International Journal of Social Science & Economic Research
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Title:
BUILD LINEAR REGRESSION MODELS FOR INSURANCE CHARGES

Authors:
Ziyi Jiang

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Ziyi Jiang
Carnegie Mellon University

MLA 8
Jiang, Ziyi. "BUILD LINEAR REGRESSION MODELS FOR INSURANCE CHARGES." Int. j. of Social Science and Economic Research, vol. 7, no. 2, Feb. 2022, pp. 422-429, doi.org/10.46609/IJSSER.2022.v07i02.012. Accessed Feb. 2022.
APA 6
Jiang, Z. (2022, February). BUILD LINEAR REGRESSION MODELS FOR INSURANCE CHARGES. Int. j. of Social Science and Economic Research, 7(2), 422-429. Retrieved from doi.org/10.46609/IJSSER.2022.v07i02.012
Chicago
Jiang, Ziyi. "BUILD LINEAR REGRESSION MODELS FOR INSURANCE CHARGES." Int. j. of Social Science and Economic Research 7, no. 2 (February 2022), 422-429. Accessed February, 2022. doi.org/10.46609/IJSSER.2022.v07i02.012.

References
[1]. Seber, G.A. and Lee, A.J., 2012. Linear regression analysis. John Wiley & Sons.
[2]. Ranstam, J. and Cook, J.A., 2018. LASSO regression. Journal of British Surgery, 105(10), pp.1348-1348.
[3]. Marquardt, D.W. and Snee, R.D., 1975. Ridge regression in practice. The American Statistician, 29(1), pp.3-20.
[4]. Kira, K. and Rendell, L.A., 1992. A practical approach to feature selection. In Machine learning proceedings 1992 (pp. 249-256). Morgan Kaufmann.
[5]. Hackeling, G., 2017. Mastering Machine Learning with scikit-learn. Packt Publishing Ltd.
[6]. Akossou, A.Y.J. and Palm, R., 2013. Impact of data structure on the estimators R-square and adjusted R-square in linear regression. Int. J. Math. Comput, 20(3), pp.84-93.

ABSTRACT:
In this project, the linear regression model is performed to predict the insurance premium fees. The model is programmed by Python and the results show that it achieves pretty good accuracy, and it is easy for people to identify the important variables. Moreover, we compared Lasso and Ridge regression for further feature importance exploration.

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