Performance comparison of classification algorithms

Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.

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.

Malicious URL detector built with deep exploration on feature engineering.

Synthetic Minority Over-sampling Technique Implementation

Data warehouse and analytics project to predict bike theft prediction from TPS data

We leverage machine learning and data analysis to address real-world challenges in the copper industry. Our documentation encompasses data preprocessing, feature engineering, classification, regression, and model selection. Explore how we've enhanced predictive capabilities to optimize manufacturing solutions.

ML-based prediction of NSCLC recurrence with gene expression data

Fraud Machine Learning Pipelining for experimenting with SMOTE

The computing scripts associated with our paper entitled "Oversampling Highly Imbalanced Indoor Positioning Data using Deep Generative Models".

Corporate Credit Rating Prediction System - Knowledge Engineering Project - A.A. 2023-2024

This project addresses cybersecurity in aviation by developing a machine learning-enhanced intrusion detection and prevention system (IDPS) for aircraft networks. Combining YARA-based signature detection with behavior-based (ML) anomaly detection, the system mitigates cyber threats in real-time, protecting aircraft from sophisticated attacks.

Routines to perform cross-validation and nested cross-validation using data transformations

demonstrate different models such as Variational Autoencoders and GANs in a variety of datasets, including tabular, text and image data, including the generation of synthetic data for comparison of their effectiveness in all models for each kind of dataset

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

Scripts developed for the paper "Understanding when SMOTE works", developed in the "Knowledge Extraction and Machine Learning" (ECAC) class.

Awarded 1st position in 'Google-Krenicki Hackathon' for cloud based ML framework to predict US household income for real-estate brokerage firm; using Google Colab and AWS S3