aniass/Spam-detection

Spam detection in SMS messages with BERT model and Machine Learning algorithms

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Experimental

Implements parallel classification pipelines using traditional ML (Naive Bayes, Logistic Regression, SVM, Random Forest) with bag-of-words vectorization alongside a fine-tuned BERT transformer model, both achieving 97-98% accuracy on the SMS Spam Collection dataset. Includes complete NLP preprocessing (tokenization, stop-word removal, stemming) and addresses class imbalance via SMOTE resampling. Provides modular Python scripts and Jupyter notebooks for model training, inference, and data cleaning using scikit-learn, NLTK, and Hugging Face Transformers.

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Last pushed

Jul 06, 2025

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