Abstract. 

In the current digital age, the world has a huge amount of data. Therefore, people are more and more confronted with the use of such methods as data analysis and machine learning. Moreover, many people are considering using machine learning algorithms for their own purposes. However, data analysis is a complex process that can hardly be carried out by people who do not have sufficient knowledge both in this field and in programming. This paper presents an approach to give non-expert users the ability to apply machine learning algorithms to their datasets using an application with a graphical interface. There are a lot of challenges involved in creating ML-solutions, even if we take advantage of existing ML-algorithms: feature engineering, outliers’ detection, filling the missing values, ML-method’s hyperparameters optimization and so on. The main point of the research is to find a balance in solving these complex tasks and to provide a Web-based user interface for unexperienced people to enable them to utilize the power of ML-methods in automatic or semi-automatic way. The practical outcome is an information system development, that consists of three interrelated parts: a web application, an API and several microservices that implement ML-algorithms from Scikit-learn library.

Keywords: 

web Application, Graphical User Interface, data analysis, ML automation.

Стр. 55-63.

DOI: 10.14357/20790279230107

 

 

References

1. Sarker, I.H. 2021. Machine Learning: Algorithms, real-world applications and research directions. SN Computer Science 2. Available at: https://doi.

org/10.1007/s42979-021-00592-x (accessed November 15, 2021).

2. Kumar Y., Kaur K., Singh G. 2020. Machine learning aspects and its applications towards different research areas. 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) Proceedings. Dubai. 150-156.

3. Angra S., Ahuja S. 2017. Machine learning and its applications: A review. 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC) Proceedings. Chirala. 57-60.

4. Santu S. K. K., Hassan M. M., Smith M. J., Xu L., Zhai C., Veeramachaneni K. 2022. AutoML to Dateand Beyond: Challenges and Opportunities. ACM

Computing Surveys (CSUR) 54(8). Available at: https://doi.org/10.1145/3470918 (accessed November 17, 2022).

5. Harrison M., eds. 2019. Machine Learning Pocket Reference: Working with Structured Data in Python. 1st ed. Sebastopol, CA, USA: O’Reilly Media. 320 p.

6. Micci-Barreca D. 2001. A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. SIGKDD Explorations 3(1).

Available at: https://doi.org/10.1145/507533.507538 (accessed March 31, 2022).

7. Jović A., Brkić K., Bogunović N. 2015. A review of feature selection methods with applications. 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). Opatija. 1200-1205.

8. Waring J., Lindvall C., Umeton R. 2020. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial Intelligence in Medicine 104. Available at: https://doi.

org/10.1016/j.artmed.2020.101822 (accessed November 21, 2021).

9. Pedregosa F. et al. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12. Available at: https://doi.org/10.48550/

arXiv.1201.0490 (accessed May 12, 2022).