Imbalanced 2-class classification project for a predictive modeling competition (13th best ranked team out of a 104)

Compares ML models on identifying credit risk.

Use a Convolution Neural Network (CNN) for the detection of Mask Wearing.

Imbalanced classification with scikit-learn and PyTorch Lightning.

Codebase for "Learn Bayesian Logistic regression from imbalanced data" post.

Source code to reproduce the results of the cost-sensitive boosting for classification of imbalanced data

A python class for making machine learning algorithms cost sensitive.

ML classification on loan data to predict whether loan will default or not.

Winning a competition on imbalanced image classification.

Profanity detection using fastText.

Experiments with imbalanced data using undersampling and oversampling techniques.

To predict whether a given blight ticket will be paid on time

This project was completed as part of the CIT 650 "Intro To Big Data" course at Nile University.

The project focuses on tackling challenges such as imbalanced data and skewed features. Through exploratory data analysis,and model training.The use of innovative techniques like focal loss and controlled oversampling allows us to address the imbalanced nature of the data and achieve better model performance.

This is a multiclass classification project to classify severity of road accidents into three categories. this project is based on real-world data and dataset is also highly imbalanced.

Explore model selection in credit card transaction analysis with Reza Mousavi's Git project. Addressing class imbalance, it employs undersampling and features tree-based models, SVM, and logistic regression for effective fraud detection

A very interesting repo towards Alzheimer disease (Healthcare) contains 2 important Notebooks one with handling the imbalance data and other without significantly handling the imbalance.

Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). 🌻

Analytics Vidhya Hackathon to predict whether the policyholder will file a claim in the next 6 months or not.