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eISSN: 2581-9615 || CODEN: WJARAI || Impact Factor 8.2 ||  CrossRef DOI

Research and review articles are invited for publication in March 2026 (Volume 29, Issue 3) Submit manuscript

Machine learning implementation in Python: Performance analysis of different libraries

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  • Machine learning implementation in Python: Performance analysis of different libraries

Praggnya Kanungo *

Student, Computer Science, University of Virginia, USA.
 
Review Article
World Journal of Advanced Research and Reviews, 2023, 20(01), 1390-1398
Article DOI: 10.30574/wjarr.2023.20.1.2144
DOI url: https://doi.org/10.30574/wjarr.2023.20.1.2144
 
Received on 29 August 2023; revised on 17 October 2023; accepted on 19 October 2023
 
This research paper presents a comprehensive performance analysis of popular machine learning libraries in Python. We compare the efficiency, accuracy, and scalability of scikit-learn, TensorFlow, PyTorch, and XGBoost across various machine learning tasks, including classification, regression, and clustering. The study evaluates these libraries using standardized datasets and benchmarks, considering factors such as execution time, memory usage, and model performance. Our findings provide valuable insights for data scientists and developers in selecting the most appropriate library for their specific machine learning projects. The results demonstrate that while scikit-learn excels in simplicity and ease of use for traditional machine learning tasks, TensorFlow and PyTorch offer superior performance for deep learning applications. XGBoost shows remarkable efficiency in gradient boosting tasks. This analysis aims to guide practitioners in making informed decisions when choosing machine learning libraries for their Python-based projects.
 
Machine Learning; Python; Performance Analysis; scikit-learn; TensorFlow; PyTorch; XGBoost
 
https://wjarr.com/sites/default/files/fulltext_pdf/WJARR-2023-2144.pdf

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Praggnya Kanungo. Machine learning implementation in Python: Performance analysis of different libraries. World Journal of Advanced Research and Reviews, 2023, 20(1), 1390-1398. Article DOI: https://doi.org/10.30574/wjarr.2023.20.1.2144

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