This project aims to build a logistic regression model of the car insurance market share of If Skadeförsäkring AB. The model is constructed by producing a set of candidates with reduced multicollinearity, subjecting each to purposeful selection of covariates and comparing the resulting summary statistics. These include the Hosmer-Lemeshow and Standardized Pearson goodness-of-fit statistics, pseudo-R^2, AIC, Mallows's Cp and AUC-ROC. The final model is examined by residual and influence diagnostics analysis. Each covariate is analysed in terms of response class frequency distribution, estimation impact and estimation impact over time. The model is benchmarked by simplistic applications of elastic net, PCA-preprocessed logistic regression and random forest. Throughout, a model trained on SMOTE oversampled data is fitted in parallel to investigate the effect of class imbalance. The final model performs in line (AUC ≈ 0.7) with the benchmark correspondents. The SMOTE versions underperform and overfit.