This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

The Anonymous Synthesizer for Health Data

This tool for generating synthetic tabular data (.csv file)

M.T.D. is a project that tackles the growing risks faced by industries worldwide due to cyber threats. Our project employs a comprehensive approach to address various cyber-attacks, focusing on intrusion and malware threats by utilizing Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (A.I.).

Streamlining the process to generate synthetic data. Just focus on the data, not the code!

Project for applied classical ML course at the Weizmann institute

Generalized Automatic Pipeline for inspecting and fixing uncertainties in your data

Mini Project about synthetic data generation by implementing CTGAN algorithm on tabular data