This analysis builds predictive models on an auto insurance dataset of 6,528 customers to estimate crash probability and claim cost. Using logistic and linear regression in R, we explore key risk factors, prepare the data, and compare multiple models. A stepwise logistic regression model is selected as the best classifier, achieving 79.1% accuracy. Predictions are generated for a holdout evaluation set.