A Deep Learning Approach for the Sales Prediction in Retail Stores: An End-to-End Analysis and Implementation

In the world of rapidly mounting businesses, the focus of business organizations is to gain profit and retain their customers committed to them consistently. In addition, the globalization effects have witnessed a humongous surge in competition among companies. One of the prominent factors that act as a driving force for companies to sustain among competitors is the forecasting of sales based on products and promotions available that help in the effective handling of revenue and inventory. Moreover, it also warrants a minimal loss. Accurate prediction of sales is possible only through the establishment of a strong bond with customers and by an apposite understanding of the market requirement. This study involves the prediction of sales data wherein, initially, the performances of existing linear models are compared with that of the deep learning models based on the available data, which includes regression models, recurrent neural network (RNN), long short-term memory (LSTM), and so on. This work also suggests an improved model that provides more optimized results on the data used than all other compared models.