Vehicle Insurance Purchase Prediction Using Machine Learning

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.