Car Insurance Purchase Prediction

Author(s)	Kalle Siva Ranganath, K. Bhanu Prakash
Country	India
Abstract	By contrasting the effectiveness of Random Forest and Support Vector Machine (SVM) algorithms, this study aims to improve machine learning methods for predicting car insurance. 1,002 of the 1,468 samples in the dataset were utilized for training, while the remaining samples were used for testing. Using consistent sample settings, the study used both algorithms to assess how well they predicted insurance results. According to the findings, the Random Forest model outperformed the SVM model with an accuracy of 94.409% as opposed to 85.263%. The credibility of the data was confirmed by statistical analysis, which showed a significant difference between the two approaches with a p-value of 0.002. These results show that Random Forest outperforms SVM and offers a more precise method for predicting car insurance.
Keywords	Car Loan, machine learning, banking industry, Random Forest, support vector machine.
Field	Engineering
Published In	Volume 17, Issue 3, July-September 2026
Published On	2026-07-04

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