The capstone is where theory meets practice. It's time to combine data cleaning, splitting, modeling, and evaluation into a single high-performance pipeline.
1Pipeline Architecture
A professional ML model is not a single script but a pipeline. It must handle data preprocessing (scaling, encoding), model instantiation, and validation consistently. This architecture ensures that your model is reproducible and ready for production.
2The Random Forest Standard
For our capstone, we use the Random Forest algorithm. It is one of the most versatile and robust classifiers available, handling both linear and non-linear patterns while being resistant to outliers and overfitting.
3Final Validation
Success is measured in the Test Set. By using a classification report, we verify that our model hasn't just memorized the training data. A high F1-score on unseen data is the ultimate proof of a successful predictive engine.
