Predictive Analysis for Retail: Sales Forecasting at Walmart

In retail, accurate profit and revenue forecasts are important for stock control and increased customer satisfaction. In this regard we examined regression algorithms such as Linear Regression, Ridge Regression, KNN Regression, Random Forest Regression, XGBoost Regression, and CatBoost. For each version, hyperparameter tuning was performed by grid seek and pass-validation. A survey metric with R-square rating has been used to evaluate the new general statistics. Feature need analysis is perfect for understanding profitability predictions. Ensemble methods such as the Voting Regressor showed changes in accuracy improvements as model strengths were combined. Our theory provides insights into instrument learning techniques for predicting retail revenue, providing concrete recommendations for specific performance outcomes.