Transparent price forecasting AI Model for basic food commodities in Uganda

Abstract

Price fluctuations have been on the rise, especially for the basic food commodities in Uganda and are attributed to many causes. The use of machine learning algorithms was used to forecast the future prices in order to address the issue of price fluctuation. Several machine learning models were employed to implement the price forecasting model, including linear regression,KNN regression,support vector machine, XGBoost,light GBM, Random Forest, and CatBoost regressor. The accuracy of each model was evaluated to determine the most precise and reliable one, which would be utilized for predictions.The models were fine tuned to improve their accuracy and performance using the grid search. To enhance the explainability and interpretability of the model, Explainable Artificial intelligence (XAI) techniques were employed. Specifically, LIME and SHAP were utilized to explain the decision-making process of the black box machine learning algorithms The study extensively reviewed relevant research papers in the field to provide a comprehensive literature review. The methodology section proposed various approaches to enhance the accuracy, precision, and prevent over-fitting in the model. Furthermore, the models were evaluated using evaluation metrics such as mean absolute error, mean squared error, root mean squared error and r-squared score upon which we selected the best price food forecasting model. According to our results, the XGboost outperformed all the other models in performance and was selected to be deployed into our web application. In conclusion, this research project aimed to address the issue of basic food commodity price fluctuations through price forecasting using AI and machine learning algorithms. The study incorporated explainability techniques to enhance the understanding of the model’s decisions. By evaluating different models and considering various factors, this research contributes to the development of effective strategies for mitigating price fluctuations in the agricultural sector.