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.

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

Shapley Values with H2O AutoML Example (ML Interpretability)

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

Wave App for H2O AutoML

Parallel Grid Search benchmark - H2O Machine Learning

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

Transformation of Akamai Logs with Spark ETL and discover of Values and similarities in logs used SparkML and H2O ML

In this project, we will identify the characteristics of women who are more likely to develop cervical cancer and use Automatic Machine Learning H2O AutoML to make prediction on whether or not a certain customer would buy a term deposit. We will also use Explainable AI (XAI) methods such as Variable Importance Plot, Partial Dependence Plot, SHAP…

Code & presentation for the 'H2O AutoML' short course at SDSS 2018 in Reston, VA

Use MLflow to make a pipeline of data preprocessing, machine learning, and predicting. Can do mlflow serving using docker in docker (dind).

My Final Submission for the 'Santander Customer Transaction Prediction'. I have participated in this very tough and interesting competition on Kaggle a while ago and I finally got the time to put all the work together in this Repo.

R package consisting of functions and tools to facilitate the use of traditional time series and machine learning models to generate forecasts on univariate or multvariate data. Different backtesting scenarios are available to identify the best performing models.

An investigation on the use of shapley explanations for unsupervised anomaly-detection models

Files for compiling my presentation about H2O.ai.

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

Using a stack of powerful tools to build an End-to-End AutoML pipeline for insurance cross-sell prediction

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.

Identify the characteristics of customers who more likely to respond and commit to a term deposit and use Automatic Machine Learning H2O AutoML to make prediction on whether or not a certain customer would buy a term deposit.