Overview
Traditional spam filters rely on simple keyword matching. This system uses advanced Natural Language Processing to understand semantics and context to identify sophisticated spam attempts.
Key Features
Semantic Analysis
TF-IDF vectorization and Word2Vec embeddings for deep contextual understanding.
High Precision
Consistently achieves over 98% accuracy on benchmark classification datasets.
The Solution
The system leverages Scikit-learn and complex classification models to provide a real-time message filtering API that adapts to evolving spam patterns.