Abstract: Accurate sales forecasting is essential for retail businesses to optimize inventory, enhance customer satisfaction, and drive strategic decisions. This paper introduces a robust sales prediction system that integrates Long Short-Term Memory (LSTM) networks for time-series forecasting and XGBoost for predictive analytics to deliver reliable and precise sales predictions. Designed for modern retail environments, the system seamlessly integrates with Point-of Sale (POS) systems to enable real-time data ingestion and dynamic prediction capabilities. Users can also upload custom datasets and explore interactive modules for analyzing current sales trends and forecasting future demand. A React-powered dashboard offers intuitive data visualization, while a Flask-based backend ensures scalability and efficient processing. By combining cutting-edge machine learning models with real-time data handling and user-centric features, this solution empowers retailers to respond to market changes and gain a competitive advantage proactively.
Keywords: Retail, sales prediction, LSTM networks, XGBoost, real-time fore casting, POS integration, machine learning, interactive analytics, time-series prediction, data visualization.
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DOI:
10.17148/IJARCCE.2025.14427
