Sales Forecasting Using Machine Learning to Optimize Business Performance

DOI

10.2991/978-94-6239-693-7_110How to use a DOI?

Keywords

Sales Forecasting; Machine Learning; Decision Tree; Random Forest; Linear Regression; Business Analytics; Predictive Modeling; Data Preprocessing; Performance Evaluation; Python

Abstract

Sales forecasting is a key ingredient to adequate business planning, inventory control, and strategy. The ability to forecast the future sales properly assists the organizations in maximizing resources, reducing the costs of operations, and enhancing the performance. However, there are complicated tendencies that conventional forecasting programs might fail to explain in huge and dynamic information. In order to control these problems, the existing project will provide a sales forecasting tool developed using machine learning, which will utilize the sales history in the past to create accurate and reliable sales forecasts in the future. The proposed system is based on Python-written data analytics and focuses on transforming raw sales data into useful information by subjecting different machine learning models to systematic preprocessing and model testing, including Linear Regression, Decision Tree, and Random Forest and selecting the most efficient forecasting model. Routine performance measures are standard error measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results of the empirical findings indicate that the Decision Tree model better fits the selected data set. The solution will offer an effective and affordable solution that will scale and enhance accuracy of predictions, allow organizations to make data-driven decisions, and optimize inventory, marketing and financial planning.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nitheswar Malisetti
AU  - Penmetsa Sri Krishna Varma
AU  - Usma Abdur Rahman
PY  - 2026
DA  - 2026/06/16
TI  - Sales Forecasting Using Machine Learning to Optimize Business Performance
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 1149
EP  - 1155
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_110
DO  - 10.2991/978-94-6239-693-7_110
ID  - Malisetti2026
ER  -