Comparative Study of Machine Learning and Artificial Intelligence Algorithms in IoT Intrusion Detection: Securing Personal Health Data in IoT-Enabled Devices

I'm seeking an in-depth comparative study on machine learning and AI algorithms for IoT intrusion detection, focusing on securing personal health data. This project will involve coding and generating results.

Key requirements:
- Hybrid algorithms for intrusion detection.
- Comparison of the following supervised learning algorithms:
- Decision Trees
- Support Vector Machines
- Random Forests
- KNN
- Neural Networks
- Convolutional Neural Networks
- Performance metrics to include:
- Confusion Matrix
- ROC Curve
- Precision
- Recall
- F1 Score
- Explainable AI (using SHAP and LIME)

- Comparison of the following unsupervised learning algorithms:
- K-means Clustering
- Principal Component Analysis
- DBSCAN

Ideal skills and experience:
- Strong background in machine learning and AI.
- Proficiency in Python and relevant libraries (e.g., Scikit-learn, TensorFlow, Keras).
- Experience with IoT security and intrusion detection systems.
- Knowledge of explainable AI techniques.

Please provide code and results for the above comparisons.