Time Series Forecasting-Store Sales 

Welcome to my Machine Learning project to predict the sales for stores of a grocery retailer. These datasets have a lot of useful and actual information for a specific case of Time Series Forecasting.

I used time-series forecasting to forecast store sales on data from Corporación Favorita, a large Ecuadorian-based grocery retailer. The model accurately predicts the unit sales for thousands of items sold at different Favorita stores.

Besides, the analysis considers some external impacts:

• Holidays and Events.
• Dates to pay wages in the public sector.
• Major crisis (A magnitude 7.8 earthquake struck Ecuador on April 16, 2016).
• Daily oil price (Ecuador is an oil-dependent country).

Benefits to having a predictive model:

• 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.
• Current subjective forecasting methods for retail have little data to back them up and are unlikely to be automated. The problem becomes even more complex as retailers add new locations with unique needs, new products, ever-transitioning seasonal tastes, and unpredictable product marketing.
• 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.

Let’s start with the Introduction.

All files on GitHub: https://github.com/linhhlp/Store-Sales-Time-Series-Forecasting-Kaggle