Email Spam Filter using SVM Classifier
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Updated
Jan 7, 2017 - MATLAB
Email Spam Filter using SVM Classifier
Using naive bayes classfier to predict spam
Email Spam Detection using Java and Apache Spark MLib
This email spam checker uses two machine learning classifiers: kNN and Naive Bayes.
Detected Spammed emails from an ‘SMS spam collection dataset’ with 98% accuracy. Used NLTK and Sklearn to preprocess the text data and classification.
The uploaded codes help to classify emails into spam and non spam classes by using Support Vector Machine classifier.
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.
Create a `Naive Bayes` model for email spam detection
Spam Email Detection using Naive Bayes
Python || Machine Learning
Some Machine Learning Experiments
This Model is used to Predict Emails data. Either emails are Spam or Normal (Ham) Mail.
Email Spam detection using KNN and Decision Trees Models Trained using the Spambase database
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.
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!
Email Spam detection using Machine Learning
Email Spam Detection using Machine Learning
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