The uploaded codes help to classify emails into spam and non spam classes by using Support Vector Machine classifier.
Email Spam Detection using Machine Learning
Email Spam Detection using Java and Apache Spark MLib
This Model is used to Predict Emails data. Either emails are Spam or Normal (Ham) Mail.
A repository containing a list of email addresses identified as sources of spam or other types of fraud.
One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.
A Python-based email spam detector using the Naive Bayes approach.
Detected Spammed emails from an ‘SMS spam collection dataset’ with 98% accuracy. Used NLTK and Sklearn to preprocess the text data and classification.
An end-to-end automated script that will check the latest email from your google mail account and give a brief report whether the email was spoofed or was supposedly impersonated!
Create a `Naive Bayes` model for email spam detection
A simple text classifier in Python that uses the Naive Bayes model to classify e-mails as spam or ham.
Using a decision tree classification model to identify spam emails based on the specific occurrence of certain features and patterns within the email text. The dataset contains over 54 feature variables from over 4000 emails and can be used to make a custom email spam detector.
A Flask-based web app that detects spam emails/SMS using Multinomial Naive Bayes and TF-IDF. Built with NLP, Scikit-learn, and NLTK for high-accuracy classification.
Using naive bayes classfier to predict spam
Some Machine Learning Experiments
Spam Email Detection using Naive Bayes
Email Spam detection using Machine Learning
Email Spam detection using KNN and Decision Trees Models Trained using the Spambase database