SayamAlt/Stellar-Classification---Sloan-Digital-Sky-Survey-17

Successfully established a machine learning model which can predict an appropriate stellar class, on the basis of a distinct set of spectral characteristics, to a substantially high level of accuracy.

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Ryndine/ml_credit_risk_analysis

Test supervised machine learning models to predict credit risk.

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yangjenny/ImbalancedLearningRL

Code implementation for ImbalancedLearningRL, a reinforcement learning-based training method for imbalanced (outcome label) training data.

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shubh1608/Jupyter-Notebook-Template

Jupyter Notebook Templates for quick prototyping of machine learning solutions

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ethansilvas/credit-fraud-logistic-regression

Comparison of logistic regression models trained on imbalanced historical loan data to predict healthy and high-risk loans

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eliiza/imbalanced-data

Examples of algorithms dealing with imbalanced data.

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iamyuanchung/csdnn

Theano implementation of Cost-Sensitive Deep Neural Networks

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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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ashwinihegde28/Credit_Risk_Analysis

Credit_Risk_Analysis using Machine Learning

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massquantity/Class-Imbalance

《机器学习之类别不平衡问题》文章代码

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raj-1411/Adaptive-Mix-Up

Adaptive Mix-Up for Highly Imbalanced Medical Image Classification

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paobranco/Pre-processingApproachesImbalanceRegression

Pre-processing Approaches for Imbalanced Distributions in Regression

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minsoo9506/catchMinor-research

research about imbalanced learning & anomaly detection (tabular, time series)

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miriamspsantos/open-source-imbalance-overlap

A collection of Open Source Contributions in Learning from Imbalanced and Overlapped Data

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iamdanialkamali/ImbalancedLittleData

Trying to solve a imbalanced little data in text sentiment analysis

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simonschoe/imbalanced-learning

This workshop is part of the "Machine Learning in R" graduate course held at University of Münster, School of Business and Economics (winter term 2020/21). :mortar_board:

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nershman/imbalanced-learning

Comparison of different imbalanced classification methods with application to credit card fraud

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lancopku/well-classified-examples-are-underestimated

Code for the AAAI 2022 publication "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

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robinsonkwame/adversarial_labeller

Adversarial labeller is a sklearn compatible instance labelling tool for model selection under data drift.

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eric-blankinshp/Credit_Risk_Analysis_Supervised_ML

About 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. Topics

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mitchellfagert/Credit_Risk_Analysis

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

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ksommerdorf/SupervisedML

A case study utilizing supervised machine learning. (In order for the code to work, unzip the csv file in the Resources folder)

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ADMaple/Credit_Risk_Analysis

An analysis to employ different machine learning techniques to train and evaluate models with unbalanced classes.

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shayleaschreurs/Supervised_Learning_Regression_Model

Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con

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Zarich12/No_Show_Appointment_ML-Logistic-Regression-

Predicts if a patient will show up at a scheduled appointment based on certain features.

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lilyhanhub/Credit_Risk_Analysis

Supervised Machine Learning project to predict credit risk

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Davidelvis/BankClientTermDeposit_Prediction

Bank Institution Term Deposit Predictive Model

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bishopce16/credit_risk_analysis

An analysis to build and evaluate a dataset from LendingClub to discover credit risk using Supervised Machine Learning Algorithms.

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lrb924/Credit_Risk_Classification

Credit Risk Classification

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paobranco/SMOGN-LIDTA17

SMOGN: a Pre-processing Approach for Imbalanced Regression - LIDTA2017

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lnshewmo/Credit_Risk_Analysis

Using machine learning algorithms to predict credit risk

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munno88/Credit_Risk_Analysis

An analysis using Machine Learning algorithms to identify credit card risk using a dataset from LendingClub.

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SayamAlt/Water-Quality-Prediction

Successfully established a machine learning model which can predict whether any given water sample is potable or not, based on its set of various properties, to a considerably high level of accuracy.

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baibai25/MNDO-NC

Multivariate Normal Distribution Based Over-Sampling for Numerical and Categorical Features

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MFairbro1/Credit_Risk_Analysis

Using machine learning models to predict credit risk

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stephperillo/Credit_Risk_Analysis

Using machine learning to determine which model is best at predicting credit risk amongst random oversampling, SMOTE, ClusterCentroids, SMOTEENN, Balanced Random Forest, or Easy Ensemble Classifier (AdaBoost).

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Owerre/anomaly-detection

Anomaly detection using unsupervised, semi-supervised, and supervised machine learning methods

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kochlisGit/Predictive-Maintainance-Tanzania-Water-Pumps

In this project, I analyze, plot and clean Tanzania's Water Pump Dataset, which is provided by DrivenData.org for a competition.

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AJMnd/Credit_Risk_Analysis

An analysis on credit risk

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miriamspsantos/datagenerator Fork of sysmon37/datagenerator

A generator of multi-dimensional and multi-class imbalanced data, designed to create artificial datasets for the study of data difficulty factors in imbalanced learning.

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Malvi1497/Credit_Risk_Analysis

To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.

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gaetanoantonicchio/DataMining-2

Repository for "Data Mining - Advanced Topics and Applications" projects exam.

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jjtiger/Machine-Learning-Classifier-

This project contains an analysis evaluating data from an API and applying several machine learning models to predict credit risk

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TrentBrunson/Machine_Learned

And the machine learned with scikit, Python, Pandas, etc.

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Topopiccione/parSMURF-NG

A Machine Learning binary classifier for extremely imbalanced datasets

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Analitico-771/creditworthiness_classification_model

This is an Application that trains a model using supervised learning and imbalanced-learn library in order to classify and identify the creditworthiness of borrowers

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joecerniglia/Glass_ML2

Historical research using Machine Learning

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wattsr1/Credit-Risk-Analysis

Develop a model to predict credit risk based on existing credit application data

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amirhfarzaneh/ddaloss-pytorch

The PyTorch implementation of Discriminant Distribution-Agnostic loss (DDA loss)

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rodolfomp123/imb-mulan

The Mulan Framework with Multi-Label Resampling Algorithms

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RomeroBarata/bimba

Sampling Algorithms for Two-Class Imbalanced Data Sets in R

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rdev12/Product-Recommender

An e-commerce company wants to recommend products to its users. The company has collected only transaction data in the past. The training dataset has only 3 columns - user_id, Product bought and Order value of the product. Using this dataset, predict for all the users in the training dataset, the top 3 categories that the user might buy from.

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UribeAlejandro/Classification_Fraud_Detection

A Machine Learning model that is able to classify credit card transactions as either fraudulent or non-fraudulent. This project shows solutions when the dataset shows a strong imbalance.

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Baylex/Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

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mmkamani7/Targeted-Meta-Learning

In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.

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MLOPTPSU/Targeted-Meta-Learning

In this repository, we implement Targeted Meta-Learning (or Targeted Data-driven Regularization) architecture for training machine learning models with biased data.

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Internship-BVoc/InsuranceClaimPrediction

Predicting whether Insurance claims will accepted or rejected for online Travel Agencies

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Kowsi/Credit_Risk_Analysis-Machine_Learning

Evaluate several machine-learning models to predict credit risk using free imbalanced dataset from LendingClub

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amylio/Credit_Risk_Analysis

An analysis using Machine Learning algorithms to identify credit card risk using a dataset from LendingClub.

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rohithteja/K-Nearest-Neighbours

Experimental analysis of KNN by using waveform dataset

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LangeJustin/Data-Mining-Cup-2019

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JalajVora/Text-Analytics-with-Multi-Class-and-Imbalanced-Learning

Genre Identification task along with Text Analytics with Multi-Class and Imbalanced Learning on Gutenberg Corpus

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sahutkarsh/likelihood-ratio-imbalance-degree

A new metric to measure multi-class imbalance degree of data using likelihood ratio test based on the paper LRID by Rui Zhu et. al.

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rakibhhridoy/HandlingImbalancedDataset-Business

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase but by others illegally. Some huge transactions can also done by suspicious figure, it need to catch em.

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maitree7/Credit_Risk_Analysis_Classification

Accessing credit risk for peer to peer lending and comparing efficacy of various models while predicting risk

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tianyiwangnova/2020_project__Customer_Segmentation_and_Campaign_Response_Prediction

☕️Customer segmentation to identify the parts of the population that best describe the core customer base of the company; Predict which individuals are the most likely to respond to the company's mail campaigns.

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susiexia/Supervised_Machine_Learning

Supervised ML models

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Sabrout/highly_imbalanced_practice

We explore three algorithms and their combinations to tackle this problem of imbalance in the given dataset. Those algorithms are Gradient Boosting, SMOTE and Tomek Links. Each one is discussed in its own section.

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PierreGuilmin/impact-annotator_v2

Develop a knowledge-based approach using MSK-IMPACT data to build an automatic variant classifier

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lxy5513/ObjectDetectionImbalance Fork of kemaloksuz/ObjectDetectionImbalance

目标检测中的不平衡问题综述

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EvandroJRSilva/VDBC

This algorithm is part of a research on Multiclass Imbalanced Domains

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harisyammnv/MasterArbeit

Master Thesis

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clairempr/spooky-classify

Text classification with scikit-learn, used to make predictions for Kaggle Spooky Author Identification competition

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shreyasbhatia09/Google-Analytics-Customer-Revenue-Prediction

Kaggle Challenge

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joseangeldiazg/MII-SIGE

Repositorio para los proyectos de la asignatura Sistemas Inteligentes para la Gestión en la Empresa (Business Intilligence) del master profesional en ingeniería informática de la Universidad de Granada.

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chrinide/smrt Fork of tgsmith61591/smrt

Handle class imbalance intelligently by using variational auto-encoders to generate synthetic observations of your minority class.

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