International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Comparative Study of Supervised Learning Models for Robust and Scalable Predictive Analytics |
|---|---|
| ABSTRACT | Machine learning models are widely used for predictive decision-making in real-world systems; however, selecting the most suitable model remains a challenging task due to differences in performance and scalability. This project focuses on developing a comparative framework that evaluates multiple supervised learning models to identify the most effective approach for predictive analytics. The proposed system uses Logistic Regression, Support Vector Machine (SVM), and Random Forest to analyze a healthcare diabetes dataset and generate predictions. The models are tested under real-world- like conditions and evaluated using metrics such as accuracy, precision, recall, and F1-score. The system improves reliability by comparing model performance and selecting the most robust approach. This helps in building efficient and scalable predictive systems for practical applications. |
| AUTHOR | N. PRASAD, M. SHEEBA GRACY, M.SAI CHANDU, P.KOTI GANESH, R. VOOHA DEVI Assistant Professor, Department of Information Technology, Sir C R Reddy College of Engineering, Eluru, India Final Year Students, Department of Information Technology, Eluru, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404117 |
| pdf/117_Comparative Study of Supervised Learning Models for Robust and Scalable.pdf | |
| KEYWORDS | |
| References | [1] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [2] C. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, vol. 20, pp. 273–297, 1995. [3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. New York: Springer, 2009. [4] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [5] M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do We Need Hundreds of Classifiers to Solve Real-World Classification Problems?” Journal of Machine Learning Research, vol. 15, pp. 3133–3181, 2014. [6] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Sebastopol, CA: O’Reilly Media, 2019. [7] C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006. [8] Scikit-learn Developers, Scikit-learn Documentation. [Online]. Available: https://scikit-learn.org [9] NumPy Developers, NumPy Documentation. [Online]. Available: https://numpy.org [10] Pandas Developers, Pandas Documentation. [Online]. Available: https://pandas.pydata.org [11] Matplotlib Developers, Matplotlib Documentation. [Online]. Available: https://matplotlib.org [12] Seaborn Developers, Seaborn Documentation. [Online]. Available: https://seaborn.pydata.org [13] IBM Cloud Education, “Supervised Learning.” [Online]. Available: https://www.ibm.com/topics/supervised-learning [14] Analytics Vidhya, “Supervised Machine Learning Algorithms.” [Online]. Available: https://www.analyticsvidhya.com [15] Kaggle Learn, “Introduction to Machine Learning.” [Online]. Available: https://www.kaggle.com/learn/intro-to-machine-learning [16] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011. [17] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016. [18] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. New York: Springer, 2013. [19] K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, MA: MIT Press, 2012. Sir C R Reddy College of Engineering Department of IT COMPARATIVE STUDY OF SUPERVISED LEARNING MODELS FOR ROBUST AND SCALABLE PREDICTIVE ANALYTICS 49 [20] S. Raschka and V. Mirjalili, Python Machine Learning, 3rd ed. Birmingham, UK: Packt Publishing, 2019. |
