An expository study of hitting probabilities for one-dimensional random walks — from the simple symmetric walk to bounded and finite-variance steps — using difference equations and martingale methods. Advised by Prof. Yuki Chino.

A supervised-learning study on an imbalanced 30,000-client dataset (~22% default), comparing five classifiers with a focus on minority-class performance rather than misleading accuracy. Course project at École polytechnique.