Predicting insurance charges using linear regression models

Wenyu Dai

doi:10.54254/2753-8818/51/2024CH0161

Research Article

Open access

Published on 1 November 2024

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Dai,W. (2024). Predicting insurance charges using linear regression models. Theoretical and Natural Science,51,51-57.

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Predicting insurance charges using linear regression models

Wenyu Dai *^,1,

¹ College of Letters & Science, University of Wisconsin-Madison, WI, 53706, United States

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/51/2024CH0161

Abstract

Linear regression method can be performed to predict the outcome from one or many input values. Its versatility allows it to be applied on many datasets that contain correlated values. However, researches on the application of linear regression on medical insurance costs, a highly important part of people’s life, are few. This paper studies an insurance dataset from Kaggle by applying linear regression on it. The author validates the dataset at first and explores the correlation between each individual factor and their corresponding charges to better show how insurance costs differ from person to person with different background. Many figures are also included to help visualize the correlation between factors. In the end, the author creates a multilinear regression model to predict the insurance charges. The R-Squared score of the model and a result table including regression coeffects are also provided to show the accuracy and details of the model.

Keywords

Insurance, charge prediction, linear regression.

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Cite this article

Dai,W. (2024). Predicting insurance charges using linear regression models. Theoretical and Natural Science,51,51-57.

Data availability

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

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About volume

Volume title: Proceedings of CONF-MPCS 2024 Workshop: Quantum Machine Learning: Bridging Quantum Physics and Computational Simulations

Conference website: https://2024.confmpcs.org/

ISBN：978-1-83558-653-2(Print) / 978-1-83558-654-9(Online)

Conference date: 9 August 2024

Editor：Anil Fernando, Marwan Omar

Series: Theoretical and Natural Science

Volume number: Vol.51

ISSN：2753-8818(Print) / 2753-8826(Online)

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