This project contains datasets for Cross Site Scripting (XSS), SQL, and LDAP injections. The project also contains the Matlab code for creating SVM, K-NN, Random Forest, and Neural Networks classifiers to detect Web applications attacks.

I built this to apply machine learning classification techniques to a novel dataset of music I had built up in midi format. It is built in Java.

📱 AI-powered phone recommendation system using Flask, Machine Learning, and Waitress. Classifies user queries and suggests the best smartphones based on their needs.

A machine learning web app that predicts customer churn risk in real-time. Built with Streamlit and Scikit-learn, it helps businesses proactively retain valuable customers.

Automatic gmail cleaner using Google's API. This program allows for scheduale or custom cleaning { (to be added) additionally I developed a SPAM detector for tailored cleaning }