samirsaci/ml-forecast-features-eng
Machine Learning for Retail Sales Forecasting — Features Engineering
Quantifies the impact of domain-specific features—stock-outs, store closures, and product cannibalization—on forecast accuracy using the M5 Walmart dataset. Implements feature engineering workflows that combine business domain knowledge with ML models to capture demand drivers beyond historical patterns, demonstrating 20-60% error reduction over statistical baselines. Built as Jupyter notebooks with extensible feature construction pipelines targeting retail demand forecasting scenarios.
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Jupyter Notebook
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Last pushed
Dec 30, 2025
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