MACHINE LEARNING MODELS TO PREDICTION OPIC CRUDE OIL PRODUCTION

This paper aimed to compare the multi-layer perceptron as an artificial neural network and the decision tree model for predicting OPIC crude oil production. Machine learning is about designing algorithms that automatically extract valuable information from data, and it has seen many success stories. The accuracy of these two models was assessed using symmetric mean absolute percentage errors, mean absolute scaled errors, and mean absolute percentage errors. Achieved were the OPIC crude oil production's maximum projected figures. The OPIC crude oil output was also represented by certain descriptive scales and graphs; A comparison was made between the results and the earlier results acquired by the others after the study of the association between the variables revealed statistical significance.