Abstract :

Medical Insurance Costs Are Influenced By A Wide Range Of Demographic, Lifestyle, And Health-related Factors, Making Accurate Prediction A Complex Task. This Project Aims To Develop A Machine Learning-based Model To Estimate Individual Medical Expenses Using Features Such As Age, Gender, Body Mass Index (BMI), Number Of Children, Smoking Status, And Region. A Publicly Available Dataset Containing 2,772 Records Is Utilized For Analysis And Model Development. The Process Begins With Data Preprocessing And Exploratory Data Analysis To Understand Patterns And Relationships Within The Dataset. A Random Forest Regressor Is Then Implemented To Model The Relationship Between Input Features And Insurance Charges. The Model Achieves A Strong Performance With An R² Score Of 0.885 On The Test Dataset, Indicating Its Effectiveness In Explaining Approximately 89% Of The Variance In Medical Costs. Feature Importance Analysis Reveals That Smoking Status, Age, And BMI Are The Most Significant Factors Influencing Insurance Charges. The Results Demonstrate The Capability Of Machine Learning Techniques To Provide Accurate And Reliable Cost Predictions. This System Can Assist Insurance Companies In Risk Assessment, Premium Calculation, And Policy Planning, While Also Helping Individuals Better Understand And Anticipate Their Medical Expenses.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1233-1240

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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