deep learning notebook

data/05_predictive_modelling/model_summary.txt

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+
+### Model Performance Summary
+
+| Model | MAE | RMSE | R² | Within ±1 | Within ±2 | Exact V | Within ±1 V |
+|-------|-----|------|----|-----------|-----------|---------|-------------|
+| Linear Regression | 1.467 | 1.882 | 0.782 | 42.6% | 73.3% | 34.9% | 79.4% |
+| Ridge Regression | 1.467 | 1.882 | 0.782 | 42.6% | 73.3% | 34.9% | 79.4% |
+| Lasso Regression | 1.475 | 1.891 | 0.780 | 42.2% | 73.0% | 34.6% | 79.3% |
+| Random Forest (Tuned) | 1.325 | 1.718 | 0.818 | 47.0% | 77.7% | 38.6% | 83.0% |
+
+### Key Findings
+
+1. **Tree-based models remain strongest on this structured feature set.**
+   - Random Forest (Tuned) achieves the best overall balance of MAE, RMSE, and grouped V-grade performance.
+   - Linear models remain useful baselines but leave clear nonlinear signal unexplained.
+
+2. **Fine-grained difficulty prediction is meaningfully harder than grouped grade prediction.**
+   - On the held-out test set, the best model is within ±1 fine-grained difficulty score 47.0% of the time.
+   - The same model is within ±1 grouped V-grade 83.0% of the time.
+
+3. **This gap is expected and informative.**
+   - Small numeric errors often stay inside the same or adjacent V-grade buckets.
+   - The model captures broad difficulty bands more reliably than exact score distinctions.
+
+4. **The project’s main predictive takeaway is practical rather than perfect.**
+   - The models are not exact grade replicators.
+   - They are reasonably strong at placing climbs into the correct neighborhood of difficulty.
+
+### Portfolio Interpretation
+
+From a modelling perspective, this project shows:
+- feature engineering grounded in domain structure,
+- comparison of linear and nonlinear models,
+- honest evaluation on a held-out test set,
+- and the ability to translate raw regression performance into climbing-relevant grouped V-grade metrics.

data/06_deep_learning/neural_network_summary.txt

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+
+### Neural Network Model Summary
+
+**Architecture:**
+- Input: 119 features
+- Hidden layers: [256, 128, 64]
+- Dropout rate: 0.2
+- Total parameters: 72,833
+
+**Training:**
+- Optimizer: Adam (lr=0.001)
+- Early stopping: 25 epochs patience
+- Best epoch: 121
+
+**Test Set Performance:**
+- MAE: 1.270
+- RMSE: 1.643
+- R²: 0.834
+- Accuracy within ±1 grade: 49.0%
+- Accuracy within ±2 grades: 80.2%
+- Exact grouped V-grade accuracy: 39.2%
+- Accuracy within ±1 V-grade: 84.3%
+- Accuracy within ±2 V-grades: 96.8%
+
+**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.