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Client behavior prediction with AI model in Banking

ExpertGuided Project

The purpose of this laboratory is to develop different types of classifiers of artificial intelligence and their ensembles for the classification of customers in banking.

4.6 (43 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Banking

Enrollment Count

  • 334

Skills You Will Learn

  • Machine Learning, Artificial Intelligence, Data Science

Offered By

  • IBM

Estimated Effort

  • 2 hours

Platform

  • SkillsNetwork

Last Update

  • May 31, 2025
About this Guided Project
The purpose of this laboratory is to develop different types of classifiers of artificial intelligence and their ensembles for the classification of customers in banking.
During the work, the task of a preliminary analysis of a positive response (term deposit) to direct calls from the bank is solved. In essence, the task is the matter of bank scoring, i.e. according to the characteristics of clients (potential clients), their behavior is predicted (loan default, a wish to open a deposit, etc.).


What you will learn

After completing this lab, you will be able to:
  1. compare different types of classifiers
  2. create an ensemble of models
  3. create an ensemble of classifiers based on neural networks
  4. classify clients on the basis of developed models

Instructors

Yaroslav Vyklyuk

Full Professor, Doctor of Computer Science, PhD

Dr. Yaroslav Vyklyuk is a full professor at the Lviv Polytechnic National University, Department of Artificial Intelligence Systems. He is an author of over 210 scientific works, 10 monographs, and books, a member of the Editorial Board of 6 international scientific journals, member of the Academic Councils on protection Ph.D. and DrSc thesis in "Mathematical modeling and computational methods". Research Interests: Data Science, Applied System Analysis, Mathematical Modeling, and Decision Making of Complex Dynamic Systems (socio-economic, geographical, tourist, and crisis systems) using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, Econometrics, Econophysics and other Advanced Mathematical Methods with implementation into information, WEB, and geographic information systems.

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Bogdan Norkin

Dr.Sc. in applied mathematics.

Research Fellow, V.M. Glushkov Institute of Cybernetics of NAS of Ukraine.

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Kateryna Hazdiuk

PhD of Software Engineering

I am an assistant professor at the Yuriy Fedcovych Chernivtsi National University, Software of Computer Systems Department; an author of over 40 scientific works and 10 training manuals. Research Interests: Mathematical Modeling of Complex Dynamic Systems (bio-like systems, socio-economic, geographical systems), Data Science, Decision Making using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, and other methods.

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Contributors

Rav Ahuja

Global Program Director, IBM Skills Network

Rav Ahuja is a Global Program Director at IBM. He leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. Rav co-founded Cognitive Class, an IBM led initiative to democratize skills for in demand technologies. He is based out of the IBM Canada Lab in Toronto and specializes in instructional solutions for AI, Data, Software Engineering and Cloud. Rav presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from University of Western Ontario.

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