undersampling

damianhorna/multi-imbalance

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

MatteoM95/Default-of-Credit-Card-Clients-Dataset-Analisys

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

atif-hassan/Regression_ReSampling

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

NestorRV/undersampling

A Scala library for undersampling in imbalanced classification.

NestorRV/SOUL

SOUL: Scala Oversampling and Undersampling Library.

AlirezaKm/HUE

Hashing-Based Undersampling Ensemble for Imbalanced Pattern Classification Problems

jayanttikmani/cross-sellingCaravanInsuranceUsingDataMining

Data Mining of Caravan Insurance Data Set Using R

gulabpatel/Handle_Imbalance

RudraChatterjee/Machine-Failure_Prediction_EnsembleMethods_ModelTuning

This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline

cedoula/Credit_Risk_Analysis

Build and evaluate several machine learning algorithms to predict credit risk.

Louis-GUENEGO/NEXYS4ddr_microphone

An audio project with the NEXYS 4 ddr

Deving789/Credit_Risk_Analysis

Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.

prabhatk579/credit-card-fraud-detection-using-logistic-regression

Classifying whether the credit card transaction is fraudulent or not using Logistic Regression

prabhatk579/credit-card-fraud-detection-using-support-vector-machine

Classifying whether the credit card transaction is fraudulent or not using Support Vector Machines

skinan/Improved-Sampling-and-Feature-Selection-to-Support-Extreme-Gradient-Boosting-For-PCOS-Diagnosis

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

hypper-team/hypper

Hypergraph-based data mining for binary classification

alexandrebvd/udacity-capstone-project-credit-card-fraud-prediction

Udacity capstone project | Credit card fraud prediction | Supervised Learning | Ensemble model | Data Sampling

Ayda-Darvishan/Tuning-ML-Classifiers

The project includes building seven different machine learning classifiers (including Linear Regression, Decision Tree, Bagging, Random Forest, Gradient Boost, AdaBoost, and XGBoost) using Original, OverSampled, and Undersampled data of ReneWind case study, tuning hyperparameters of the models, performance comparisons, and pipeline development for productionizing the final model.

cdalsania/Credit_Card_Fraud_Detection

This project researched the credit card transaction dataset and tried various machine learning classification models on the dataset to determine the best model that would flag suspicious activity more accurately.

mserra0/Credit-Card-Fraud-Detection

A machine learning project addressing credit card fraud detection using imbalanced datasets. Utilizes techniques like cost-sensitive learning, SMOTE, and ensemble models for high precision and accuracy, emphasizing robust performance despite challenging data distributions.

RamEppala/imbalanceddatasetproject

Machine Learning Project on Imbalanced Data in R

RomeroBarata/bimba

Sampling Algorithms for Two-Class Imbalanced Data Sets in R

Chandradithya8/Handling_Imbalanced_Dataset

Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class.

cviaai/IGS

Iterative gradient sampling

jCodingStuff/NLPReddit

Multinomial classification tasks in Reddit

kpratikin/Credit-Card-Fraud

Identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. (Python, Logistic Regression Classifier, Unbalanced dataset).

NestorRV/undersampling_memory

undersampling: A Scala library for undersampling in imbalanced classification.

ravising-h/The-Great-Data-Science-Challenge

A text analysis challenege on Hackerearth by Infosys where data was highly imbalanced.

Safaa-p/Fraudulent-Insurance-Claims-Detection

Different models to detect if a claim is fraudulent or not

Sayansurya/Project-on-Class-Imbalance-Problem

shivtosh/Feature-engineering

This repository has the code for implementation of Principal Component Analysis, Upsampling (SMOTE), Downsampling (Random Undersampler) and combined via SMOTETomek.

ZihaoChen0319/Deep-MR-Reconstruction-And-Undersampling-Pattern-Learning

This repository build a deep learning framework to learn task-adaptive under-sampling masks and to reconstruct MR image jointly.

juliorodrigues07/tumour_detection

Brain tumour detector built with YOLOv8 model.

mcarocortes/Fraudulent_Transactions

Implementación de modelos de detección de fraude en tarjetas de crédito utilizando técnicas de aprendizaje automático y detección de anomalías. Se aborda el problema del desbalance de clases y se optimiza el rendimiento del modelo para minimizar falsos negativos.

UNITES-Lab/sparse-cafm

This is the official accompanying repository for the paper – SparseC-AFM: a deep learning method for fast and accurate characterization of MoS2.