AI-Based sales forecasting using a hybrid ANN and XGBOOST model

The development and assessment of the AI-Based Sales Forecasting System show the potential and effectiveness of leveraging machine learning, deep learning, and reinforcement learning techniques in modern businesses' forecasting and pricing procedures. The efficient integration of structured data preprocessing workflows, competing forecasting techniques, dynamic pricing, and an interactive web-based dashboard provides an efficient and comprehensive analytics platform for supporting pricing decisions in modern businesses. Ingestion of sales data by the developed platform and automatic generation of accurate demand forecasts and prices proves the practicality of implementing artificial intelligence technologies for decision making within commercial organizations.   High correlation between predictions provided by the algorithmically generated models and actual sales figures, especially among the hybrid models, demonstrates the efficiency of using advanced algorithms in extracting the necessary temporal information out of retail sales data. Also, the successful increase in revenue produced by the reinforcement learning-based dynamic pricing strategy proves the great commercial value of using advanced pricing techniques. Feature importance analysis and multi-model view make the advanced intelligence available even to non-technical users, who need explanations behind certain decisions made by the models. Additionally to the effective algorithmic forecasting pipeline, the created tool incorporates efficient revenue analysis dashboards and session monitoring features.

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