Leveraging AI-Powered Business Intelligence for Data-Driven Decision Making in Retail Industry

Artificial Intelligence (AI) combined with Business Intelligence (BI) has been of much benefit to the retail sector because it enables companies to understand and monetize the huge amounts of data collected about customers. The present paper presents an AI-powered Business Intelligence system of retail sales prediction using ML technology. In order to maximize the quality of the data and the model's outcomes, the study makes use of a publicly accessible Kaggle Retail Sales dataset and a variety of data pretreatment techniques, such as missing values, normalization, and feature selection. An 80:20 ratio is used to separate the dataset into training and testing to guarantee effective evaluation. The LightGBM is one of the models considered because it has superior performance among the models as it has an efficient learning mechanism and scalability. This model has a high value of R2 and lower error values of MAE, RMSE, and MSE, which means that it has a strong predictive capacity. A comparison with other models, including XGBoost, Support Vector Machine, and Ridge Regressor, further validates the efficacy of the suggested method. The results show how AI-based analytics may help the retail business make better strategic decisions and anticipate demand.