To protect the middle class from over-indebtedness, banking institutions need to implement a flexible analytic-based evaluation method to improve the banking process by detecting customers who are likely to have difficulty in managing their debt. In this paper, we test and evaluate a large variety of data balancing methods on selected machine learning algorithms (MLAs) to overcome the effects of imbalanced data and show their impact on the training step to predict credit risk. Our objective is to deal with data unbalance to achieve the best predictions. We investigated the performance of these methods by different learners when classification models are trained using MLAs.Povzetek: Predstavljena je metoda strojnega učenja z neuravnoteženimi podatki za oceno tveganja prezadolžitve srednjega razreda.
For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.
Because of the complexity of the actors and the relationships between them, social networks are always represented by graphs. This structure makes it possible to analyze the effectiveness of the network for the social actors who are there. This work presents a social network analysis approach that focused on processing Facebook pages and users who react to posts to infer influential people. In our study, we are particularly interested in studying the relationships between the posts of the page, and the reactions of fans (users) towards these posts. The topics covered include data crawling, graph modeling, and exploratory analysis using statistical tools and machine learning algorithms. We seek to detect influential people in the sense that the influence of a Facebook user lies in their ability to transmit and disseminate information. Once determined, these users have an impact on business for a specific brand. The proposed exploratory analysis has shown that the network structure and its properties have important implications for the outcome of interest.
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