A Naive Bayes spam/ham classifier based on Bayes' Theorem. A bunch of emails is first used to train the classifier and then a previously unseen record is fed to predict the output.
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Updated
Jan 16, 2018 - Python
A Naive Bayes spam/ham classifier based on Bayes' Theorem. A bunch of emails is first used to train the classifier and then a previously unseen record is fed to predict the output.
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The project leverages Naive Bayes Classifiers, a family of algorithms based on Bayes’ Theorem, which presumes independence between predictive features. This theorem is crucial for calculating the likelihood of a message being spam based on various characteristics of the data.
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This Project is aimed at classifying emails into Spam or Non-Spam Category using KNN, Naive Bayes and Decision Trees. This project doesn't use any existing machine learning library for classification but just pure Python.
This repository is made to support my application of MLH fellowship. This project had been done during my 2 years of work experience at Sailfin Technologies and is stored as a private repository before. The repository contains full process code from email cleaning, data modelling, database authentication (postgres for Salesforce), REST API build…
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