Interpretable High-Dimensional Advertisement Classification with Regularized Logistic Regression: A Numerically Stable Framework

High dimensional advertisement classification is hard due to the distribution of predictive information across disparate geometric and sparse lexical features, imbalanced class distributions, and the need for systems that explain their decisions in simple and reproducible ways. This work proposes an interpretable and numerically stable binary classification framework based on UCI Internet Advertisements dataset that has 3,279 observations and 1,558 predictive features. The model is a logistic regression trained with a self-written L2 regularization that fits within a leakage safe pipeline that contains stratified partitioning, training-only median imputation, and training-derived feature standardization. To avoid computational instability, numerical safeguards are used, such as sigmoid clipping and stabilized binary cross-entropy computation. The model achieves 97.56% test accuracy, 95.24% precision, 86.96% recall, 99.29% specificity, and a 90.91% F1-score. Training and validation losses are close and go to each other steadily, learning-curve analysis exhibits better generalization as sample size grows. A stable high-performance region is also identified through learning-rate sensitivity analysis. The framework is intrinsically interpretable, since the predictions are directly related to the coefficients that can be inspected. Overall, the results illustrate that a well-regularized linear model can provide good predictive performance, while maintaining transparency, numerical stability, reproducibility, and computational simplicity.