### Neural Network Model Summary

**Architecture:**
- Input: 48 features
- Hidden layers: [256, 128, 64]
- Dropout rate: 0.2
- Total parameters: 54,657

**Training:**
- Optimizer: Adam (lr=0.001)
- Early stopping: 25 epochs patience
- Best epoch: 153

**Test Set Performance:**
- MAE: 1.893
- RMSE: 2.398
- R²: 0.646
- Accuracy within ±1 grade: 33.8%
- Accuracy within ±2 grades: 60.5%
- Exact grouped V-grade accuracy: 27.8%
- Accuracy within ±1 V-grade: 67.9%
- Accuracy within ±2 V-grades: 88.4%

**Key Findings:**
1. The neural network is competitive, but not clearly stronger than the best tree-based baseline.
2. Fine-grained score prediction remains harder than grouped grade prediction.
3. The grouped V-grade metrics show that the model captures broader difficulty bands more reliably than exact score labels.
4. This makes the neural network useful as a comparison model, and potentially valuable in an ensemble.

**Portfolio Interpretation:**
This deep learning notebook extends the classical modelling pipeline by testing whether a neural architecture can improve prediction quality on engineered climbing features.
The main result is not that deep learning wins outright, but that it provides a meaningful benchmark and helps clarify where model complexity does and does not add value.