sachinML/Churn-Prediction-Web-Application-using-Deep-Learning

End to End Deep learning based Project to predict whether the customer will churn or not.

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antorguez95/synthetic_data_generation_framework

This repository contains the code of our published work in IEEE JBHI. Our main objective was to demonstrate the feasibility of the use of synthetic data to effectively train Machine Learning algorithms, prooving that it benefits classification performance most of the times.

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GenTaylor/Traffic-Accident-Analysis

Traffic Accident Analysis using python machine learning

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daehan-lim/associative-classifier-mortality-prediction

An interpretable associative classifier for predicting patient mortality using Electronic Medical Records (EMRs). Designed to handle highly imbalanced healthcare datasets.

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miriamspsantos/dcai-ecai-tutorial-2024

A multi-view panorama of Data-Centric AI: Techniques, Tools, and Applications (ECAI Tutorial 2024)

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Sh-31/Credit-Card-Fraud-Detection

This repository contains the code for a Credit Card Fraud Detection project using a highly unbalanced Kaggle dataset of 284,807 transactions with only 492 frauds. To address the imbalance the project implements voting classifier and a neural network with focal loss in PyTorch, achieving an F1-score of 0.86 and PR_AUC of 0.85 for the positive class.

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MuafiraThasni/Credit-Card-Fraud-Prediction

Credit card fraud is a major concern in the financial industry nowadays. Analysing fraudulent transactions manually is unfeasible due to huge amounts of data and its complexity. However, given sufficiently informative features, one could expect it is possible to do using Machine Learning.

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marinafajardo/prevendo-nivel-satisfacao

Prevendo o Nível de Satisfação dos Clientes do Santander.

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marinafajardo/prevendo-customer-churn

Prevendo Customer Churn em Operadoras de Telecom

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leabrodyheine/ML-Kaggle-Cirrhosis-Data

This project showcases skills in machine learning, data preprocessing, and model evaluation using Python libraries such as scikit-learn, XGBoost, and Optuna. It involves implementing various machine learning models, handling imbalanced data, and employing imputation techniques to enhance model performance for predicting cirrhosis outcomes.

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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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micahwiesner67/Decision_Tree_Classifier_Heart_Disease

This is an end-to-end machine learning model in which I implement random-forest and decision tree classifiers to predict heart disease. I utilized cross-validation, and oversampling to deal with an imbalanced dataset.

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As00-00/Fraud_detection

Credit card fraud detection from european cardholders transactions

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paulinamoskwa/Pediatric-Pneumonia-Chest-X-Ray

Pediatric pneumonia image classification with (strongly) imbalanced data via Pytorch 🫁

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dhwabqryh/Data-Mining-I

Tugas praktikum Data Mining I

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power-TY/Imbalnce_Handling_PySpark

PySpark를 이용한 불균형 데이터 처리 알고리즘 구현

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yaxinhou/imFTP

imFTP: Deep Imbalance Learning via Fuzzy Transition and Prototypical Learning (imFTP, Information Sciences 2024)

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Tejas-Nakave/Fraud-Analytics-ML-

Fraud analytics for credit cards utilizes advanced algorithms and machine learning to monitor transaction patterns and detect suspicious activities. By analyzing real-time data, it identifies anomalies such as unusual spending behaviors, geographic inconsistencies, and high-risk transactions.

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ashishpatel26/datascienv

datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries

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abhishekdbihani/Home-Credit-Default-Risk-Recognition

The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

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ArmanDavoodi/CS-SBU-MachineLearning-BSc-2022 Fork of alisharifi2000/CS-SBU-MachineLearning-BSc-2022

Machine Learning Course of Computer Science Faculty of Shahid Beheshti University. Winter 2022

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SerkanGuldal/sentetik

Synthetic data generation package to balance imblanaced datasets

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emrecanduran/Marketing-Campaign-Response-Prediction

Develop a model to predict which retail customers will respond to a marketing campaign. Logistic Regression shows the best performance.

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nguyendoanhoang/Graduation-Thesis

Using Machine Learning in predicting customer churn from bank credit card services

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viniciusds2020/ml_balaceamento_allknn

Este repositório contém um código de Machine Learning que utiliza o algoritmo AllKNN do pacote imblearn para realizar o balanceamento de dados.

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shinho123/23.11.10-1st-Korean-Society-of-Industrial-Engineers

2023년 11월 대한산업공학회(UNIST) : 다중 역할 경험을 고려한 게임 유저 이탈 예측: 롤 게임을 중심으로, 1저자

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Davityak03/Credit-Card-Fraud-Detection

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Rahafzsh/CreditCardFraudDetection

An Artificial Neural Network (ANN) model detects whether a credit card is fraudulent or not. 

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mdh266/TextClassificationApp

Building and Deploying A Serverless Text Classification Web App

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aditya11ad/ML-course

Best for beginners | Well explained ML algorithms | organized Notebooks | Case Studies

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pradeepdev-1995/databalancer

Databalancer is the python library using in machine learning applications to balance the imbalanced text classification datasets before the model training.

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filipusarif/Imbalance-data-SVM-Python

Working with Imbalance Dataset for classification using SVM model

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

Using R Markdown for Data Analysis, Machine Learning

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foxcroftjn/PAKDD-Class-Ratio

Supplementary code for "Class ratio and its implications for reproducibility and performance in record linkage" presented at The Pacific-Asia Conference on Knowledge Discovery and Data Mining 2024.

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emrecanduran/Marketing-Campaign-Classification

In this repository, two tree-based models from different families-bagging(RF) and boosting(XGB)- are implemented using the CRISP-DM process to predict responses for a marketing campaign, incorporating Borderline-SMOTE to effectively tackle the imbalance in the dataset.

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ekellbuch/longtail_ensembles

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

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christopher-w-murphy/Class-Imbalance-in-WW-Polarization

Treating the measurement of the same-sign W polarization fraction as a class imbalance problem

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NaquibAlam/TheMisfits

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lavinomenezes/desafio_indicium

Projeto de classificação multiclasse de dados desbalanceados | Multiclass classification project for imbalanced data.

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gkoays/Oversample-Image-Data-with-Augmentations

Language: Python - Size: 165 KB - Last synced at: 5 months ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 0

pgrondein/scoring_model_financial_company

Scoring model for financial company - all files

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deBUGger404/Imbalanced-classification

Imbalanced Data Classification Repository - 📦🤖 Code for classifying products into categories using deep learning. Divided into dataset creation, model development, and transfer learning sections. Implements TensorFlow for efficient training, tackles imbalanced classes, and includes saved models and one-hot encoded labels.

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amy-panda/NBA_Career_Prediction

Predicting if a NBA rookie player will last at least 5 years in the league

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zjukg/AdaMF-MAT

[Paper][LREC-COLING 2024] Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion

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fsarshad/Covid19XRaysHw2

Covid-19 X-Rays Deep Learning

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rishisinghlive/Fraud-Detection

Transection Fraud Detection

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GeorgeM2000/Vehicle-Insurance-Fraud-Detection-and-Credit-Risk-Assessment

This project endeavors to synthesize the challenges posed by varying misclassification costs and class imbalances, along with the corresponding solutions available for addressing these issues.

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MylieMudaliyar/Credit-Card-Fraud-Detection

Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.

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LaurentVeyssier/Starbucks_case_study_Udacity_Data_Science

Case study from UDACITY Data Scientist Nanodegree

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gadiankit/Pseudo_Code

Pseudo Code or parts of code to speed up the process of data exploration and model building with little modifications.

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vyshaal/circleup-mltask

Exploratory Data Analysis & Data Modeling

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Samir-Zade/Feature-Engineering-and-Exploratory-Data-Analysis

This repository contains resources and code examples related to Feature Engineering and Exploratory Data Analysis (EDA) techniques in the field of data science and machine learning.

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KhoiDOO/ibla 📦

IBLA - Imbalance Learning Archive

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ireneban/imbalanced_classification_python

study repo for imbalanced data classification

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igarleni/Imbalanced-Data-analysis-in-R---KAGGLE-competition

Imbalanced data Analysis in R combining different technyques.

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igarleni/Imbalanced-Data-analysis-with-R---First-steps

Learning how to analyze imbalanced Data, implementing SMOTE and using unbalanced R package

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kaiquefreire05/imbalanced-data-attribute-selection

- Notebook with my studies on how to handle unbalanced data and attribute selection.

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abdoghareeb46/NTI-Final-Assignment

NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

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WiktorPieklik/BaggFold

Official implementation of Bagging Folds using Synthetic Majority Oversampling for Imbalance Classification

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abhirup-ghosh/credit-risk-modelling

LightGBM credit default predictor + AWS deployment (EC2 + ECR)

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mohammad95labbaf/Outlier-Imbalanced-Fraud-Detection

The Credit Card Fraud Detection project uses statistical techniques and machine learning for identifying fraudulent transactions. It includes data preprocessing, outlier detection using Boxplots and Z-scores, and a decision tree model. Evaluation goes beyond accuracy, considering precision, recall, F1-score, and ROC AUC.

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phiyodr/multilabel-oversampling

Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). :sunflower:

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Divya-Bhargavi/wids_datathon_2019

Women in Data Science Competition

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Dhrumil-Zion/Sentiments-Prediction-Using-NLP

Predicting customer sentiments from feedbacks for amazon. While exploring NLP and its fundamentals, I have executed many data preprocessing techniques. In this repository, I have implemented a bag of words using CountVectorizer class from sklearn. I have trained this vector using the LogisticRegression algorithm which gives approx 93% accuracy. I have found out the top 20 positive and negative feedback words from thousands how feedbacks. Also after processing this much I have automated the whole process with one function so that it can be used as generic for many machine learning algorithms. I have also tested another algorithm called DummyClassifier which gives an accuracy of around 84%. After that, I have executed the famous algorithm which is TF-IDF for NLP. I have combined TF-IDF with LogisticRegression which gives almost 93% accuracy but deep insights. Also, while working with data has solved the problem of imbalanced data through RandomOverSampler class from imblearn library.

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DocentSzachista/CNN-tackle-imbalance

Deep learning course project

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Hands-On-Fraud-Analytics/Chapter-12-Data-Preparation-for-Fraud-Analytics

Chapter 12: Data Preparation for Fraud Analytics

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rezamosavi8740/Model-Selection-in-Credit-Card-Transaction-Analysis

Explore model selection in credit card transaction analysis with Reza Mousavi's Git project. Addressing class imbalance, it employs undersampling and features tree-based models, SVM, and logistic regression for effective fraud detection

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shreyasngredd/SentimentAnalysis-Bank-Reviews

Data scraping, data pre-processing, exploratory data analysis, sentiment analysis of Bank Online Reviews

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ashishrana1501/Feature-Engineering

This particular notebook consist of all the Feature Engineering technique and Feature Transformation technique

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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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AjNavneet/Customer_LiabilityToAsset_PredictiveAnalysis

Classifier for predicting customers who can be converted from liability to asset.

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huongnd12/credit-card-fraud-detection

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dipakexe/CREDIT_CARD_FRAUD_DETECTION

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atmacvit/bincrowd

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

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aaryadevg/Metro_PdM_Research

Failure prediction for APU’s on a Metro System Research project source code

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anudeepvanjavakam1/churn_prediction

A flask app to predict customer churn for a subscription service business

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rezamosavi8740/Detect-Tuberculosis-Chest-X-ray-Dataset

In the project, it is related to the identification of radiological photographs in which tuberculosis patients are diagnosed. Among the main challenges in this project are data imbalance and data preprocessing and implementing deep learning models to solve it.

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Giut0/NASA-NEO-Project

Final project for Data Mining course (Uniba)

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creativeDev6/twitter_sentiment_analysis_with_word2vec

Twitter sentiment analysis with word2vec.

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Hands-On-Fraud-Analytics/Chapter-11-Handling-Imbalanced-Data-Sets

Handling Imbalanced Data Sets

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ammahmoudi/Credit-Risk-Prediction

Predicting credit risk when a person requests for loan using random forest on south German dataset (fixing imbalanced data)

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alaminbhuyan/Handling-Imbalance-data-in-Machine-Learning

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seoruosa/ds-for-healthcare-final-project-autism

Repositório referente ao projeto final com o tema de diagnóstico de autismo da matéria de Ciência e Visualização de Dados em Saúde na UNICAMP desenvolvido por Gabriela Servidone, Felipe Labate e Thiago Giachetto de Araujo.

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baibai25/MNDO

Multivariate Normal Distribution based Oversampling

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anjalysam/Health-Insurance-Cross-Sell-Prediction

Predict Health Insurance Owners' who will be interested in Vehicle Insurance

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sharmaroshan/Fraud-Detection-in-Insurace-Claims

This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.

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chenxu93/imbalanced_flow

PCCN for imbalanced flow data classifcation

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30lm32/ml-imbalanced-car-booking-data

Create a ML model using Random Forest Classifier over skew (imbalanced) booking data

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Albertsr/Class-Imbalance

Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.

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FarhanaTeli/Kyphosis_Disease_Prediction_with_NN_XGBoost

Kyphosis disease prediction using simple Neural Network (NN) model and XGBoost model with GridSearchCV

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Soumyajit2709/Drug-Detection-using-Graph-Embedding

It is a simple machine learning algorithm to get the latent vector of the Molecules from the datasets. After that we address the imbalance problem in the dataset and handle it by using various resampling techniques. Then we measure the performance of the algorithm by deploying various Classifiers.

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cristinelpopescu/Resampling-strategies-for-imbalanced-datasets

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pankush9096/kaggle-Credit-Card-Fraud-Detection

This is an highly imbalanced data with only 1.72% minority and 98.28% majority class, i will be explaining Up and down sampling and effect of sampling before and while doing cross validation. Model has been evaluated using precision recall curve.

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Soumyajit2709/AN-APPROACH-TO-CLASSIFY-ASTRONOMICAL-OBJECT-USING-IMBALANCED-SLOAN-DIGITAL-SKY-SURVEY-DATA

Classify stars, galaxies, and quasars with SDSS DR16 data. Balanced dataset using resampling techniques improves AdaBoost classifier's performance, enhancing astronomical object classification accuracy.

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alecngo/cervical-cancer-project

Deploy SVM, Random Forest, and Streamlit Package to make a web app to early detect Cervical Cancer

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Swastik-25/Imbalanced-Data-with-SMOTE-Techniques

This repository contains implementation of some techniques like SMOTE, ADASYN, SMOTE + Tomek Links, SMOTE + ENN to overcome class imbalance in a binary classification problem.

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LuChang-CS/MTGAN

Code for the paper: Multi-Label Clinical Time-Series Generation via Conditional GAN

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xecyborg/fraud-transaction-detection

Fraud transaction detection using Machine Learning algorithms on highly imbalanced dataset

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arunsinghbabal/Automated-Defective-Substrate-Identification-for-Expedited-Manufacturing

Identifies the faulty wafer before it can be used for the fabrication of integrated circuits and, in photovoltaics, to manufacture solar cells. The project retrains itself after every prediction, making it more robust and generalized over time.

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bnafack/Turorial-machine-learning-

This is the tutorial I gave to the undergraduate student in Cameroon

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