Big Mart Sales Prediction Using Random Forest

Abstract

The urgency of decision-making and growth for retail businesses hinges on sales predictions. Big Mart, a large retail chain, needs forecasting models for inventory control, product availability, and profitability. This project poses a machine learning approach to forecasting sales of different products in various Big Mart outlets under the Random Forest Regression algorithm. The consolidated dataset used for the study consisted of a blend of product-level and store-level features such as product type, item visibility, outlet size, location, and historical sales, among others. The data preprocessing pipeline encompassed missing value treatment, correct abnormal formats, and categorical encoding. The Random Forest algorithm was favored because the ensemble nature of the algorithm utilizes multiple trees to combine predictive strength with resisting overfitting tendencies. Hyperparameters were incorporated and validated to yield a model that was highly accurate and superior to a set of other traditional regression models. The final trained model was able to predict future sales with minimal error, thereby proving its robustness and ability to handle complex nonlinear relationships present in the data. Therefore, this prediction system will aid retailers in time-sensitive, data-driven decision-making, provide precise demand forecasts, and justify resource allocation. In summary, this means Big Mart reduces stock out, meets customer demand on time, and maximizes revenue.