Back To Projects

Predicting International Commodity Prices, Production, and Land Usage Towards Reducing Agricultural Emissions

Sahir G.

Our long-term goal for this research project was to analyze food insecurity and determine if we could predict the emissions and pricing of different food commodities.


Our long-term goal for this research project was to analyze food insecurity and determine if we could predict the emissions and pricing of different food commodities. Towards this goal, we needed to understand the changes in food production, land use, efficiency, and pricing of these commodities in a sample of different countries. Given the rampant food insecurity heavily influenced by climate change, aiding in reducing the impacts of climate change could be a crucial step towards alleviating food insecurity. Our approach to answering this question was to first gather and analyze data and then use an univariate ARIMA model for time series to further develop our hypothesis and figure out if it was possible to predict future trends. Despite not including external factors like world conflicts, inflations, weather, and trade, the univariate ARIMA is able to predict the prices, production, and efficiency well on some commodities for some countries. Our major conclusions were that our research could be used to make cursory predictions for future values of the food prices and agricultural emissions.

Explore More!

Sahir G.
Landon Butler
PhD Candidate at Berkeley, UPenn CS Master's

Related Projects

Prediction of Nitrogen Dioxide Level using Machine Learning Models

As we release more pollution into the atmosphere as factory and vehicle byproducts, acid rains are formed and are constantly damaging the environment and harming wildlives under and above the ocean. If the occurrence of acid rains can't be limited, the phenomenon of this man-made disaster will threaten the health and safety of billions. By predicting the potential development of acid rain with nitrogen dioxide (NO2) levels, one of the major components of acid rain, we could prevent it by limiting the number of specific gasses produced.
Audrey W.
Mentored by Matthew Radzihovsky
workspace_premium
Comparing Machine Learning and Soft-Voting Ensemble Models for Credit Card Fraud Detection in Imbalanced Transaction Data

This study evaluates multiple machine-learning models for detecting highly imbalanced credit card fraud, finding that the configured XGBoost model achieved the best overall performance with an AP of 0.9084 and F1 score of 0.8534. However, the results are specific to the synthetic dataset and experimental setup, with several limitations preventing broader conclusions about XGBoost’s superiority.
Christian B.
Mentored by Eric Bradford
Predicting Precipitation and Other Weather Conditions With Logistic Regression and Random Forest Classifiers

An issue we have with our current algorithms is the inability to predict weather accurately for any time range further than ten days. Our intentions with this project were to create a model that could get an accuracy within 5% of our current weather prediction algorithms.
Vadim Y.
Mentored by Darnell Granberry