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Machine learning and data-driven techniques have become very famous and significant in several areas in recent times. In this paper, we discuss the performances of some machine learning methods with the case of the catBoost classifier algorithm on both loan approval and staff promotion. We compared the algorithm's performance with other classifiers. After some feature engineering on both data, the CatBoost algorithm outperforms other classifiers implemented in this paper. In analysis one, features such as loan amount, loan type, applicant income, and loan purpose are major factors to predict mortgage loan approvals. In the second analysis, features such as division, foreign schooled, geopolitical zones, qualification, and working years had a high impact on staff promotion. Hence, based on the performance of the CatBoost in both analyses, we recommend this algorithm for better prediction of loan approvals and staff promotion.

Modeling traffic distribution and extracting optimal flows in multilayer networks is of the utmost importance to design efficient, multi-modal network infrastructures. Recent results based on optimal transport theory provide powerful and computationally efficient methods to address this problem, but they are mainly focused on modeling single-layer networks. Here, we adapt these results to study how optimal flows distribute on multilayer networks. We propose a model where optimal flows on different layers contribute differently to the total cost to be minimized. This is done by means of a parameter that varies with layers, which allows to flexibly tune the sensitivity to the traffic congestion of the various layers. As an application, we consider transportation networks, where each layer is associated to a different transportation system, and show how the traffic distribution varies as we tune this parameter across layers. We show an example of this result on the real, 2-layer network of the city of Bordeaux with a bus and tram, where we find that in certain regimes, the presence of the tram network significantly unburdens the traffic on the road network. Our model paves the way for further analysis of optimal flows and navigability strategies in real, multilayer networks.

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