End-to-End Sales Forecasting Pipeline

Context

Datalysis Group needed an automated sales forecasting system that could deliver daily predictions to branch managers, helping them make informed inventory and staffing decisions. The challenge was building a reliable end-to-end pipeline — from raw transactional data all the way to actionable daily forecasts — following production-quality engineering practices.

Approach

Data Pipeline Design

Designed the full data pipeline following CRISP-DM principles:

• Data ingestion: Connected to raw transactional data sources in Snowflake
• Data cleaning: Built validation and cleaning steps to handle missing values, outliers, and schema inconsistencies
• Feature engineering: Created time-series features from transactional data to capture seasonality, trends, and branch-level patterns

Model Training & Evaluation

Built reproducible model training and evaluation workflows:

• Multiple forecasting approaches evaluated against held-out data
• Automated retraining on updated data with consistent evaluation metrics
• Clear documentation of model assumptions and performance characteristics

Production Pipeline

Orchestrated the full workflow using Mage AI, producing automated daily predictions that branch managers could use directly for operational decisions.

Reliability & Maintainability

Improved reliability with validation checks, schema conventions, and documentation so that models and pipelines are reproducible and easier to extend by future team members.

Technical Details

• Data warehouse: Snowflake
• Orchestration: Mage AI
• Language: Python, SQL
• Methodology: CRISP-DM
• Output: Automated daily sales predictions per branch

Outcome

Delivered a working end-to-end forecasting system that automated the path from raw transactional data to daily branch-level predictions, with validation checks and documentation to support ongoing maintenance and iteration.