Email Spam Classifier Project Implementation using Naive Bayes and SVM Machine learning Technique

This project implements a Spam Mail Detection System using Python and machine learning techniques. The model classifies emails as either spam or ham (not spam) based on their content. It uses the SVM (Support Vector Machine) algorithm to achieve high accuracy in text classification tasks.

Data science projects: Iris flower classification and Spam email detection using machine learning in Python.

A machine learning project for spam email detection using TF-IDF feature extraction and Naïve Bayes classification, implemented in Python.

A Python-based Email Spam Detection project that classifies emails as spam or ham using machine learning. This project demonstrates text preprocessing, feature extraction, and classification in a single Python file for easy understanding and implementation.

Email Spam Detection project built using Python in Jupyter Notebook. This project applies supervised machine learning techniques to classify emails as spam or ham using text preprocessing, feature extraction, and classification models to improve email filtering accuracy and automation.

Spam Email Detection – Machine Learning Project I built a Spam Email Detection system using Python and Machine Learning, implementing both a command-line version and an interactive web app. The model uses TF-IDF vectorization and Naive Bayes for text classification, achieving strong accuracy in distinguishing spam from normal emails.

The Email Spam Detection Web Application is a full-stack project built with Python 3, Django 5, and MySQL, designed to classify emails as Spam or Not Spam using a Machine Learning Spam Classifier. It leverages TF-IDF feature extraction and Naive Bayes classification for accurate detection. Users can paste email content, analyze it in real-time,