• Developed machine learning models for crop yield forecasting, integrating spatial-temporal data
• Conducted suitability analysis for agricultural land use planning, integrating remote sensing data
• Processed large-scale climate and vegetation datasets to assess rainfall and temperature trends
• Managed and cleaned survey datasets related to crop yields and farmer demographics
• Developed interactive GIS maps and geospatial databases to support research projects
• Applied R and Python for spatial statistics and predictive analytics
Master of Science in Geospatial Technologies - 09/2022 - 03/2024
Final grade: 17/20 B | Thesis: Integrating spatial heterogeneity to enhance spatial temporal crop yield predictions
Bachelor of Science in Geoinformatics - 10/2016 - 09/2020
Final grade: GPA 3.7/5 | Thesis: Rainfall and temperature trends for suitable adaption strategies in Agriculture a case of Morogoro
GIS & Remote Sensing: ArcGIS, QGIS, ERDAS, ENVI, Google Earth Engine, GRASS GIS
Programming & Analytics: Python (Pandas, NumPy, Geopandas, Scikit-learn), R (tidyverse, sf, terra), JavaScript, SQL, PostgreSQL
Machine Learning: Spatial modeling, time series forecasting, classification models, regression analysis
Data Visualization: Tableau, ggplot2, Matplotlib, Seaborn
Muthoni, F. K., Msangi, F. M., & Kigosi, E. (2023). Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa. Atmósfera, 37, 481-500.
Please feel free to contact me at my email address or through my LinkedIn Account below.
francismich196@gmail.com