Abstract: Disclosed is a computer-implemented method for classification and/or prediction of data samples on unbalanced datasets, the method comprising: receiving unbalanced training dataset; generating at least one first model and at least one second model using unbalanced training dataset, the generation of the at least one first model comprises generating first training dataset comprising first data subset and second data subset of equal amounts of data; the generation of the at least one second model comprises generating second training dataset comprising third data subset and fourth data subset of equal amounts of data, machine learning algorithm(s) are employed for learning from the first and second training datasets; generating composite model using the first model(s) and the second model(s); employing composite model for classification and/or prediction on unbalanced test dataset for generating output, the output includes at least one classified data and/or a prediction for a data sample.