Week 5 has been defined by a deep dive into data validation and a significant push to refine our user experience.

We reached a major milestone by manually labeling approximately 1,200 anomalous transactions, providing our ensemble machine learning model with the robust “ground truth” necessary for rigorous performance verification.

On the frontend, we’ve improved system interactivity by implementing a feature that allows users to navigate directly from a specific entry in the “Recent Anomalies” list to its precise location on the map. Additionally, we’ve made progress on our Feature Extractor by integrating time windowing, which will enhance our model’s temporal awareness.

Looking toward next week, we are shifting our focus to deploying the ensemble model to AWS, populating MongoDB Atlas with our newly labeled data, and brainstorming high-impact data visualizations for the UI.

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