Given bike-sharing data, how would you build a model to predict the rental price?
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You are given bike-sharing (rental) data and asked to build a model that predicts the rental price. Which variables do you think affect the price? How would you engineer features, handle missing data and outliers, and treat special periods such as COVID? Which model would you choose, for example linear regression or a random forest, and what are the assumptions, advantages and drawbacks of each? In a linear regression, how would you deal with heteroskedasticity, multicollinearity and seasonality? How would you split the data into training, validation and test sets, and which evaluation metric would you use (accuracy, MSE, MAE or ROC AUC)?
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