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Reseach Article

Implementation of Curve Fitting using Polynomial Regression in Python

by Ahmad Farhan AlShammari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 6
Year of Publication: 2024
Authors: Ahmad Farhan AlShammari
10.5120/ijca2024923400

Ahmad Farhan AlShammari . Implementation of Curve Fitting using Polynomial Regression in Python. International Journal of Computer Applications. 186, 6 ( Jan 2024), 27-32. DOI=10.5120/ijca2024923400

@article{ 10.5120/ijca2024923400,
author = { Ahmad Farhan AlShammari },
title = { Implementation of Curve Fitting using Polynomial Regression in Python },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 6 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number6/33076-2024923400/ },
doi = { 10.5120/ijca2024923400 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:54.783676+05:30
%A Ahmad Farhan AlShammari
%T Implementation of Curve Fitting using Polynomial Regression in Python
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 6
%P 27-32
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The goal of this research is to develop a curve fitting program using polynomial regression in Python. Curve fitting is an important application in machine learning. It helps to find the curve that best fits to the data points. The polynomial regression is used to model the relationship between the independent variable (x) and the dependent variable (y) using a polynomial function of degree (n). Polynomial regression can provide linear and non-linear models. The basic steps of curve fitting using polynomial regression are explained: preparing observed points, computing matrix, computing transpose of matrix, multiplying by transpose, performing forward elimination, performing back substitution, finding out coefficients, making polynomial equation, computing predicted points, and plotting curve. The developed program was tested on an experimental dataset from Kaggle. The program successfully performed the basic steps of curve fitting using polynomial regression and provided the required results.

References
  1. Sammut, C., & Webb, G. I. (2011). "Encyclopedia of Machine Learning". Springer Science & Business Media.
  2. Jung, A. (2022). "Machine Learning: The Basics". Singapore: Springer.
  3. Badillo, S., Banfai, B., Birzele, F., Davydov I., Hutchinson, L., Kam-Thong, T., Siebourg-Polster, J., Steiert B., Zhang, J. "An Introduction to Machine Learning". Clinical Pharmacology & Therapeutics, 107(4), 871-885.
  4. Forsyth, D. (2019). "Applied Machine Learning". Cham: Springer International Publishing.
  5. Chopra, D., & Khurana, R. (2023). "Introduction to Machine Learning with Python". Bentham Science Publishers.
  6. Raschka, S. (2015). "Python Machine Learning". Packt Publishing Ltd.
  7. Richert, W., & Coelho, L. (2013). "Building Machine Learning Systems with Python". Packt Publishing Ltd.
  8. Jordan, M. I., & Mitchell, T. M. (2015). "Machine Learning: Trends, Perspectives, and Prospects". Science, 349(6245), 255-260.
  9. Das, S., Dey, A., Pal, A., & Roy, N. (2015). "Applications of Artificial Intelligence in Machine Learning: Review and Prospect". International Journal of Computer Applications, 115(9), 31-41.
  10. Dhall, D., Kaur, R., & Juneja, M. (2020). "Machine Learning: A Review of the Algorithms and its Applications". Proceedings of ICRIC 2019: Recent Innovations in Computing, 47-63.
  11. Sarker, I. H. (2021). "Machine Learning: Algorithms, Real-world Applications and Research Directions". SN Computer Science, 2(3), 160.
  12. Brandt, S. (2014). "Data Analysis: Statistical and Computational Methods for Scientists and Engineers". Cham: Springer.
  13. VanderPlas, J. (2017). "Python Data Science Handbook: Essential Tools for Working with Data". O'Reilly Media.
  14. Johansson, R. (2015). "Numerical Python: A Practical Techniques Approach for Industry". Apress.
  15. Atkinson, K. (1989). "An Introduction to Numerical Analysis". John Wiley & Sons.
  16. Chapra, S. C. (2010). "Numerical Methods for Engineers". McGraw-Hill.
  17. Golub, G. H., & Van Loan, C. F. (2013). "Matrix Computations". JHU Press.
  18. Cassel, K. W. (2021). "Matrix, Numerical, and Optimization Methods in Science and Engineering". Cambridge University Press.
  19. Arlinghaus, S. (2023). "Practical Handbook of Curve Fitting". CRC Press.
  20. Zielesny, A. (2016). "From Curve Fitting to Machine Learning". Berlin Heidelberg: Springer.
  21. Lancaster, P., & Salkauskas, K. (1986). "Curve and Surface Fitting: An Introduction". London: Academic Press.
  22. Guest, P. G. (2012). "Numerical Methods of Curve Fitting". Cambridge University Press.
  23. Motulsky, H., & Christopoulos, A. (2004). "Fitting Models to Biological Data using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting". Oxford University Press.
  24. Fahrmeir, L., Kneib, T., Lang, S., & Marx, B. D. (2021). "Regression: Models, Methods, and Applications". Berlin: Springer.
  25. Peckov, A. (2012). "A Machine Learning Approach to Polynomial Regression". Ljubljana, Slovenia, URL: https://kt.ijs.si/wp-content/uploads/2021/11/phd_ aleksandar_peckov.pdf.
  26. Ostertagová, E. (2012). "Modeling using Polynomial Regression". Procedia Engineering, 48, 500-506.
  27. Gupta, A., Sharma, A., & Goel, A. (2017). "Review of Regression Analysis Models". International Journal of Engineering Research & Technology, 6(08), 58-61.
  28. Kumar, S., & Bhatnagar, V. (2022). "A Review of Regression Models in Machine Learning". Journal of Intelligent Systems and Computing, 3(1), 40-47.
  29. Choksi, B., Venkitaraman, A., & Mali, S. (2017). "Finding Best Fit for Hand-drawn Curves using Polynomial Regression". International Journal of Computer Applications, 174(5), 20-23.
  30. Stigler, S. (1974). "Gergonne's 1815 Paper on The Design and Analysis of Polynomial Regression Experiments". Historia Mathematica, 1(4), 431–439.
  31. Python: https://www.python.org
  32. Numpy: https://www.numpy.org
  33. Pandas: https:// pandas.pydata.org
  34. Matplotlib: https://www. matplotlib.org
  35. NLTK: https://www.nltk.org
  36. SK Learn: https://scikit-learn.org
  37. Kaggle: https://www.kaggle.com
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence Machine Learning Curve Fitting Polynomial Regression Numerical Methods Gauss Method Python Programming.