Junior Machine Learning Engineer
- Identifying Potential Areas for Urban Agriculture in Milan, Italy | Local Chapter Project
- Implemented exploratory image analysis techniques like RGB Histogram analysis, Blurriness Detection, Total Energy, and Median Absolute Deviation using OpenCV and PIL to extract key features from Apple Leaf images.
- Analyzed tabular crop data using statistical techniques such as univariate, bivariate, and variance inflation factors to identify key features for optimal crop selection.
- Utilized Python scripts to automate the webscraping process, extracting geospatial image data from Sentinel 2 via Google Earth Engine (GEE) and QGIS, resulting in the identification of 50 potential urban farming areas.
- Led the coordinated collection of geospatial data from various online sources, resulting in a 25% increase in project efficiency and accuracy among a team of 100 global collaborators.
- Developed and deployed automated Python scripts to extract crucial data points for geospatial analysis, resulting in a 15% increase in efficiency in predicting optimal urban agricultural areas in Milan.
May 2024 - Jul 2024
