Act 1 — The Setup Phase 4 ended with LR + SMOTE as the model to beat. Churn recall of 0.80. ROC-AUC of 0.839. A logistic regression — the simplest model in the toolkit — holding the top spot after fou

Episode 15 — Phase 4: Class Imbalance

Apr 26 · 3 min read · Act 1 — The Problem With Getting 81% Right Phase 3 ended with an uncomfortable truth. Logistic Regression scored 81% accuracy and an ROC-AUC of 0.842. On paper, that looks good. But it was missing 44%

Episode 14: Phase 3: Baseline Models

Apr 26 · 6 min read · Title: I Ran Three Baseline Models on Churn Data. The Simplest One Won. Act 1 — The Setup Phase 2 left me with a clean preprocessing pipeline. A ColumnTransformer that handles three feature types, a

Episode 13: Phase 2: The Preprocessing Pipeline

Apr 26 · 3 min read · Act 1 — The Problem With Raw Data Raw data is a mess. Strings where there should be numbers. Categories with three possible values mixed alongside categories with seven. Numerical features on complete