data-imbalance

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

compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance

pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss

The Mulan Framework with Multi-Label Resampling Algorithms

software vulnerability detection

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

Submission for HR Analytics Hackathon - AnalysticsVidya.

The final project for the CE888: Data Science and Decision Making module (Spring Term) at the University of Essex

Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.

This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.

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

Applied undersampling and oversampling using SMOTE.

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.

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.

Customer Retention Analysis : Predict customer churn

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

Predicting whether a client will subscribe for a term deposit after a bank marketing campaign

Detection of dermoscopic structures for melanoma diagonsis

Dice loss for data-imbalanced NLP tasks