PREDICTION USING THE SVM ALGORITHM IN MACHINE LEARNING

Authors

  • Qarshiyev Abduvali Berkinovich Author
  • Tursunov Muhammadsolih Sa’din o’g’li Author
  • Quvondiqov Muhammad Dilmurod o’g’li Author

Keywords:

Machine learning, SVM algorithm, prediction, classification, data analysis, Flask, web integration.

Abstract

This article explores the process of developing a system that predicts users' purchasing probability based on their socio-economic indicators using the Support Vector Machines (SVM) algorithm. The study covers the issues of data training, scaling, and integrating the trained model into a web interface via the Flask framework.

References

1. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297.

2. Pedregosa, F., et al. “Scikit-learn: Machine learning in Python.” Journal of machine learning research, 12, 2825-2830 (2011).

3. Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media.

4. Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python. O'Reilly Media.

5. Collection of scientific articles on information technology of the Samarkand branch of TUIT named after al-Khwarizmi (2025).

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Published

2026-03-10