Opaleke Oluwaseyi

I’m a data scientist with hands-on experience in machine learning, environmental data analysis, and Python-based geospatial processing. I enjoy building scalable pipelines and modeling systems for both real-time and large-scale data, especially where data quality directly impacts decisions.

My work centers on satellite data workflows, IoT-based monitoring, and computer vision projects. In my Air Quality IoT Monitoring System, I developed real-time sensor data pipelines using ESP modules and Python to collect and visualize air quality levels, then performed exploratory data analysis on trends affecting environmental health.

I also build practical computer vision solutions. For my YOLOv3 object detection and CCTV tracking project, I trained a model to detect human movement in live video feeds using YOLOv3 and OpenCV, and I explored how these capabilities could support environmental surveillance.

Through projects like coordinate detection with OpenCV + Python and automated multi-CSV air quality analysis across Korean cities, I strengthen the full workflow from data cleaning and EDA to reliable outputs. I’m motivated by mission-driven AI applications in climate resilience and natural disaster prevention, where geospatial context and timely signals matter.