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

Synthetic Minority Over-Sampling Technique for Regression

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

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

Bank customers churn dashboard with predictions from several machine learning models.

A repository of resources for understanding the concepts of machine learning/deep learning. 

spark tutorial for big data mining。包括app流量运营分析、als推荐、smote样本采样、RFM客户价值分群、AHP层次分析客户价值得分、手机定位数据商圈挖掘、马尔可夫智能邮件预测、时序预测、关联规则、推荐电影好友等。

Synthetic Minority Over-sampling Technique

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

Address imbalance classes in machine learning projects.

Implementation of novel oversampling algorithms.

Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn

Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets

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

Approx-SMOTE: fast SMOTE for Big Data on Apache Spark

This repository presents the code for digital modulation detection in Communication networks

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

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