This article explores the application of machine learning algorithms to analyze, predict, and personalize the educational process based on students' academic performance. Through statistical and computational methods, various student-related attributes were analyzed, with the Random Forest algorithm identified as the most accurate predictive model. The study led to the development of an intelligent system for diagnostic evaluation, personalized approaches, and automated pedagogical recommendations. The findings highlight the significant potential of artificial intelligence tools in enhancing the effectiveness of education.

Machine learning, student performance,, random forest

Ikromov, X. TA’LIM JARAYONIDAGI SUN’IY INTELLEKTGA ASOSLANGAN AXBOROTNI QAYTA ISHLASH VOSITALARINING TAHLILI. O ‘ZBEKISTON RESPUBLIKASI OLIY TA’LIM, FAN VA INNOVATSIYALAR VAZIRLIGI ANDIJON DAVLAT UNIVERSITETI UMUMIY PEDAGOGIKA KAFEDRASI, 315.

Umidjon’s, K. I., & Ilhomjon’s, S. D. (2024). EFFECTIVENESS OF BUSINESS PROCESS AUTOMATION IN GROCERY STORES. International Journal of Advance Scientific Research, 5(12), 279-284.

IKROMOV, X. (2024). TALABALARNI MA’LUMOTLAR BAZASINI BOSHQARISH ASOSIDA INNOVATSION AXBOROT TIZIMLARINI ISHLAB CHIQISHGA O ‘RGATISH METODIKASI. News of the NUUz, 1(1.1. 1), 93-96.

Baker, R. S., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In Learning Analytics (pp. 61–75). Springer.

Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618.

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.