A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features

🗂 Split folders with files (i.e. images) into training, validation and test (dataset) folders

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

🎲 Iterable dataset resampling in PyTorch

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

Detect Fraudulent Credit Card transactions using different Machine Learning models and compare performances

📈 🐍 Multidimensional synthetic data generation with Copula and fPCA models in Python

Classification and Gradient-based Localization of Chest Radiographs using PyTorch.

A library for audio oversampling, which tries to offer a simple api while wrapping HIIR, by Laurent De Soras, for minimum phase antialiasing, and r8brain-free-src, by Aleksey Vaneev, for linear phase antialiasing.

Synthetic Minority Over-sampling Technique

Dealing with class imbalance problem in machine learning. Synthetic oversampling(SMOTE, ADASYN).

Implementation of novel oversampling algorithms.

Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

A python library for repurposing traditional classification-based resampling techniques for regression tasks

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

SOUL: Scala Oversampling and Undersampling Library.

Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). 🌻

Advancing Cybersecurity with AI: This project fortifies phishing defense using cutting-edge models, trained on a diverse dataset of 737,000 URLs. It was the final project for the AI for Cybersecurity course in my Master's at uOttawa in 2023.

This example shows how to classify images for imbalanced training dataset using oversampling