😎 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

Efficient Permutation-based GWAS for Normal and Skewed Phenotypic Distributions

pylambertw - sklearn interface to analyze and gaussianize heavy-tailed, skewed data

Official Implementation of ACMMM'21 paper "Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting"

LambertW R package: Lambert W x F distributions and Gaussianization for skewed & heavy-tailed data

Skin lesion image analysis that draws on meta-learning to improve performance in low data and imbalanced data regimes.

Quantum ML for extremely imbalanced data

data engineering challenges and fun

Fix data skew by packing into bins

Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

Machine Learning Nano-degree Project : To help a charity organization identify people most likely to donate to their cause

This repository contains data visualization programs on various datasets done using python.

Trying to recogize and predict fraud in financial transactions is a good example of binary classification analysis. A transaction either is fraudulent, or it is genuine. What makes fraud detection especially challenging is the is the highly imbalanced distribution between positive (genuine) and negative (fraud) classes.

No description provided.

Space-Time Statistical Quality Control of Extreme Precipitation Observation

No description provided.

Course Major Project of Pattern Recognition and Machine Learning( CSL2050 )

No description provided.

This is a data mining model to predict client behavior within an organization, enabling better alignment with client needs. The model determines whether clients are likely to churn using advanced data preprocessing and imbalanced learning techniques. The dataset for this analysis was sourced from Kaggle.

Build predictive models on highly skewed data by selecting an example of fraudulent transactions in the financial institutions🚀

A base possui informações obtidas de análises químicas de vinhos da mesma região da Itália, porém são provenientes de 3 diferentes cultivadores. A análise mostra a quantidade de 13 componentes achados em cada um dos 3 tipos de vinhos.

Predicting Time of Arrival for Food Delivery Service

A Statistical Data Analysis project from TripleTen

Opportunities and challenges in partitioning the graph measure space of real-world networks

This project demonstrates building a classification model for imbalanced data. Feature engineering, feature selection and extensive EDA. Comparing of logistic regression, random forest and ADA Boost models are done before finalizing the best model.

Normalization on skewness and kurtosis of a dataset

This project was completed as part of the CIT 650 "Intro To Big Data" course at Nile University.