saswat555/intercessor
A smart ware-house management system
Leverages ARIMA time-series forecasting via TensorFlow to predict upcoming sales demand, enabling automated restocking decisions that minimize perishable goods waste. Built on Flask with MongoDB for data persistence, the system ingests CSV inventory data and uses R-based statistical analysis to refine prediction variables before generating demand forecasts. Architecture supports customizable commodity configurations, allowing warehouse operators to adjust forecast parameters and ML model inputs per product category.
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Aug 30, 2021
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