engleski

Application of Machine Learning Methods for Data Analytics in Social Sciences

This article addresses the challenges in the application of artificial intelligence methods such as machine learning, computational intelligence and/or soft computing methods in social sciences. The literature review is performed in order to give a review of different approaches and methods that have been applied so far. The most used method in social sciences and management is the SWOT method, for the identification of strengths, weaknesses, opportunities, and threats when making strategic decisions. Two fundamental characteristics of previous approaches are the development of numerical models of utility functions and the possibility of upgrading these models by formalizing the intuition of strategic decision- makers. There are several shortcomings of the existing approaches. The application of computational intelligence and machine learning methods in social sciences is identified as one of the most challenging and promising areas, which could overcome identified shortcomings. The principles of one popular machine learning method, the decision tree, are explained and a demonstration is performed on the case study of churn prediction. Benchmarking data set from the publicly available repository is used to demonstrate the suggested approach. Evaluation results measured through model accuracy and reliability gave promising results for further analysis. A developed predictive model could serve as a standalone tool or as support for decision- making in social sciences.

Computational intelligence ; data mining ; data science ; machine learning ; social sciences ; business.

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