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An investigation of several aggregate ML Models and their efficacy in identifying spam emails.

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bfrizzell01/spambase-classifier

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Spam Email Classification

This project aims to classify emails as 'spam', given a 'bag of words' distribution of significant words. Data was obtained from the UCI Machine Learning Repository Various classifier models, such as Random Forests and AdaBoost, are tuned and compared.

This project was part of an assignment for a machine learning course (SENG 474) at the University of Victoria. A report is also included to summarize results.

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An investigation of several aggregate ML Models and their efficacy in identifying spam emails.

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