In this "getting started" competition, you'll use time-series forecasting to forecast store sales on data from Corporación Favorita, a large Ecuadorian-based grocery retailer. Specifically, you'll build a model that more accurately predicts the unit sales for thousands of items sold at different Favorita stores. You'll practice your machine learning skills with an approachable training dataset of dates, store, and item information, promotions, and unit sales.
Forecasts are especially relevant to brick-and-mortar grocery stores, which must dance delicately with how much inventory to buy. Predict a little over, and grocers are stuck with overstocked, perishable goods. Guess a little under, and popular items quickly sell out, leading to lost revenue and upset customers. More accurate forecasting, thanks to machine learning, could help ensure retailers please customers by having just enough of the right products at the right time.
If successful, you'll have flexed some new skills in a real world example. For grocery stores, more accurate forecasting can decrease food waste related to overstocking and improve customer satisfaction. The results of this ongoing competition, over time, might even ensure your local store has exactly what you need the next time you shop.
Evaluation Metric The evaluation metric for this competition is Root Mean Squared Logarithmic Error (RMSLE).
Submission File For each id in the test set, you must predict a value for the sales variable. The file should contain a header and have the following format:
id,sales
3000888,0.0
3000889,0.0
...