State-of-the art Automated Machine Learning python library for Tabular Data

This repository contains an example of each of the Ensemble Learning methods: Stacking, Blending, and Voting. The examples for Stacking and Blending were made from scratch, the example for Voting was using the scikit-learn utility.

Learning with Subset Stacking

🏆데이콘 AI해커톤 대회 우수상 솔루션🏆

Identify the type of disease present on a Cassava Leaf image

A Stacking-Based Model for Non-Invasive Detection of Coronary Heart Disease

This streamlit app predicts the churn rate using Gradient Boosting models (XGBoost, Catboost, LightGBM) on IBM Customer Churn Dataset

This project is dedicated to accurately classify Alzheimer's disease into Demented, Non-demented and Converted Category.

Trabajos prácticos realizados en la materia Organización de Datos de la FIUBA.

A stacking framework for the Linking Writing Processes to Writing Quality Kaggle competition

Machine Learning Model to predict student graduation grade

A deep convolutional network made of stacked feature extractors

A powerful ensemble learning class that supports multi-layer stacking and blending models for regression tasks, with K-fold cross-validation and hold-out validation set options, for robust model performance.

final_project of sun_ai_courses

This repository contains my current model for the Housing Prices Kaggle competition.