Sales Forecasting Using Machine Learning Algorithm in the Retail Sector

Abstract

The ability to predict future sales is essential for modern firms. The already difficult work of sales forecasting is made much more difficult by a lack of data, missing data values, or outliers. Regression has a stronger relationship with sales forecasting complexity than time series does. Intricate patterns in the dynamics of sales that also involve a range of risk factors can be discovered using machine learning algorithms and supervised machine learning techniques. A company's sales projections need to be correct for it to succeed. By utilizing a reliable sales projection model, businesses may identify potential risks and make smarter decisions. In this study, Rossmann sales data will be analyzed using the Extreme Gradient Boosting (XG-Boost), FB-Prophet, and autoregressive integrated moving average (ARIMA) prediction models. A corporation can reduce costs associated with excess inventory, make future plans, and boost profitability with the use of an accurate sales forecast. Therefore, the model needs to be assessed using statistical techniques like R2, RMSE, and MAE. To determine if models are more accurate at predicting sales, the results are employed.