A curated list of gradient boosting research papers with implementations.

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

NOTE: skutil is now deprecated. See its sister project: https://github.com/tgsmith61591/skoot. Original description: A set of scikit-learn and h2o extension classes (as well as caret classes for python). See more here: https://tgsmith61591.github.io/skutil

D2C(Data-driven Control Library) is a library for data-driven control based on reinforcement learning.

Python binding for the H2O HTTP server

Wave App for H2O AutoML

A machine learning approach to soil moisture estimation using NASA's CYGNSS data.

This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.

Benchmark of current ML automation frameworks

Verteego Data Suite documentation

This my entry for the Titanic competition on Kaggle. May 2019: public score is 0.80382, which is a top 10% ranking on the leader board of around 11.249 participants.

Yet - Another - Auto - ML

DayF (Decision at your Fingertips) is an AutoML opensource project

AutoML-Builder is an API that automates the generation of machine learning models for regression or classification tasks from provided datasets.

It's a notification for your daily water intake with amazing water factz..

Python program utilizing H2O to predict credit approval using the Naïve Bayes Classifier.

Automatic Model Selector

Prediction of Academic success using the automated machine learning tool : H2O AutoML

Prometheus Scraper for and Coin Analysis of crypto from coinmarketcap

Utilities to use google/quic-trace with `h2olog quic` logs