Insurance Cost Prediction Using Polynomial Ridge Regression and Random Forest Classifier

G. Rajeswari; J.V. Kowsalya; T.S. Navalakshmi; N.S Sakthi Aashitha; T.S Sakthi Jothi

Original Article

Insurance Cost Prediction Using Polynomial Ridge Regression and Random Forest Classifier

Paper ID 905
Volume 6, Issue 2
Pages 41-43
Published Online: March-April 2025
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Authors

G. Rajeswari¹
J.V. Kowsalya²
T.S. Navalakshmi³
N.S Sakthi Aashitha⁴
T.S Sakthi Jothi⁵

¹Assistant Professor, Department of Computer Science and Engineering, K.L.N. College of Engineering, Sivagangai, Tamilnadu, India. ²³⁴⁵ Final Year Students, Department of Computer Science and Engineering, K.L.N. College of Engineering, Sivagangai, Tamilnadu, India.

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Article Information

DOI: No DOI
Published In: Volume 6, Issue 2 (2025)
Published Online: March-April 2025
Pages: 41-43
Article Type: Original Article
Check for updates: No DOI available for cross-reference

Abstract

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Machine Learning, particularly Polynomial Ridge Regression and Random Forest Classifier, is used to predict medical insurance costs with accuracy and personalization. Polynomial Ridge Regression models complex relationships for cost estimation, while Random Forest Classifier categorizes individuals into cost groups. Key factors like age, BMI, smoking, and region serve as inputs. Data preprocessing ensures accuracy, while real-time feedback enhances predictions. Feature importance analysis helps assess financial risks. With scalability and a user-friendly interface, the system supports transparent, reliable, and adaptive insurance cost management.

Insurance Cost Prediction Using Polynomial Ridge Regression and Random Forest Classifier

Article Information

Abstract

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