Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库

An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case.

NLP based Classification Model that predicts a person's personality type as one of the 16 Myers Briggs personality types. Extremely challenging project dealing with correlation between human psychology and casual writing styles and handling heavily imbalanced classes. Check the app here - https://mb-predictor-motetuzs5q-uc.a.run.app/

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

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.

Multi-View LEArning-based data Proliferator (MV-LEAP) for boosting classification using highly imbalanced classes.

⚖️ Imbalance-degree measure implemented in python.

Deployment of a classification model on a webapp using FLASK for the backend and html/CSS/JS for frontend

Google Developer Group Ahmedabad - Machine Learning for Imbalanced Class Distributions Session code

Developed machine learning models that can help bank tellers to predict telemarketing campaign response from clients. Analyzed precision-recall trade off, customized for models for different business scenarios

The main objective is to build a predictive model that predicts whether a new client will subscribe to a term deposit or not, based on data from previous marketing campaigns.

Spatio-temporal Urban Change Mapping with Time-Series SAR data

Evaluating ensemble performance in long-tailed datasets (Neurips 2023 Heavy Tails Workshop)

Company Bankruptcy Prediction with Naïve Bayes Algorithm

Imbalanced Data Visualization and Random Forest

"Machine learning in banking - predicting lead conversion for a lending product" - my final project from the Data Science bootcamp organized by "Kodołamacz". Article about the project: https://www.kodolamacz.pl/blog/uczenie-maszynowe-w-bankowosci/

Image classification pipeline relying on Deep Learning models dealing with training over multiple datasets with unbalanced labels and classes. In addition, also classification using histogram data was done for better generalization over different datasets.

This repo contains various techniques to handle imbalanced classes

This is an interesting problem in flagging credit risk when all variables are dimensionally reduced

With imbalanced observed data, a search for the best model is conducted. The bank is seeing its customers leave. Wondering if there are patterns to their decision to exit, the bank wishes to anticipate for this trend. When the positive class is the minority in an imbalanced dataset, a model need to be trained for robustness.