Our work will be implemented at Rossmann Pharmaceuticals as a Machine Learning Engineer. The finance team wants to forecast sales in all their stores across several cities six weeks ahead of time. Managers in individual stores rely on their years of experience as well as their personal judgment to forecast sales. The data team identified factors such as promotions, competition, school and state holidays, seasonality, and locality as necessary for predicting the sales across the various stores.

LSTM methodology, while introduced in the late 90's, has only recently become a viable and powerful forecasting technique.In this article, we are going to use LSTM RNN on a Rossman Pharmaceutical time series dataset to predict sales on a real-world business problem taken from Kaggle. Problem Statement Rossmann operates over 3,000 drug stores in 7 European countries. Currently, Rossmann store managers are tasked with predicting their daily sales for up to six weeks in advance. Store sales are influenced by many factors, including promotions, competition, school and state holidays, seasonality, and locality. With thousands of individual managers predicting sales based on their unique circumstances, the accuracy of results can be quite varied.