End-to-end implementation of Spam Detection in Email using Machine Learning, Python, Flask, Gunicorn, Scikit-Learn, and Logistic Regression on the Heroku cloud application platform.

A laravel package to check URLs with Google's Safe Browsing API.

The Spam Mail Classification project is a web-based application that uses machine learning to classify emails as spam or ham. It features a Flask backend, a frontend created with HTML, CSS, and JavaScript, and a MySQL database for storing user data and email classifications.

OOPSpam Anti-Spam API blog

ML-powered Flask app to perform spam classification of SMS messages. Uses TFIDF vectorization + logistic regression to achieve ~98% accuracy.

OOPSpam project website

In this repo, I have stored all the files which I used to deploy my naive bayes classifier model on google cloud platform

Classifying SMS Messages as Spam or not(Using Text classification with ML techniques ).

The Email Spam Classifier is a web application powered by machine learning to discern whether an email is spam or not. Built using Flask, Bootstrap, and a trained machine learning model, the project provides a user-friendly interface for inputting email content. The underlying model, trained on diverse email data, swiftly predicts the spam or not.

Spam Ham Classifier: A Python Flask application for categorizing messages as spam or ham. This classification is based on analyzing existing data in the database and predicting the likelihood of a message being spam or ham, without the use of machine learning.

It is django based web app from where users can download machine learning projects as well as they can directly run on the website