Log classification system

Abstract

The exponential growth of system logs in modern IT environments necessitates efficient, automated methods for analyzing and classifying log data. This project presents the development of a Log Classification System using Python, NLP techniques, and machine learning models to categorize logs into predefined classes such as errors, warnings, and informational messages. The system pipeline includes data exploration, clustering with DBSCAN, feature extraction using BERT embeddings, and classification via Logistic Regression. The project leverages PyCharm Professional for development, and a FastAPI backend to enable real-time log processing. Additionally, comprehensive documentation including README and requirements files ensures reproducibility and deployment readiness. By integrating preprocessing, advanced embeddings, and machine learning, the proposed system automates log management, reduces manual effort, and provides actionable insights for system monitoring and anomaly detection. This implementation demonstrates the practical application of modern AI techniques for enhancing operational efficiency in enterprise IT systems.