Retail — Retail Sales Forecasting Model Training Pipeline

Overview

The primary purpose of this DAG is to train predictive models that forecast retail sales, leveraging historical sales data and engineered features. The data sources include transaction logs, customer demographics, and seasonal trends, which are ingested into the pipeline for processing. The workflow begins with data extraction from these sources, followed by a series of transformation steps that include feature engineering and normalization. Model selection is then performed, utilizing various a

The primary purpose of this DAG is to train predictive models that forecast retail sales, leveraging historical sales data and engineered features. The data sources include transaction logs, customer demographics, and seasonal trends, which are ingested into the pipeline for processing. The workflow begins with data extraction from these sources, followed by a series of transformation steps that include feature engineering and normalization. Model selection is then performed, utilizing various algorithms to identify the best-performing model based on historical data. The models undergo rigorous evaluation through cross-validation to ensure robustness and minimize overfitting. Key performance indicators (KPIs) such as model accuracy and training time are monitored throughout the process to assess performance. The final outputs include the trained model artifacts and evaluation reports, which are stored for future reference and analysis. In case of any failures during the training process, the DAG is designed to automatically restart after sending an alert, ensuring continuous operation. This structured approach not only enhances the predictive capabilities of retail operations but also drives strategic decision-making by providing reliable sales forecasts.

Part of the Scientific ML & Discovery solution for the Retail industry.

Use cases

• Improved sales forecasting accuracy for better inventory management
• Enhanced decision-making capabilities based on data-driven insights
• Reduced operational risks through automated failure handling
• Increased efficiency in model training and evaluation processes
• Ability to adapt quickly to changing market conditions