Predicting the temporal and geographical occurrence of conflicts in Myanmar with two paradigms of spatiotemporal networks.
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Updated
Dec 5, 2024 - Jupyter Notebook
Predicting the temporal and geographical occurrence of conflicts in Myanmar with two paradigms of spatiotemporal networks.
This project was conducted for "API 222: Machine Learning and Data Analytics", taught at the Harvard Kennedy School. We created a novel dataset and explored how machine learning can predict the onset of civil conflict.
Development of An Automated Conflict Prediction System by State Space ARIMA Methods
M.Sc. Thesis - Predicting Violent Conflict in Africa - Leveraging Open Geodata and Deep Learning for Spatio-Temporal Event Detection
Final class project for Modeling II: Machine Learning at USF; a graduate course in the Data Science program.
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