Email Spam Detection Web App
Developed and deployed an Email Spam Detection Web App that leverages machine learning algorithms to classify emails as either spam or legitimate, enhancing email security and user productivity. The application is accessible online, offering a seamless and interactive user experience.

Real-Time Spam Classification:
Utilizes advanced natural language processing techniques to analyze email content and metadata, accurately distinguishing between spam and legitimate emails. This helps users maintain a clutter-free inbox and safeguards them from phishing attacks and malicious content.

Machine Learning Model Integration:
Incorporates a robust machine learning model trained on a large dataset of spam and ham emails, ensuring high accuracy and adaptability to evolving spam tactics. The model continuously improves its performance with new data inputs, enhancing its predictive capability.

User-Friendly Interface and Deployment:
Designed with an intuitive interface that allows users to easily input email text and receive instant classification results. Deployed on Heroku for reliable cloud hosting and scalability, ensuring consistent accessibility and performance.

This project demonstrates my expertise in machine learning model development, natural language processing, and end-to-end deployment using cloud platforms. It showcases my ability to build practical, user-centric solutions that enhance digital communication security.

Click here to try out the Email Spam Detection App!