waypoint 02home / Projects / IoT Anomaly Detection
- status
- Completed
- year
- 2026
- category
- Applied AI
IoT Anomaly Detection
An applied AI project for detecting unusual behavior in IoT data
Overview
IoT Anomaly Detection is an applied AI project focused on identifying unusual behavior in Internet of Things data. The project is centered on Python-based model work, where IoT-related data is used to explore how abnormal patterns can be detected and separated from regular behavior. Rather than presenting itself as a complete monitoring product, it represents the model and experimentation side of anomaly detection, with attention to data preparation, training workflow, and the practical role of intelligent detection in connected environments. In my portfolio, this project connects machine learning with IoT security and reliability, showing how AI methods can support early detection of unexpected or suspicious behavior in device-based systems.
Tags
Highlights
- Applies machine learning ideas to anomaly detection in IoT-related data.
- Focuses on separating unusual patterns from normal device behavior.
- Connects AI modeling with IoT security, reliability, and early detection.