A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

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.

use the data set and run the ipynb file

Detect Credit Card Fraud with Machine Learning in R

🌳 Stacked Gradient Boosting Machines

Lightweight screen streaming server

glmark2 is an OpenGL 2.0 and ES 2.0 benchmark

Library used to assists in building C based applications that require vulkan renderers.

Project to produce supervised ML algorithm to predict which customers are likely to leave and produce .Rmd report

Sentiment140 dataset with 1.6 million tweets

HIV-1 Envelope Sequence Resistance Predictor to 33 Broadly Neutralizing Antibodies

Train Gradient Boosting models that are both high-performance *and* Fair!

Pricing and Analysis of Financial Derivative by Credit Suisse using Monte Carlo, Geometric Brownian Motion, Heston Model, CIR model, estimating greeks such as delta, gamma etc, Local volatility model incorporated with variance reduction.(For MH4518 Project)

This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.

Twitter sentiment analysis is the process of analyzing tweets posted on the Twitter platform to determine the overall sentiment expressed within them. It involves using natural language processing (NLP) and machine learning techniques to classify tweets.

This project aims to use machine learning models on Kaggle data to predict corporate credit ratings to aid investment decisions.

A full pipeline AutoML tool for tabular data

Machine learning classification model with streamlit deployment.