The 15-Day AI/ML Interview Bootcamp
Day 2 — Data, features, and the split you must never get wrong
Train, validation and test splits, data leakage, stratification, feature scaling, one-hot encoding, missing values, and the tiny Matrix and Dataset utilities the rest of this machine learning course is built on — with the data questions interviewers use to separate practitioners from readers.
The notebook said 97.5%. You showed the number in the review, everyone nodded, the model shipped on Friday. By Wednesday the dashboard read 82% — and 82% happens to be exactly what you’d score by predicting "no" for every customer. The model hadn’t changed. The model was never the problem. Somewhere in the fifty lines before the model, one value was computed on rows it should never have seen, and that one line quietly turned a coin-flip feature into your best predictor. Yesterday you built a model. Today you build the part of the job that actually decides whether the number you report is true:…
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