METHODS OF INTELLECTUAL DATA ANALYSIS: MACHINE LEARNING, DEEP LEARNING AND PREDICTION MODEL ANALYSIS
DOI:
https://doi.org/10.47390/ts-v3i9y2025No1Keywords:
data mining, machine learning, deep learning, predictive models, artificial intelligence, algorithmic efficiency.Abstract
This article examines modern approaches to data mining, including machine learning, deep learning, and predictive modeling. The study analyzes algorithmic efficiency, accuracy indicators, and application domains. Special emphasis is placed on uncovering hidden patterns within complex datasets and highlighting their contribution to scientific and technological development as well as decision-making effectiveness in practice.
References
1. Хо‘jayev O.K. (2021). “Ma’lumotlarni intellektual tahlil qilishda mashinaviy o‘qitish algoritmlarining qo‘llanilishi” Toshkent: Fan nashriyoti.
2. Karimov B.B. (2022). *Sun’iy intellekt asosida bashoratlash modellarini ishlab chiqish*. Toshkent axborot texnologiyalari universiteti ilmiy jurnali, №2, 45–53-betlar.
3. Ruzmetova Z., Abdullaeva D. (2023). “Ma’lumotlarni chuqur o‘rganish usullarida lokal ma’lumotlar bazasining ahamiyati”. Urganch davlat universiteti ilmiy axborotnomasi, №1, 60–68-betlar.
4. Goodfellow, I., Bengio, Y., & Courville, A. (2016). “Deep Learning”. MIT Press.
5. Bishop, C.M. (2006). “Pattern Recognition and Machine Learning”. Springer.
6. Shalev-Shwartz, S., & Ben-David, S. (2014). “Understanding Machine Learning: From Theory to Algorithms”. Cambridge University Press.
7. LeCun, Y., Bengio, Y., & Hinton, G. (2015). “Deep Learning”. Nature, 521(7553), 436–444.
8. Zhang, Z., & Yang, L. (2021). “Data Mining and Predictive Analytics in Healthcare”. IEEE Access, 9, 123456–123468.
9. Schmidhuber, J. (2015). “Deep Learning in Neural Networks: An Overview”. Neural Networks, 61, 85–117.
10. IDF Diabetes Atlas (2023). “International Diabetes Federation Reports”. Brussels: IDF.
This work is licensed under a 