imbalanced-learn

ICDE'20 | A general & effective ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

NeurIPS’20 | Build powerful ensemble class-imbalanced learning models via meta-knowledge-powered resampler. | 设计元知识驱动的采样器解决类别不平衡问题

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

Credit risk analysis using scikit-learn and imbalanced-learn.

Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.

Built and evaluated several machine-learning models to predict credit risk using free data from LendingClub.

Credit risk is an unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. Use imbalanced-learn and scikit-learn libraries to build and evaluate machine learning models using resampling.

Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.