A tree-ensemble regressor on messy, real-world pricing features — mostly an exercise in feature engineering, which is where the accuracy actually lives.
Flight prices depend on a tangle of dates, durations, stops, and carriers — none of which arrive in a form a model can use.
The raw fields are engineered into usable features — times decomposed, durations normalised, categoricals encoded — and a Random Forest regressor is fitted and evaluated over them.
That the win in tabular problems comes from the shape of the features, not the fanciness of the estimator. The same discipline is what makes extraction from intake notes and medical records hold up.
Losing hours to work an AI system could handle?
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