Smart Retail Inventory Prediction using Random Forest Ensemble Learning"

The contemporary retail landscape is undergoing a significant paradigm shift, transitioning from traditional, intuition-based inventory management to sophisticated, data-driven predictive architectures. This research addresses the critical challenge of inventory optimization within Small and Medium Enterprises (SMEs), with a specific focus on the burgeoning regional markets of Chhattisgarh, India. Small-scale retailers often grapple with the "Inventory Paradox"—the dual financial risk of overstocking, which leads to capital stagnation and product obsolescence, and under-stocking (stock-outs), which results in immediate revenue loss and long-term erosion of customer loyalty. This study proposes and implements a Smart Retail Inventory Predictor utilizing the Random Forest Ensemble Learning algorithm. Unlike traditional linear statistical models such as ARIMA, which struggle with the volatile and non-stationary nature of retail sales, the Random Forest approach leverages the power of multiple decision trees to capture complex, non-linear patterns in consumer behavior. The methodology involves a rigorous four-stage pipeline: data acquisition of over 50,000 historical transaction records, advanced pre-processing to eliminate noise and outliers, a specialized feature engineering phase that incorporates cyclical time-encoding (Sine/Cosine transformations) and lag-based variables, and finally, hyper-parameter optimization using Grid Search Cross-Validation. The experimental results demonstrate a high degree of predictive reliability, with the model achieving an R-Squared (R^2) score of 0.914 and a significantly minimized Mean Absolute Error (MAE) of 2.1 units. These metrics indicate that the system can successfully forecast demand with over 90% accuracy across diverse product categories, including high-value electronics and seasonal appliances. Furthermore, the integration of a Streamlit-based visualization dashboard allows non-technical stakeholders to interpret complex AI outputs through intuitive graphical interfaces. The findings of this research underscore the democratization of Artificial Intelligence, proving that robust, high-accuracy forecasting tools can be deployed in localized SME environments without the need for high-end computational infrastructure. By implementing this system, retailers can expect a projected reduction in capital tie-up by approximately 22% and a marked improvement in supply chain resilience. This thesis serves as a comprehensive roadmap for digital transformation in regional retail sectors, providing both a theoretical framework and a practical implementation guide for future-ready inventory management.