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.

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。

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

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

Synthetic Minority Over-sampling Technique

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