A non-conventional model to explain and forecast prices and trade flows of food staples across developing nations in Southeast Asia

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The purpose of this research is to develop an unconventional model to understand and explain the trade flows of homogenous and perishable food products across geographically distinct markets. When working with rice price data from developing countries, I used web mining methods to obtain as many sources of data as possible, and performed data cleaning and visualization of the data. I then employed unsupervised machine learning techniques such as Prrincipal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to better understand the co-movements of food prices across groups of trading partners. These data-compressing techniques allowed me to identify groups of trading markets whose food prices co-move to a high degree.