Skip to content
#

imbalanced-learn

Here are 56 public repositories matching this topic...

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

  • Updated Sep 29, 2025
  • Python

Onco-Logic is a comprehensive, multi-modal decision support ecosystem designed to transform cancer care by unifying fragmented patient data. The suite leverages advanced AI and machine learning to provide clinicians and researchers with a holistic understanding of each patient's disease, enabling a new frontier in precision oncology.

  • Updated Oct 19, 2025
  • Python

Data visualization of the NYC restaurant data, and data analysis to gauge if a restaurant located in a high-income area receives a higher health inspection grade. Uses Python (Pandas, Scikit-learn, Imbalanced-learn), PostgreSQL, SQLAlchemy, Tableau, JavaScript (Plotly.js library), HTML, CSS, and Bootstrap.

  • Updated Oct 19, 2022
  • JavaScript

Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.

  • Updated Apr 1, 2021
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the imbalanced-learn topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the imbalanced-learn topic, visit your repo's landing page and select "manage topics."

Learn more