Predictive Analytics for Big Mart Sales Using XGBoost

Machine Learning

Big Mart Sales prediction is an essential aspect of the retail industry, enabling businesses to optimize inventory management, devise effective marketing strategies, and make informed decisions. This study proposes a sales prediction system for Big Mart that utilizes the Extreme Gradient Boosting (XGBoost) algorithm, a powerful ensemble learning technique. By leveraging historical sales data, product attributes, customer information, and external factors, the system aims to accurately forecast future sales volumes. The implementation process involves various stages, including data preprocessing, feature selection, train-test splitting, XGBoost model training, and performance evaluation. The historical sales data is carefully processed to ensure data integrity, and relevant features are engineered to capture significant patterns and trends. Feature selection techniques are applied to identify the most informative attributes that contribute to accurate predictions. The XGBoost algorithm is then employed to construct a predictive model by iteratively combining weak decision tree models to form a robust ensemble. The model is trained using the historical sales data, learning from the errors of previous iterations to continually enhance prediction accuracy. Hyperparameter tuning is conducted to optimize the model’s performance. To evaluate the system’s effectiveness, a separate testing set is utilized to assess the accuracy of the sales predictions. Common evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) are employed to measure the model’s performance against the actual sales values. The accuracy of the XGBoost algorithm is 91.14 % And the accuracy of Big Mart sales prediction is 61.14 %

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