GitHub topics: undersampling

Repositories

alessandrosocc/Machine-Learning-Project-2022

Final project for the Machine Learning course at the University of Cagliari in 2022. Analysis of a dataset, use of Machine Learning techniques with Oversampling and Undersampling techniques. Final report with the results obtained.

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MohamedLotfy989/Credit-Card-Fraud-Detection

This repository focuses on credit card fraud detection using machine learning models, addressing class imbalance with SMOTE & undersampling, and optimizing performance via Grid Search & RandomizedSearchCV. It explores Logistic Regression, Random Forest, Voting Classifier, and XGBoost. balancing precision-recall trade-offs for fraud detection.

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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.

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attilalr/cv_with_transforms

Routines to perform cross-validation and nested cross-validation using data transformations

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damianhorna/multi-imbalance

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

Language: Python - Size: 66 MB - Last synced at: 8 days ago - Pushed at: 11 months ago - Stars: 77 - Forks: 11

Luckilyeee/Solar-Flare-Prediction-through-Time-Series-Data-Augmentation

Solar Flare Prediction through Time Series Data Augmentation

Language: Python - Size: 22.4 MB - Last synced at: 6 months ago - Pushed at: 7 months ago - Stars: 0 - Forks: 0

MaxHalford/pytorch-resample

🎲 Iterable dataset resampling in PyTorch

Language: Python - Size: 242 KB - Last synced at: 23 days ago - Pushed at: over 3 years ago - Stars: 91 - Forks: 4

saranya-ponnarasu/Binary-Classification-of-Insurance-Cross-Selling

Predicting customer insurance uptake using a Decision Tree model."

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ramiyappan/Credit-card-Fraud

Explored various resampling techniques to learn from an imbalanced dataset for detecting Credit card frauds.

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mhadeli/Credit_Fraud-Detector

Detecting credit card fraud using a neural network model.

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hugohiraoka/Credit_Card_Customer_Churn_Prediction

Bank Credit Card Customer churn prediction

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juliorodrigues07/url_detection

Malicious URL detector built with deep exploration on feature engineering.

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juliorodrigues07/tumour_detection

Brain tumour detector built with YOLOv8 model.

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IgnOrtega/Financial-payment-system-Fraud

Este proyecto consiste en la detección de fraudes utilizando machine learning, datos desbalanceados y técnicas de muestreo.

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zhanghaoshuang/Data-Analytics-in-Business-Group-Project

Using R Markdown for Data Analysis, Machine Learning

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hypper-team/hypper

Hypergraph-based data mining for binary classification

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Pasqni/Cross_Selling_Prediction

ProfessionAI Data Science Master: Final project for "Fundamentals of Machine Learning" module: Cross Selling Prediction Model

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

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

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Safaa-p/Fraudulent-Insurance-Claims-Detection

Different models to detect if a claim is fraudulent or not

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haniye6776/outlier-detection

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haniye6776/loan-risk

SVM with different kernels and decision trees

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b1llywitant0/Hotel-Booking-Cancellation-V2

Supervised Classification Machine Learning Model Building #1.2 : Improvement to the previous project of hotel booking cancellation prediction

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epicure24/Classifier-for-highly-unbalanced-data

This repo represents all the resampling techniques needed to achieve better results in highly unbalanced or skewed data that has 77 % of data in one class and rest in others.

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shivamkc01/Handling_Imbalanced_dataset

This project is about how you can deal with imbalanced data and which performance metrics' particularly important compared to usual practices with fairly balanced data.

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atif-hassan/Regression_ReSampling

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

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cviaai/IGS

Iterative gradient sampling

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ahmettalhabektas/Predicting-Device-Failure

Failure Prediction using Machine Learning (Undersampling situtation)

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newsteps8/Term-Deposit-Prediction

Unbalanced Customer Data

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ncdisrup-ai/CreditCardFraudDetection

Detect fraudulent credit card transactions through supervised machine learning

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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.

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Angienoelhaverly/Credit_Risk_Analysis

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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abhijha3011/Techniques-To-Handle-Imbalanced-Data

Different Techniques to Handle Imbalanced Data Set

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NatenaelTBekele/Credit-Card-Users-Churn-Prediction

Classification model that will help the bank improve its services so that customers do not renounce their credit cards

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MathCortes/Projeto5-Diabetes-ML_Classification

A base de dados que será estudada nesse projeto contém diversas informações de saúde de pacientes localizados no Hospital de Frankfurt, na Alemanha. Através dela podemos ver quais são os pacientes com e sem diabetes

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prernasingh05/Bank_Customer_Churn_Model

Churn modelling for bank customers using machine learning.

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NestorRV/undersampling

A Scala library for undersampling in imbalanced classification.

Language: Scala - Size: 9.63 MB - Last synced at: 5 months ago - Pushed at: almost 7 years ago - Stars: 16 - Forks: 0

NestorRV/undersampling_memory

undersampling: A Scala library for undersampling in imbalanced classification.

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a-memme/Credit_Risk_Analysis

Leveraging sampling techniques and classification algorithms to predict credit risk

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RaffelRavionaldo/customer-churn-detection-in-telecommunications-companies

Membuat model machine learning XGboost dan logistic regression untuk mendeteksi status dari pelanggan perusahaan telekomunikasi

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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.

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Maoelan/heart-disease-prediction

Heart Disease Prediction with Imbalanced Data Handling using Oversampling and Undersampling, and Deployment using Flask.

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jianninapinto/Bandersnatch Fork of BloomTech-Labs/BandersnatchStarter

This project implements a machine learning model using Random Forest, XGBoost, and Support Vector Machines algorithms with oversampling and undersampling techniques to handle imbalanced classes for classification tasks in the context of predicting the rarity of monsters.

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NestorRV/SOUL

SOUL: Scala Oversampling and Undersampling Library.

Language: Scala - Size: 8.52 MB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 10 - Forks: 2

jCodingStuff/NLPReddit

Multinomial classification tasks in Reddit

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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.

Language: Python - Size: 1.02 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 1 - Forks: 1

jayanttikmani/cross-sellingCaravanInsuranceUsingDataMining

Data Mining of Caravan Insurance Data Set Using R

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adrian-io/mortgage-default-prediction

The goal of this project is to perform default prediction for commercial real estate property loans based on 17 variables.

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shivtosh/Feature-engineering

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

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Krystkowiakk/Heart-Disease-Patients-Classification

Metis project 4/7

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eleveyuan/Imb_dat

some algorithm for imbalanced dataset

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AdityaBrahme98/Fraud_detection_ML

Use various machine learning models to see how accurate they are in detecting whether a transaction is a normal payment or a fraud.

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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).

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

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

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Rutgers-Data-Science-Bootcamp/Credit_Risk_Analysis

Data preparation, Statistical reasoning, Machine Learning

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BaileeRice/Credit_Risk_Analysis

using machine learning to assess credit risk

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Sayansurya/Project-on-Class-Imbalance-Problem

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alyssonvidal/Credit-Card-Limit-Classification

The project is a challenge for the DS community, where students divided into groups should develop a machine learning model capable of predicting whether the customer, according to their history, could have their request for an increase in the credit limit granted or denied.

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abidor13/linear_regression_salary

There are a number of classification algorithms that can be used to determine loan elgibility. Some algorithms run better than others. We built a loan approver using different Supervised Machine Learning algorithms and compared their accuracies and performances

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Rl16193/Credit_Risk_Analysis

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,

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bholeneha/Credit_Risk_Analysis

Credit card credit dataset analyzed using multiple machine learning models to determine which model best fits the data, reduces bias and predicts credit risk. Undersampling and oversampling done using various python libraries (imbalanced-learn and scikit-learn).

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gulabpatel/Handle_Imbalance

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Wamuza1/Credit_Risk_Analysis

Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

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

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

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annakthrnlee/Credit_Risk_Analysis

Using my skills in data preparation, statistical reasoning, and machine learning I employed different techniques to train and evaluate models with unbalanced classes.

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MFairbro1/Credit_Risk_Analysis

Using machine learning models to predict credit risk

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vishishtpriyadarshi/imbcobra

COBRA for Classification tasks on Imbalanced Data

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david-garza/Credit_Risk_Analysis

Supervised machine learning model to classify loan applicants into high and low risk categories

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

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

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ElsevierSoftwareX/SOFTX_2019_253 Fork of NestorRV/SOUL

SOUL: Scala Oversampling and Undersampling Library. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021000868

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An-Dongsun/Section2-Project

머신러닝 프로젝트 : 심장병 예측 모델 제작 및 해석

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cedoula/Credit_Risk_Analysis

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

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enj657/Credit_Risk_Analysis

Built and evaluated several machine learning algorithms to predict credit risk.

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existentialplantperson/Week_15

Week 15 - Support Vector Machines, Oversampling, and Undersampling

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Nexer8/Imbalanced_Data

Experiments with imbalanced data using undersampling and oversampling techniques.

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ZeroDarkHardy/Credit_Risk_Analysis

Train and test multiple Machine Learning models to predict risk based on consumer credit profiles.

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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.

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RomeroBarata/bimba

Sampling Algorithms for Two-Class Imbalanced Data Sets in R

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RileyCC56/Credit_Risk_Analysis

Creating a supervised machine learning model that could accurately predict credit risk using 6 different methods,

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Marcus-V-Freitas/Tratamento_de_Dados_Desbalanceados

Repositório com tratamento de dados utilizando as técnicas de UnderSampling e OverSampling.

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desininja/Employee-Attrition-analysis

To know the main reasons for attrition of employees.

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carolinacraus/Credit_Risk_Analysis

The purpose of this script is to predict credit risk by employing different techniques to train and evaluate models with unbalanced classes

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

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KaranSharma18/Credit-Card-Fraud-Detection

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

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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.

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YassirMatrane/DealingWithImbalancedData

This project aims to show you the different strategies to mitigate the imbalanced data issue by combining different approaches to resampling data (undersampling, oversampling, and hybrid sampling) and different machine learning algorithms and visualizing the results in order to choose the performest approaches. I highly recommend reading the ppt file to understand better and have an idea about the newest approaches.

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undersampling 95 oversampling 65 machine-learning 46 smote 28 logistic-regression 20 classification 16 imbalanced-data 16 python 14 random-forest 14 data-science 12 xgboost 12 smote-sampling 11 machine-learning-algorithms 9 scikit-learn 9 fraud-detection 8 smoteenn 8 random-forest-classifier 6 decision-trees 6 supervised-learning 6 data-visualization 6 imbalanced-learning 6 pandas 6 adaboost 6 gradient-boosting 5 cluster-centroids 4 resampling 4 supervised-machine-learning 4 ensemble-machine-learning 4 data-preprocessing 4 gridsearchcv 4 credit-card-fraud 4 unbalanced-data 4 support-vector-machines 4 hyperparameter-tuning 4 jupyter-notebook 4 cross-validation 4 balanced-random-forest 4 ensemble-model 4 exploratory-data-analysis 4 python3 4 deep-learning 3 data-mining 3 sampling-methods 3 sklearn 3 classification-algorithm 3 svm 3 imbalanced-classification 3 feature-engineering 3 outlier-detection 3 randomoversampler 3 feature-selection 3 randomizedsearchcv 3 ensemble-learning 3 ensemble-classifier 3 pca 3 algorithm 2 xgboost-classifier 2 imblearn 2 credit-card 2 classification-model 2 pipeline 2 data-cleaning 2 edited-nearest-neighbors 2 xgboost-model 2 oversampling-technique 2 pca-analysis 2 crossvalidation 2 easy-ensemble-classifier 2 polycystic-ovary-syndrome 2 linear-regression 2 pcos 2 ensemble 2 support-vector-machine 2 machinelearning 2 naive-random-oversampler 2 tensorflow 2 scala 2 data-analysis 2 bagging 2 regression 2 class-imbalance 2 r 2 svm-model 2 preprocessing 2 scikitlearn-machine-learning 2 student-project 2 recall 2 fine-tuning 2 feature-importance 2 tsne 2 classification-models 2 numpy 2 decision-tree 2 matplotlib 2 insurance 2 eda 2 smotetomek 2 smote-oversampler 2 normalization 1 udacity-machine-learning-nanodegree 1