Research Article Published: June 15, 2025 Volume 1, Issue 4

Computational Efficiency Optimization in Python: Benchmarking High-Performance Frameworks for Data Science and Machine Learning Workflows

Authors

Priya Kapoor*1

Affiliation: School of Computing, National University of Singapore, Singapore

Abstract

In recent years, Python has become the go-to language for data science and machine learning app developers. It is important to note, however, that data scientists are not always programming experts. In spite of the fact that Python enables them to rapidly develop their algorithms, when they are operating at scale, the need for efficient computation becomes unavoidable. Thus, optimizing high-performance hardware, such as graphics processing units and multi-core processors, to achieve their maximum potential is no simple feat.

It is possible to consider the current narrative survey to be a reference document for practitioners of the Python language, which will assist them in navigating the vast array of tools and approaches that are accessible for use with the Python programming language. The user scenarios that are the focus of our document are designed to cover the majority of the possible circumstances that users may encounter. Tool developers may also find this document useful; by reading it, they will be able to see where our work could be lacking in current tools and be more motivated to fill such gaps. We feel that this information may be of service to tool developers.

Keywords

Python Data science Machine Learning

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How to Cite

APA Style:

Kapoor, P. (2025). Computational Efficiency Optimization in Python: Benchmarking High-Performance Frameworks for Data Science and Machine Learning Workflows. International Journal of Advanced Research in Engineering and Related Sciences, 1(4), 2.

IEEE Style:

P. Kapoor, "Computational Efficiency Optimization in Python: Benchmarking High-Performance Frameworks for Data Science and Machine Learning Workflows," International Journal of Advanced Research in Engineering and Related Sciences, vol. 1, no. 4, paper 2, 2025.

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