Theoretical and Natural Science

Theoretical and Natural Science

TNS Vol.2 (CIAP 2022), 02 February 2023

Open Access | Article

Python-based Model Optimization Platform

Zhangjin Ding 1
1 School of Hospitality and Tourism Management, University of Surrey, Guildford, England, United Kingdom, GU2 7XH

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, TNS Vol.2 (CIAP 2022), 221-227
Published 02 February 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Zhangjin Ding. Python-based Model Optimization Platform. TNS (2023) TNS Vol.2 (CIAP 2022): 221-227.


With the increasing degree of informatization in today's society, the presentation of problems has become more complex, which puts forward higher requirements for people's ability to solve problems. Python is a popular language recently, and it is very popular among developers because of the many mature libraries that are encapsulated in it. People can use related libraries in Python and use open-source related libraries for algorithm research. The main purpose of this paper is to study the optimization platform of the model based on Python. This paper mainly analyzes the characteristics of the Python language and the structure of Python programming, and uses the relevant database of Python to realize the modeling work. The experiment shows that the accuracy of the decision tree model is 96.94 %, the accuracy of the KNN classification model is 89.05%.


Python Programs, Python Language, Database Sets, Model Optimization


1. Rao C S , Karunakara K . Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI[J]. Multimedia Tools and Applications, 2022, 81(5):7393-7417.

2. Alonso D H , Rodriguez L , Silva E . Flexible framework for fluid topology optimization with OpenFOAMand finite element-based high-level discrete adjoint method (FEniCS/dolfin-adjoint)[J]. Structural and Multidisciplinary Optimization, 2021, 64(6):4409-4440.

3. Farina F , Camisa A , Testa A , et al. DISROPT: a Python Framework for Distributed Optimization[J]. IFAC-PapersOnLine, 2020, 53(2):2666-2671.

4. Ullah A , Wang B , Sheng J , et al. Optimization of software cost estimation model based on biogeography-based optimization algorithm[J]. Intelligent Decision Technologies, 2020, 14(4):1-8.

5. Jammalamadaka K , Parveen N . Testing coverage criteria for optimized deep belief network with search and rescue[J]. Journal of Big Data, 2021, 8(1):1-20.

6. Cragnolini T , Sahota H , Joseph A P , et al. TEMPy 2: a Python library with improved 3D electron microscopy density-fitting and validation workflows[J]. Acta Crystallographica Section D Structural Biology, 2021, 77(1):41-47.

7. R Sreenivasulu, Chaitanya G , Kumar G V , et al. Inverse Kinematic Solution for Five bar Parallel Linkage Planar Manipulator using PYTHON and Optimization by Taguchi Method[J]. International Journal of Engineering Trends and Technology, 2021, 69(5):94-100.

8. Rao A , Rao C S , Cheruku D R . Differentiating digital image forensics and tampering localization by a novel hybrid approach[J]. Multimedia Tools and Applications, 2022, 81(13):18693-18713.

9. Minquiz G M , Borja V , M López-Parra, et al. Machining Parameters and Toolpath Productivity Optimization Using a Factorial Design and Fit Regression Model in Face Milling and Drilling Operations[J]. Mathematical Problems in Engineering, 2020, 2020(5):1-13.

10. Fearn S , Trembath D . Southern distribution limits and a translocated population of the scrub python Morelia kinghorni (Serpentes: Pythonidae) in tropical Queensland[J]. Herpetofauna, 2020, 36(2):85-87.

11. Rao C S , Karunakara K . Efficient Detection and Classification of Brain Tumor using Kernel based SVM for MRI[J]. Multimedia Tools and Applications, 2022, 81(5):7393-7417.

12. Melingi S B , Mojjada R K , Tamizhselvan C , et al. A self-adaptive monarch butterfly optimization (MBO) algorithm based improved deep forest neural network model for detecting and classifying brain stroke lesions[J]. Research on Biomedical Engineering, 2022, 38(2):647-660.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this journal agree to the following terms:

1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Copyright © 2023 EWA Publishing. Unless Otherwise Stated