An Ensemble-Based Sales Forecasting System Integrating Machine Learning and Real-Time Data Analytics for Enhanced Strategic Decision-Making

F. O. Okorodudu, C. O. Ogeh and G. C. Omede
Additional contact information
F. O. Okorodudu: Faculty of Sciences, Department of Computer Science, Delta State University, Abraka, Delta State, Nigeria.
C. O. Ogeh: Faculty of Sciences, Department of Computer Science, Delta State University, Abraka, Delta State, Nigeria.
G. C. Omede: Faculty of Sciences, Department of Computer Science, Delta State University, Abraka, Delta State, Nigeria.

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 6, 2380-2388

Abstract: In the fast-changing field of commercial analytics, precise sales forecasting is an essential component of making strategic decisions and making the most use of resources. This study presents a new Sales Forecasting System that combines advanced machine learning techniques in an ensemble framework to work together. The system utilizes Multiple Linear Regression (MLR) to identify linear relationships and a Decision Tree Classifier to detect complex, nonlinear patterns and categorical trends. The system was built using Python, as it offers numerous analytical libraries. It uses Flask as a backend framework, and HTML, CSS, and JavaScript make the user interface easy to use and interactive. The system’s ability to respond to changing market circumstances is even more effective when it is able to integrate other sources of real-time data, including market indicators and promotional efforts. The ensemble methodology outperforms individual models and standard forecasting methods because it mitigates the problems associated with relying on a single model. This strong and flexible all-in-one solution provides companies with exact, real-time sales projections. This increases both strategic planning flexibility and accuracy. Combining academic rigor with pragmatic relevance for data-driven markets helps the proposed paradigm enhance sales analytics.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... ssue-6/2380-2388.pdf (application/pdf)
https://rsisinternational.org/journals/ijriss/arti ... gic-decision-making/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:9:y:2025:issue-6:p:2380-2388

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().