Predict bike demand and avoid overfitting

Q: Predict bike demand and avoid overfitting

This question evaluates time-series forecasting, feature engineering, awareness of data leakage risks, model evaluation choices, and overfitting prevention competencies within the Machine Learning domain applied to demand prediction.

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Question

You are given historical data for a city bike-sharing system. Available fields include station_id, hourly timestamp, number of bike pickups and returns, dock capacity, current bikes available at prediction time, weather, holidays, and nearby transit or event signals.

Design a model to predict the number of bike pickups from a specific dock during the next hour.

Discuss:

how you would define the target and avoid data leakage;
what features you would engineer from temporal patterns, station behavior, weather, and geography;
what train/validation/test strategy you would use for this time-dependent problem;
which evaluation metric(s) you would choose (for example, MAE, RMSE, Poisson deviance, or a downstream empty/full-dock metric) and the trade-offs;
how you would detect and prevent overfitting.

Predict bike demand and avoid overfitting

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