Materiały konferencyjne SEP 2021

10 LITERATURA [1] Crispim, S.F. 2014: Alinhamento dos projetos de TI aos modelos de negócio das organizações, 621– 634. [2] Project Management Institute. 2018: Success in Disruptive Times: Expanding the Value Delivery Landscape to Address the High Cost of Low Performance. Pulse of the Profession, 35. [3] Project Management Institute. 2017: The Standard for Portfolio Management – Fourth Edition. [4] Cooper, R , Edgett,, S., Kleinschmidt, E. (1992). New Product Portfolio Management: Practices and Performance. Journal of Product Innovation Management, (16), pp. 333-351 [5] Oltmann, J. 2008: Project portfolio management: how to do the right projects at the right time. New- town Square, PA: Project Management Institute. [6] Humphreys D. 2019: Mining Productivity and the Fourth Industrial Revolution. Mineral Economics. [7] Dinov I. D. 2018: Data Science and Predictive Analytics: Biomedical and Health Applications using R Data Science and Predictive Analytics: Biomedical and Health Applications Using R. [8] Williams G. 2011: Data Mining with Rattle and R, 175 [9] Fauser, J., Schmidthuysen, M., Scheffold, B. 2015: The Prediction of Success in Project Management. Predictive Project Analytics. Projektmanagement aktuell, 26, pp. 66-74. [10] McCarthy, R. V., McCarthy, M. M., Ceccucci, W., Halawi L. 2019: Applying Predictive Analytics: Finding Value in Data [11] Rebala, G., Ravi, A. and Churiwala, S. 2019: An Introduction to Machine Learning, Deep Learning and Neural Networks. Springer International Publishing. [12] Sharma, M. and Joshi, S. 2020: Analytics in healthcare: a practical introduction. Asia Pacific Business Review [13] Klix, F. 1985: Machine Learning. An Artificial Intelligence Approach. ZAMM - Journal of Applied Mathematics and Mechanics, 65(11): 568–568 [14] Etaati, L. 2019: Machine Learning with Microsoft Technologies. Machine Learning with Microsoft Technologies [15] Dreyfus, G. 2005: Neural Networks. Methoology and Applications, Springer, Berlin, Heidelberg. [16] Wach M.,Chomiak-Orsa I. 2021: The application of predictive analysis in decision-making processes on the example of mining company’s investment projects. 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Szczecin. Title: The application of predictive analysis in the process of managing investment project portfolios on the example of a mining company. ABSTRACT: The aim of this paper is to indicate the possibility of using predictive analytics in decision-making processes at selected stages of the investment project portfolio management process in a mining company. The paper describes the essence of predictive analytics, current applications and requirements for the implementation of predictive analytics in large organiza- tions. The presented topic is complemented by the research utilizing predictive modeling to identify and verify the most effective algorithms and key attributes of investment projects, ena- bling the prediction of significant threats to the scheduled execution of project portfolios. KEYWORDS: Predictive analytics, investment project portfolio management, project man- agement