samirsaci/ml-forecast-features-eng
Machine Learning for Retail Sales Forecasting — Features Engineering
This project helps retail analysts and supply chain managers improve the accuracy of their sales forecasts. It takes historical sales data and other business information (like promotions or stock-outs) and processes them to create better inputs for machine learning models. The result is more precise sales predictions, enabling better inventory planning and operational decisions.
Use this if you need to understand how factors like store closures, promotions, or product cannibalization influence your retail sales forecasts and want to integrate these insights to reduce forecasting errors.
Not ideal if you are looking for a simple, off-the-shelf statistical forecasting model without incorporating detailed business-specific events and features.
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Last pushed
Dec 30, 2025
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