One of the important research problems in the context of financial institutions is the assessment of credit risk and the decision to whether grant or refuse a loan. Recently, machine learning based methods are increasingly employed to solve such problems. However, the selection of appropriate feature selection technique, sampling mechanism, and/or classifiers for credit decision support is very challenging, and can affect the quality of the loan recommendations. To address this challenging task, this article examines the effectiveness of various data science techniques in issue of credit decision support. In particular, processing pipeline was designed, which consists of methods for data resampling, feature discretization, feature selection, and binary classification. We suggest building appropriate decision models leveraging pertinent methods for binary classification, feature selection, as well as data resampling and feature discretization. The selected models’ feasibility analysis was performed through rigorous experiments on real data describing the client’s ability for loan repayment. During experiments, we analyzed the impact of feature selection on the results of binary classification, and the impact of data resampling with feature discretization on the results of feature selection and binary classification. After experimental evaluation, we found that correlation-based feature selection technique and random forest classifier yield the superior performance in solving underlying problem.

In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures.

Renewable energy sources play a crucial role in sustainable development strategies, addressing environmental protection, energy security, economic growth, and enhancing quality of life. Renewable energy sources are significantly less damaging to the environment compared to traditional energy sources, as they help lower greenhouse gas emissions, decrease air and water pollution, and consequently, limit environmental degradation. The study helped formulate the following research goals: assessment of changes in the use of renewable energy sources and the degree of environmental degradation in the European Union countries, and comparison of the spatial differentiation of the EU countries in terms of the use of renewable energy sources and the degree of environmental degradation, taking into account both indicated criteria. The survey covers the years 2015, 2019, and 2021. The article highlights the important role of analytical methods in the analysis of renewable energy consumption and its impact on the environment. The Self-Organizing Map was used for grouping the European Union countries based on renewable energy sources’ levels and environmental degradation, allowing the identification of patterns and clusters. The results showed that the Scandinavian countries are leading the way in renewable energy sources and low environmental degradation, while the Eastern European countries are struggling, with a low share of renewable energy sources and high degradation.