Data mining approach for analyzing factors influencing vegetable prices

Abstract

Vegetables have a special place in the Sri Lankan economy. The price of vegetables, unlike the prices of other products, changes daily. There are several reasons for this and the examples include environmental conditions, supply variability, demand, festivals and seasonality, social environment, political conditions, etc. The main purpose of this research is to analyze and predict the factors influencing daily vegetable price fluctuations using data mining techniques. In this research, the most influential factors for vegetable prices were classified using the classification algorithms J48, Random Tree, Random Forest, and Support Vector Machine, taking into account the data obtained from the secondary data sources. The highest classification accuracy of 97.7143% was given by the Random Forest algorithm and it also recorded the best values for Precision, Recall, F-measure, and MCC comparing with the other three. Furthermore, it is clear that the Random forest algorithm is the most suitable to predict influential factors and it can be recommended for the purpose.