Prediction of Tea Production in Rwanda using Data Mining Techniques

Abstract

Agriculture is the main economic activity in Rwanda and tea is major cash crop in Rwanda. There has been extensive research on prediction of tea production but most of the methods applied were the traditional statistical analyzes with limited prediction capability. Data mining algorithm models, linear regression, K-Nearest Neighbor (KNN), Random Forest Regression, Extremely Randomised Trees are discussed in this study to identify critical features in different domains to facilitate accurate prediction of tea production in Rwanda. In this study also, I identified different factors which are strongly associated with tea production and developed data mining models for predicting tea production using training and test data from National Agricultural Export Development Board (NAEB) 2010-2019. The findings reveal that random forest is the best model among the others to predict tea production in Rwanda.