Abstract: In today’s competitive retail environment, accurate demand forecasting is essential for effective inventory management and customer satisfaction. This research explores a machine learning approach for retail demand forecasting that not only uses historical sales data but also integrates social media trends and local event data to capture real world demand influencers. Unlike many existing models that focus solely on past sales patterns, this approach leverages big data sources to refine predictions and address fluctuating consumer preferences. The proposed model employs algorithm such as Random Forest and XGBoost to analyze a broad array of data and enhance forecasting accuracy. A key feature of this system is its ability to automatically adjust forecasts based on real-time social sentiment, allowing retailers to respond dynamically to shifts in customer interest, such as sudden demand spikes for trending products. The forecasting results are visualized through an interactive React-based dashboard, enabling retailers to quickly access demand insights. With a cloud-based backend for data processing and storage, this solution ensures scalability and timely data handling, helping businesses make data-driven inventory and supply chain decisions.
Keywords: Demand forecasting, retail, machine learning, big data, real-time analytics, social media trends, React, Random Forest, XGBoost.
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DOI:
10.17148/IJARCCE.2025.14235
