Vehicle Insurance Purchase Prediction Using Machine Learning
Abstract
Aim: The research intends to Comparative Analysis of vehicle insurance prediction with Random Forest and logistic regression.
Materials and Methods: In this study, two groups Random Forest in comparison with Logistic Regression to improve Accuracy. To improve Accuracy. 100 dataset samples had been used for research study which contains 80% for training and remaining 20% for testing. For predicting vehicle insurance which were estimated by using a 10 N sample size for each,
Results: The Random Forest improves the data of accuracy with (93.605%). accuracy against (84.583%). for Logistic Regression. With a significant value of p= e of p=0.002 (p is statistically significant for prediction of vehicle insurance).
Conclusion: The Random Forest method for prediction of vehicle insurance significant improvement over Logistic Regression because of its higher accuracy.
How to Cite This Article
Tamma Sreya, Badugu Renuka, Sampathi Kalyan Chakravarthi, Nakkala Chaitanya Krishna, Mallela Narasimha Rao (2025). Vehicle Insurance Purchase Prediction Using Machine Learning . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 6(2), 761-765. DOI: https://doi.org/10.54660/.IJMRGE.2025.6.2.761-765