Imbalanced 2-class classification project for a predictive modeling competition (13th best ranked team out of a 104)
-
Updated
Feb 6, 2021 - Jupyter Notebook
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.
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.
Analytics Vidhya Hackathon to predict whether the policyholder will file a claim in the next 6 months or not.
Credit Risk Analyzer is a Python program which uses a machine learning algorithm to solve credit risk problem in a typical peer-to-peer lending financial industry.
Learning Imbalanced Datasets With Maximum Margin Losss
Multilabel classification to categorise messages received during a disaster
Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.
Build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate.
Classification models to predict patient length of stay, a multi-class categorical target with imbalanced predictor features. Optimized models include multi-class and voting ensembles.
Add a description, image, and links to the imbalanced-classification topic page so that developers can more easily learn about it.
To associate your repository with the imbalanced-classification topic, visit your repo's landing page and select "manage topics."