InvenTrack: A Hybrid Ensemble Learning Framework for Sales Forecasting with Social Media Signal Integration

Accurate sales forecasting is essential for effective inventory management and business growth. However, many organizations still rely only on historical sales data and manual estimation methods, which fail to consider external market trends. This often results in stock shortages or excess inventory. To address this issue, this project proposes InvenTrack, an intelligent sales forecasting system that combines historical data with machine learning techniques. Phase 1: Sales Data Processing and Feature Engineering In the first phase, the system collects and preprocesses historical sales data. Time- series feature engineering techniques such as lag values, rolling averages, seasonal indicators, and trend analysis are applied to extract meaningful patterns from past sales records. This structured dataset forms the foundation for accurate forecasting. Phase 2: Ensemble-Based Demand Forecasting In the second phase, multiple machine learning models including Linear Regression, Random Forest, ARIMA, and LSTM are trained to forecast future demand. Each model captures different characteristics such as linear trends, seasonality, non-linear relationships, and long-term dependencies. An ensemble learning approach combines these models to produce a more reliable and stable forecast compared to standalone methods. Implementation and Usage: The system is developed using Python, with FastAPI for backend services and React for visualization. Machine learning models are implemented using TensorFlow and Scikit-learn. Users can upload sales data and view forecast results through an interactive dashboard. SQLite is used to store processed data and prediction results. By integrating ensemble learning with intelligent forecasting techniques, InvenTrack provides a reliable and scalable solution for demand prediction and inventory optimization.