• Mr. Sandeep Bordia
• Bhavyaraj Singh Chauhan
• Chavi Bhatia
• Akshay Suthar

This research proposes an AI-driven approach to automate the identification of the best ML algorithm for a given dataset using meta learning techniques. Machine learning has gained significant attention for its ability to solve diverse problems across various domains. However, selecting the most suitable algorithm for a given dataset remains a challenging task, often requiring extensive experimentation and domain expertise. By extracting statistical and structural features from datasets, we train a meta-model to predict the optimal ML algorithm based on performance metrics such as accuracy, execution time, and computational efficiency. This study evaluates multiple classification, regression and clustering algorithms on benchmark datasets to build a robust recommendation system. Our result demonstrate that the proposed meta-model can effectively predict the most suitable algorithm with high accuracy, reducing the time and effort required for manual selection. This work contributed to the advancement of automated machine learning and provides a framework for optimizing ML workflows in various application.

Identification Of Algorithm from Given Dataset Using AI/ML Techniques

"Identification Of Algorithm from Given Dataset Using AI/ML Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.389-394, June-2025, Available :http://www.jetir.org/papers/JETIRGW06065.pdf

"Identification Of Algorithm from Given Dataset Using AI/ML Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. pp389-394, June-2025, Available at : http://www.jetir.org/papers/JETIRGW06065.pdf