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This project uses the SpamAssassin corpus to build a Naive Bayes classifier that predicts whether emails are spam or ham. After preprocessing and vectorizing the text, the model is trained and evaluated on a test set. The results highlight the challenges of imbalanced text data while demonstrating the full workflow for building a spam detection model in R.

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