🎲 Iterable dataset resampling in PyTorch

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

A Scala library for undersampling in imbalanced classification.

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

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

SOUL: Scala Oversampling and Undersampling Library.

Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems

Build and evaluate several machine learning algorithms to predict credit risk.

Data Mining of Caravan Insurance Data Set Using R

An audio project with the NEXYS 4 ddr

Classifying whether the credit card transaction is fraudulent or not using Support Vector Machines

Hypergraph-based data mining for binary classification

Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.

This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline

Classifying whether the credit card transaction is fraudulent or not using Logistic Regression

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

Sampling Algorithms for Two-Class Imbalanced Data Sets in R

Udacity capstone project | Credit card fraud prediction | Supervised Learning | Ensemble model | Data Sampling

This project researched the credit card transaction dataset and tried various machine learning classification models on the dataset to determine the best model that would flag suspicious activity more accurately.