08 Finalised Summary Notebook¶

This final notebook provides a high-level summary of the full pipeline developed across the ReCoDE exemplar. It is designed to help learners and reviewers grasp the structure, logic, and educational intent of the project.

All core methods are revisited here, with emphasis on how each contributes to behavioural anomaly detection under data-scarce conditions.

Notebook Overview (Pipeline Summary)¶

Notebook	Title	Purpose
01	Dataset Preparation	Introduces the cleaned behavioural dataset and prepares it for modelling
02	Preprocessing and Baseline IForest	Applies preprocessing steps and establishes a baseline using Isolation Forest
03	Dimensionality and Clustering	Reduces feature space using PCA and applies HDBSCAN for behavioural segmentation
04	Model Interpretation and Explanation	Interprets anomaly outputs and visualises key patterns
05	Ethical Reflection and False Positives	Highlights key ethical risks, including false positives and model opacity
06	Visual Polishing and Citations	Refines visual presentation and links methods to academic sources
07	Reproducibility and Environment Testing	Reruns key components under deterministic conditions to verify reproducibility

Final Pipeline Flow¶

Raw Behavioural Data  
   → Preprocessing and Preparation  
       → PCA for Dimensionality Reduction  
           → HDBSCAN for Behavioural Segmentation  
               → Isolation Forest for Anomaly Detection  
                   → Visual Review and Ethical Reflection  
                       → Reproducibility Validation

Key Methodological Choices¶

• Unsupervised Learning: Isolation Forest, HDBSCAN
• Dimensionality Reduction: PCA
• Cluster Validation: Behaviour-based segmentation
• Ethics & Fairness: Scenario-based discussion of false positives
• Reproducibility: Fixed seeds, controlled library versions

ReCoDE Learning Goals Addressed¶

• Working with incomplete behavioural data

• Applying unsupervised techniques to detect behavioural
  irregularities in ambiguous or label-scarce contexts

• Using interpretable models under ethical constraints

• Ensuring code reproducibility and transparency

Next Steps and Encouragement¶

Learners are invited to experiment with:

• Different clustering settings (e.g., minimum cluster size)
• Anomaly detection thresholds
• Feature inclusion/exclusion
• Ethical questions in applied settings

For further details, see the main README or revisit the annotated notebooks.