spam-email-detection

Most Powerful Send Fake Mail Using Any Mail I'd undetectable

This repository contains a Python script that uses various machine learning models to classify spam messages from ham messages. The model is trained on a Popular dataset of Spam emails and we use multiple machine learning models for classification.

🥡️✉️📦️ The SpamBox is an open source separate implementation of the typical email spam folder.

Spam Dector Frontend created using Next JS shadcn ui and tailwind css

The app takes email input in text format from the user and accurately classifies it either as spam or ham (not spam) with an overall accuracy of 95%. You can access the app using the link below.

Spam Email Detection using Machine Learning with TF-IDF, Naive Bayes, and KNN, deployed as a Streamlit web app for real-time classification.

a Python and Jupyter Notebook implementation of NumPy exercises, Condorcet-style majority-vote analysis, and Naive Bayes spam-email classification. This project was developed as Computer Assignment Zero for the Engineering Probability and Statistics course at the University of Tehran.

This repository contains a Python script that uses various machine learning models to classify spam messages from ham messages. The model is trained on a Popular dataset of Spam emails and we use multiple machine learning models for classification.

Repositori ini berisi model untuk deteksi email spam yang diimplementasikan menggunakan Keras dan TensorFlow.

E-mail spam detection using Transformer model.

An training and testing enviroment to find the most efficient machine learning algorithm for spam filtering.

This project is an end-to-end Email Spam Detection System that classifies email messages as Spam or Not Spam (Ham) using Machine Learning. The model is deployed via a Flask web application, allowing users to test emails through a browser interface.

Research works on different classification topics

Here I have Demonstrated Some of my Machine Learning works

Application of naive bayes to filter spam emails, from scratch

These are some of the projects that I have worked on while taking the Machine Learning course at UT Dallas under Prof Anjum Chida in Spring 16. Assignment 2 consists of Naive Bayes and Logistic Regression codes for classifying emails as either spam or ham. Assignment 3 consists of Image segmentation using K-Means and Perceptron algorithm for ema…

CSE422 Final Lab Project FALL2022

I've been learning about machine learning models, so in this project I used Decision Tree Classifier, Random Forest Classifier, and Logistic Regression for Spam email detection and find the model with the best accuracy.

Machine Learning - Binary Classification