A curated list of gradient boosting research papers with implementations.

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

Wave App for H2O AutoML

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

Verteego Data Suite documentation

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

Benchmark of current ML automation frameworks

Python binding for the H2O HTTP server

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

Automatic Model Selector

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

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

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

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

Yet - Another - Auto - ML

Prometheus Scraper for and Coin Analysis of crypto from coinmarketcap

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

Automatically determine trends, correlations, and feature selections given dataset(s)

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

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.