DSkapinakis/sales-time-series-forecasting-ml-models
Sales Time Series Forecasting using Machine Learning Techniques (Random Forest, XGBoost, Stacked Ensemble Regressor)
This project helps businesses predict future sales for individual stores using historical sales data and store information. It takes raw daily sales records and store details as input, then generates precise 6-week sales forecasts. The insights are valuable for sales managers, retail strategists, and operations planners in drug stores or similar retail environments.
No commits in the last 6 months.
Use this if you need accurate, store-specific daily sales forecasts to inform inventory, staffing, or marketing decisions for multiple retail locations.
Not ideal if you only need high-level, aggregate sales predictions or if your business is not in retail with similar historical transaction data.
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Jupyter Notebook
License
MIT
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Last pushed
Jan 10, 2024
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