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

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

Data Mining of Caravan Insurance Data Set Using R

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

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

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

Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.

🎲 Iterable dataset resampling in PyTorch

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.

Identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. (Python, Logistic Regression Classifier, Unbalanced dataset).

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

Machine Learning Project on Imbalanced Data in R

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

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.

An audio project with the NEXYS 4 ddr

SOUL: Scala Oversampling and Undersampling Library.

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.

This repository build a deep learning framework to learn task-adaptive under-sampling masks and to reconstruct MR image jointly.

Predicts the absence or presence of arrhythmia and classifies them into 16 groups.