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Implementation of Multiple Regression using Least Squares and Gradient Descent in Python
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 21 |
| Year of Publication: 2024 |
| Authors: Ahmad Farhan AlShammari |
10.5120/ijca2024923692
|
Ahmad Farhan AlShammari . Implementation of Multiple Regression using Least Squares and Gradient Descent in Python. International Journal of Computer Applications. 186, 21 ( May 2024), 4-9. DOI=10.5120/ijca2024923692
Abstract
The goal of this research is to develop a multiple regression program using least squares and gradient descent in Python. Multiple regression helps to predict the output data based on the features of the input data using a linear polynomial. Least squares is used to minimize the error between the observed and predicted data. Gradient descent is used to find the optimal solution that provides the minimum value of error function. The basic steps of multiple regression using least squares and gradient descent are explained: preparing observed data, initializing weights and bias, computing predicted data, computing error function, computing partial derivatives, updating weights and bias, obtaining prediction equation, computing final prediction, and plotting predicted data. The developed program was tested on an experimental dataset. The program successfully performed the basic steps of multiple regression using least squares and gradient descent and provided the required results.
References
- Sammut, C., & Webb, G. I. (2011). "Encyclopedia of Machine Learning". Springer Science & Business Media.
- Jung, A. (2022). "Machine Learning: The Basics". Singapore: Springer.
- Kubat, M. (2021). "An Introduction to Machine Learning". Cham, Switzerland: Springer.
- Mohammed, M., Khan, M. B., & Bashier, E. B. M. (2016). "Machine Learning: Algorithms and Applications". Crc Press.
- Dey, A. (2016). "Machine Learning Algorithms: A Review". International Journal of Computer Science and Information Technologies, 7 (3), 1174-1179.
- Bonaccorso, G. (2018). "Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning". Packt Publishing.
- Jo, T. (2021). "Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning". Springer.
- Chopra, D., & Khurana, R. (2023). "Introduction to Machine Learning with Python". Bentham Science Publishers.
- Müller, A. C., & Guido, S. (2016). "Introduction to Machine Learning with Python: A Guide for Data Scientists". O'Reilly Media.
- Raschka, S. (2015). "Python Machine Learning". Packt Publishing.
- Forsyth, D. (2019). "Applied Machine Learning". Cham, Switzerland: Springer.
- Sarkar, D., Bali, R., & Sharma, T. (2018). "Practical Machine Learning with Python". Apress.
- Swamynathan, M. (2019). "Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics using Python". Apress.
- Kong, Q., Siauw, T., & Bayen, A. (2020). "Python Programming and Numerical Methods: A Guide for Engineers and Scientists". Academic Press.
- Unpingco, J. (2022). "Python for Probability, Statistics, and Machine Learning". Cham, Switzerland: Springer.
- Brandt, S. (2014). "Data Analysis: Statistical and Computational Methods for Scientists and Engineers". Springer.
- VanderPlas, J. (2017). "Python Data Science Handbook: Essential Tools for Working with Data". O'Reilly Media.
- James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). "An Introduction to Statistical Learning: With Applications in Python". Springer Nature.
- Young, D. S. (2017). "Handbook of Regression Methods". Chapman and Hall/CRC.
- Allison, P. D. (1999). "Multiple Regression: A Primer". Pine Forge Press.
- Berry, W. D., & Feldman, S. (1985). "Multiple Regression in Practice". Sage.
- Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). "Applied Linear Statistical Models". McGraw-Hill/Irwin.
- Sen, A., & Srivastava, M. (2012). "Regression Analysis: Theory, Methods, and Applications". New York, NY: Springer.
- Darlington, R. B., & Hayes, A. F. (2016). "Regression Analysis and Linear Models: Concepts, Applications, and Implementation". Guilford Publications.
- Montgomery, D.C., Peck, E.A., Vining G. G. (2021). "Introduction to Linear Regression Analysis". John Wiley & Sons.
- Olive, D. J. (2017). "Linear Regression". Berlin: Springer International Publishing.
- Seber, G. A., & Lee, A. J. (2003). "Linear Regression Analysis". John Wiley & Sons.
- Yan, X., & Su, X. (2009). "Linear Regression Analysis: Theory and Computing". World Scientific.
- Kutner, N., Nachtsheim, C., & Neter, J. (2004). "Applied Linear Regression Models". McGraw-Hill.
- Weisberg, S. (2005). "Applied Linear Regression". John Wiley & Sons.
- Rawlings, J. O., Pantula, S. G., & Dickey, D. A. (1998). "Applied Regression Analysis: A Research Tool". New York, NY: Springer.
- Massaron, L., & Boschetti, A. (2016). "Regression Analysis with Python". Packt Publishing.
- Python: https://www.python.org
- Numpy: https://www.numpy.org
- Pandas: https:// pandas.pydata.org
- Matplotlib: https://www. matplotlib.org
- NLTK: https://www.nltk.org
- SciPy: https://scipy.org
- SK Learn: https://scikit-learn.org
- Kaggle: https://www.kaggle.com
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