dennismgoetz/DAC

"Data Analytics Challenge" course at the Catholic University of Eichstätt-Ingolstadt

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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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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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abis330/footbalysis

Player Rating System in Soccer using Machine Learning

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manideep-d/classificationOfFraudTransactions

Classification of Fraudulent Transactions in Mobile Based Payments

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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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vicaaa12/advanced-machine-learning

Advanced Machine Learning

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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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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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khoaht312/py-bank

Technical Project - Python

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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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zaha2020/Machine_Learning

Machine Learning projects

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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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joshyaffee/healthcare_stroke_ML

Project for predicting strokes from healthcare data for INDE 577 (Spr. 23) at Rice University

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TheThugnomist/BankFraud

Building a machine learning model to check bank frauds

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jincy-p-janardhanan/imbalanced-fraud-detection

This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.

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yourssincerely/eurovision

This project provides a comprehensive analysis of the Eurovision Song Contest, with insights derived from both traditional statistical methods and machine learning techniques.

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shiranzada/pure-noise

Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810

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sprcoder/Customer_Segmentation_ML

A machine learning project to predict Customers/Clients into correct segment to provide promotional information or for product advertising.

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BibhuPrasadPanda97/Credit-Card-Default-Risk---AmExpert-CodeLab

Competition conducted by American Express on HackerEarth Platform to Predict Credit Card Defaulters by building Machine Learning Models for the given data.

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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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Arvindhh931/SMS-Spam-Classification

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alfianhid/Sentiment-Analysis-of-Mobile-App-Reviews-using-NBC-and-Text-Associations-Case-Study-PLN-Mobile-App

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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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Sophy8281/sms-spam-detection

Spam messages detection model

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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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