Abstract :

Predicting Insurance Claims Is A Challenging Task Due To The Dynamic And Uncertain Nature Of Policyholder Behavior And Associated Risk Factors. This Project Focuses On Developing A Machine Learning-based Approach To Forecast Insurance Claim Amounts And Frequencies Using Linear Regression. By Leveraging Historical Data From 2020 To 2024, The System Incorporates Advanced Feature Engineering Techniques, Including Analysis Of Past Claim Trends And Risk-category Mapping, To Enhance Prediction Accuracy. The Developed Model Demonstrates Strong Performance, Achieving An Accuracy Score Of 0.94, Indicating Its Effectiveness In Capturing Patterns Within Insurance Data. Additionally, Interactive Visualizations Using Plotly Are Integrated To Provide Clear Insights Into Claim Trends By Comparing Actual And Predicted Values And Identifying Key Loss Patterns. Overall, This Project Highlights The Practical Application Of Regression Techniques In The Insurance Domain. It Enables Insurers To Better Anticipate Financial Risks, Optimize Premium Pricing Strategies, And Improve Decision-making Processes, Thereby Contributing To Enhanced Operational Efficiency And Stability.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1330-1337

Section:

Articles

License:

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

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