data-imbalance

Real-time ML data drift detection system monitors model health using KS statistical testing and alerts when retraining is needed.

Shortcut to Nowhere: Demystifying Deep Spurious Regression

A machine learning solution for churn prediction using CatBoost, achieving a 0.8464 AUC-ROC through feature engineering and hyperparameter optimization.

Real-time fraud detection microservice built with XGBoost, FastAPI, and Docker, implementing an end-to-end machine learning pipeline from EDA to production deployment.

ECG Arrhythmia Detection with ResNet and Transfer Learning

[NeurIPS 2025] This repo is official PyTorch implementation of the paper "Learning Dense Hand Contact Estimation from Imbalanced Data".

Codes for paper titled "TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses"

dau is a Python package that implements Density-Aware Undersampling (DAU), a novel undersampling technique for handling imbalanced datasets.

Preprocessing the heart disease dataset: A practical guide to EDA, feature encoding, discretization, and handling class imbalance with SMOTE.

Data-driven approaches for identifying depression severity in social media posts.

Code for paper "Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation (ECCV 2024)"

This project detects failure of machine. It also detects the type of failure and gives instructions to machine operator in simple language using report.

This repository features a machine learning project utilizing the Pima Indians Diabetes Dataset to predict diabetes risk. It explores data preprocessing, model training, and evaluation using techniques such as Naive Bayes and K-Nearest Neighbors (KNN) . The aim is to highlight the impact of various health factors on diabetes prediction.

Exploration and optimization of a ML pipeline, delving into various techniques for enhancing different stages of ML workflows, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.

Demonstrate the application of machine learning on a real-world predictive maintenance dataset, using measurements from actual industrial equipment.

This notebook is a study of the application of sklearn Logistic Regression model and analysis of metric quality with a focus on the impact of imbalanced data. The problem presented is the analysis of sales of newspapers of a local stand in order to classify the probability of the newspaper being Sold Out or Not, given a set of features.

Membership Inference Attacks on Imbalanced Federated Learning setups

software vulnerability detection

Detection of dermoscopic structures for melanoma diagonsis

Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.