Predictive Modelling of Nickel Potential with the Integration of Multisource Information Based on Random Forest

Business Challenges Mineral exploration activities require robust predictive models that accurately map the possibility that mineral deposits can be found at as pecific location. Suggested Solution Random forest (RF) is a powerful machine data-driven predictive technique unknown in potential mineral mapping. This project explores the performance of RF regression for the nickel deposits in the […]

Business Challenges Predicting Mineral Product (MP) prices have been a significant and difficult task usually addressed by econometric, stochastic-Gaussian, and timeseries methods. None of these methods has proved suitable to characterize the dynamic behaviour and time-related nature of MP markets. Chaos Theory (CT) and Machine Learning (ML) methods can signify the temporal relations of variables, […]