The purpose of this project is to deal with a real world forecasting problem and approach it with a business concern: from explaining the insights we can get just from the data, find a ML model that would fit on the forecasting problem and finally put into production the model and the insights we can get from it.

For this project I've chosen this Kaggle dataset. This dataset contains information about the sales of multiple stores of the company Favorita, in Ecuador. The goal of this project is to find a model that able to predict the estimation of sales for each family group of products and stores. Predicting the sales of an element (in this case a product) gives to any business and important and powerful information to take advantage on. In the case of the stores, one of these huge advantages could be able to estimate future revenue, allocate resources more effectively, etc. Here for more information.

So, in the following sections we will put ourselves in the shoes of a real store company, which want to understand which useful insights we can get from their historical data, train a model able to predict the future amount of sales and take advantage of this to increase the turnover.

Final results are presented in a user friendly report:

The results of the best model found in this project have been submitted to the Kaggle competition and currently the rating of the results is among the top 26% (190th position out of 726):

Click here to properly visualize the internal links of the Jupiter notebook.