Titanic - Getting 0.799 with Random Forests and Gradient Boosting

Details

The materials here build on Section 1-5 the Kaggle Berlin Introductory Tutorial, comprising parameter-tuned implementations of Random Forests and Gradient Boosting, as well as the ensemble of both models.

Tutorial

The tutorial provides a more detailed, step-by-step explanation:

• Section 1-0 - First Cut.ipynb
• Section 1-1 - Filling-in Missing Values.ipynb
• Section 1-2 - Creating Dummy Variables.ipynb
• Section 1-3 - Parameter Tuning.ipynb
• Section 1-4 - Building Pipelines.ipynb
• Section 1-5 - Final Checks.ipynb

In addition, further discussion is provided on cross-validation and visualisation:

• Appendix A - Cross-Validation.ipynb
• Appendix B - Visualisation.ipynb