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03ML Model Comparison2026
Freight Cost Prediction
Pulled invoice data, engineered one feature that mattered, then ran three models head-to-head to find what actually predicts freight costs.
Problem
Nobody could answer 'what should this shipment cost?' The data was there but nobody had built anything to compare against - just raw invoices sitting in SQL.
Approach
- Started with correlation analysis - quantity, freight cost, invoice value. Freight per unit showed the strongest signal.
- Ran Linear Regression, Decision Tree, and Random Forest side-by-side. Random Forest won on R².
Architecture
- Invoice SQL dump
- Pandas cleanup
- Feature: freight per unit
- Compare 3 models
- Pick best (Random Forest)
Specification
- Best model
- Random Forest
- R² score
- 0.87
- Key feature
- Freight per unit
- Output
- Predicted cost per shipment
Stack
- Python
- SQL
- Pandas
- Scikit-learn
- Seaborn
All case studies
Can share the notebook. Just ask.