Learning to distinguish real email addresses from generated ones with CatBoost

Website owners frequently face the issue of fake account registrations, necessitating the implementation of effective protection systems. In a new article on Habr, the author details the process of building a machine learning model using CatBoost to assess the 'humanity' of email addresses and calculate their scoring. The material provides a step-by-step guide through key development stages: dataset preparation and labeling, generating synthetic data for training, and feature engineering techniques. Special attention is given to the use of n-grams and specialized name dictionaries to improve classification accuracy. This approach enables the automation of filtering suspicious registrations and reduces fraud levels on platforms. The article is a valuable resource for developers and security specialists looking to implement ML solutions to combat unwanted traffic and automated bots.