Emeraldugbeyide93/Undersampling-and-Oversampling-techniques-for-imbalanced-datasets

Beginner friendly project focusing on dataset imbalances using the oversampling and under sampling techniques

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Dihan07/Undersampling-Comparision

Comparision of different undersampling techniques on a credit card dataset

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SherineTarek224/Credit_Fraud_Detection

This Project focuses on building a Fraud Detection using highly unbalanced Dataset from Kaggle of 170,884 and 305 frauds only using different machine learning models. Using Logistic Regression ,RandomForest and Neural Networks with preprocessing on Training Dataset and hyperparameters Tuning

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coderjolly/credit-risk-modelling

The aim of the project is to create a robust machine learning model that predicts the likelihood for a bank's customers to fail on their credit payments for the next month. The dataset used contains information on 24028 customers across 26 variables that includes information regarding whether customer defaulted, credit limits, bill history etc.

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Jesly-Joji/Money-Laundering-Classification

Money Laundering Classification on IBM Transactions

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Demon-2-Angel/Cereberal-Stroke-Analysis

Cerebral stroke, a critical condition, demands vigilant analysis. Machine learning models, coupled with resampling techniques like SMOTEENN, enhance stroke prediction accuracy by addressing imbalanced datasets.

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mmsaki/credit-risks-ml

Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.

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Ezgigunbatar/ToxicityDetection

Toxicity detection on imbalanced social media data. Focused on 2 main topics of toxicity detection: Class imbalance problem and detecting toxic comments.

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RAVI-CHANDRIKA-05/PREDICTING_PROBABILITY_OF_PAYING_BLIGHT_TICKETS

This repository contains files on Predict probability whether a given blight ticket will be paid

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vicaaa12/advanced-machine-learning

Advanced Machine Learning

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ClJuRo/Classification-of-Good-Loans-and-Bad-Loans-using-ML-algorithms

Predictive Modeling of Credit Risk Faced by a P2P lending platform

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joanitolopo/eval-sampling-methods

🔍 Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset.

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AI-14/lumpy-skin-disease-classification

Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.

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gabrielecola/Imbalance_classification_problem

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loopiiu/DSP2_Endterm

Expresso Churn Prediction Challenge - dealing with imbalanced dataset

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TheThugnomist/BankFraud

Building a machine learning model to check bank frauds

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vargovema/ccf_classification

Credit card fraud detection

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lamferzon/Heart-disease-analysis

Statistical analysis in R of a heart disease dataset by using logistic regression and random forest.

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Godson199/Ecological-Footprint-Predictor

In this project, I worked on a classification problem using an imbalanced dataset which predicts ecological footprints. The aim of the project was not necessarily to build a classification model but to investigate the different methods of correcting an imbalanced dataset in order not to build a biased classifier

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nafisa-samia/Credit-Card-Fraud-Detection-Dealing-with-Imbalanced-Data

This project is based on supervised machine learning where you will be predicting whether a credit card transaction is original transaction or fraud transaction based on various parameters. This is a classification problem.

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ramtiin/Credit-Card-Fraud-Detection

In this project, I explore different methods for detecting credit card fraud transactions; including using the Catboost algorithm with undersampling & oversampling methods, and using an almost new approach, by using deep learning and autoencoder.

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Soumyapro/Credit-Card-Fraud-Detection

The project was intended to detect fraudulent transactions from a highly imbalanced dataset.To solve the imbalance dataset problem random undersampling techniques were used.

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kuntala-c/A-streamlit-app-to-predict-customer-purchase-using-ML-model Fork of nafisa-samia/A-streamlit-app-to-predict-customer-purchase-using-ML-model

A Machine Learning model that predicts the customer's possibility of purchase using historical data.

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COOLMudi/Data-Scientist-Spotify-Skip-Action-Prediction-

It's Technocolabs Software Data Scientist Internship Project (1st Dec 2021 - 15th Jan 2022). In this project the team was instructed to analysis big data of Spotify users and to perform Statistical and Exploratory Data Analysis and Model Development for Predicting Listener Behavior.

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Sophy8281/sms-spam-detection

Spam messages detection model

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vivekyadav07/Credit-Card-Fraud-Detection

ML Project ,XGboost .Logistic Regression as classification,Decision Tree & balancing technique Undersampling & SMOTE.

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aolayeye/Credit_Risk_Analysis

Predicting Credit Risk with undersampling, oversampling, and ensemble methods across various machine learning algorithms.

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disha2sinha/Reducing-Imbalanced-dataset-by-Under-sampling-approach-Consensus-Clustering

Under-sampling based consensus clustering is applied on the three best clustering algorithms found after applying several Clustering Algorithms like K-means, K-modes, K-prototypes , K-means++ and fuzzy K-means on the majority class data of the IMBALANCED colon dataset to produce a BALANCED dataset.

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