An Email Spam Classifier project, helps you detect your spam email from correct email. Try it out here!

Implemented Preprocessing steps, Feature Extraction techniques and Naive Bayes Classifier in C++. Moreover, we have also implemented all the steps using python for comparative analysis.

Identifying and distinguishing spam SMS and Email using the multinomial Naïve Bayes model.

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.

Would you like to know which e-mail is spam and which is ham?

Email Spam Detection using Machine Learning

Email Spam Detection Using Logistic Regression

This is an Email/SMS spam detection system, built as a project for AutumnnHacks Hackathon. It classifies messages you recieve on emails and sms as spam or not spam.

An end-2-end project

To check email is spam or not spam

Proyek ini bertujuan untuk memeriksa bahwa email yang diterima adalah spam atau ham melalui klasifikasi teks di WEKA menggunakan algoritma J48 Decision Tree dan Naive Bayes Multinomial Text.

This project is to classify emails as spam or not spam using various machine learning models. Hyperparameter tuning is performed to optimize model performance.

Classify the message is spam or not using Multinomial Naive Bayes.

A email spam classifier based on Multinomial Naive Bayes model and running on Streamlit.

Email Spam Tool is a powerful application designed for testing and analyzing email systems by generating and sending bulk emails. This tool is meant for security professionals and developers to evaluate email filtering systems and anti-spam measures.

An email spam detection system with ML and Python

Email Spam detection using Machine Learning

Email Spam Detection

Email spam classification for Naive Bayes, Gradient Boosting Machine, Support Vector Machine and Random Forest

Linear classifier using Support Vector Machines (SVM) which can determine whether an email is Spam or not with an accuracy of 98.7%. Used regularization to prevent over-fitting of data. Pre-processed the E-mails using Porter Stemmer algorithm. Used a spam vocabulary to create a Feature Vector for each E-mail. Prints the top 15 predictors of spam