End-to-End Predictive Analytics Pipeline of Sales Forecasting in Python for Business Decision Support Systems

Abstract

In the modern world economy where competition becomes the dominant element, the importance of sales forecasting in business strategy development, inventory management, and resource cannot be overestimated. Appropriate sales forecasting is critical in improving inventory management, the forecast the demands and strategic planning in the retail business. This study aims to attain a higher level of accuracy in sales projections by examining data-driven methods using state-of-the-art machine learning (ML) techniques applied to the Walmart dataset. The methodology is followed as an extensive data preprocessing method such as processing missing data and the deletion of outliers towards the integrity of data. Changes that would be of significant importance, like data normalization, label encoding of categorical values, and feature engineering would be performed to improve the quality of model input. Because of this efficiency and good predictive power on structured data, the XGBoost algorithm is utilized. Model evaluation is carried out using the standard regression metrics, such as the coefficient of determination (R^2) and root mean square error (RMSE), with results of 0.946 and 21.77, respectively, to assess the correctness and reliability. It is seen that a comparative analysis against those traditional models showcases, how the proposed approach has a greater forecasting capability, which is a useful tool to be used in support of data-driven decisions within a retail setting.