Corporación Favorita Grocery Sales Forecasting

Description of Problem

This notebook attempts to solve for a Kaggle problem given here: https://www.kaggle.com/competitions/favorita-grocery-sales-forecasting/. We are trying to solve for a delicate situation that brick and motar grocery stores face with overstocking goods and perishable goods. Items that are popular are quickly sold out while the other goods are left overstocked. As the retailer add more variables like locations, new product demands or seasonal products, to the problem, the issue becomes more complex. This data is derived from a large Ecuadorian-based grocery retailer, Corporación Favorita, they operate on a large scale with hundreds of locations and with more than 200,000 different products on their shelves. The question that we are trying to answer here is how to predict an accurate sales forcast. For this we will be using various machine learning models to do a predictive analysis. Python libraries like sklearn come in handy for this operation.

Import Python Libraries

Start with importing the libraries