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

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.