1. Environment Setup: Install PennyLane for quantum machine learning, along with scikit-learn, matplotlib, and seaborn.
2. Importing Libraries:
3. Data Loading: Mounting the Google Drive and loading the pavement dataset from Google Drive.
4. Basic Statistics:
5. EDA Visualizations:
6. Class Distribution:
7. Feature Selection:
8. Feature Matrix Setup:
9. Preprocessing and Scaling:
10: Training Subsets
11. Evaluation Metrics Helper: Defining a reusable helper function that computes the same standardized set of metrics (Accuracy, Precision, Recall, F1 Score, AUC-ROC, Balanced Accuracy, MSE, training time, inference time, parameter count) for every model.
12: Classical GNB (Full Data)
13: Classical GNB (Reduced Data)
14: Quantum Circuit Components
15: Hybrid Quantum Kernel SVM
16: Quantum VQC
17: VQC Convergence Plot
18: Model Comparison DataFrame
19: Accuracy Comparison Bar Chart
20: Scalability Analysis