H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

This repository includes sample code for AutoML tools AutoGluon, AutoKeras, AutoSklearn, H2O, PyCaret, TPOT

An AI-powered application leveraging machine learning for detecting fraudulent activities in financial transactions with MLOps.

Benchmark for some usual automated machine learning, such as: AutoSklearn, MLJAR, H2O, TPOT and AutoGluon. All visualized via a Dash Web Application

Final project of the Data Analytics bootcamp. Ironhack Barcelona. December 2022

The Credit Card Approval Prediction system leverages regression models, AutoML, SHAP analysis, and advanced data visualization techniques. This comprehensive system enhances accuracy in predicting credit card approvals, showcasing a blend of sophisticated modeling, automation, and interpretability.

Various Jupyter Notebooks for a Kaggle Competition

Este proyecto "databases-trikis" es una aplicación que utiliza SQLAlchemy, Redis y H2O.

A directory of common utilities and use cases, implemented in Python

H2O3 example notebooks.

Wave App for H2O AutoML

This is an end-to-end network intrusion detection model with H2O AutoML, Mlflow, Streamlit, and FastAPI which can classify network activities as normal or anomalous

Files for compiling my presentation about H2O.ai.

Bank Customer Churn Prediction using ANN and H2O Auto ML models

Interpreting coefficients and results of the following models: 1. Logistic Regression 2. Random Forest 3. AutoML (H2O)

Our team (Versed Chimpanzee) came first among 340 people and 148 registered teams (119 teams did submission) in TUM Analytics Cup 2022 challenge sponsored by Siemens Advanta Consulting and organized by TUM Informatics Decision Sciences & Systems Department.

Computational experiments for the paper "A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost" (IJCNN 2021)

AutoML Libraries for training multiple ML models in one go with less code.

An End-to-End Implementation of AutoML with H2O, MLflow, FastAPI, and Streamlit for Insurance Cross-Sell

Code for the Master's thesis of Mads Rindal and Vegard Haneberg, TBA4925