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.

Comparing gradient and Newton boosting

Sistema web para el uso de modelos de riesgo de defunción y severidad hospitalaria asociada a diferentes afecciones médicas en pacientes hospitalizados.

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.

Researching how old classical machine learning – gradient boosting – and new modern deep learning algos cope with tabular data

🤡Курсовой проект СпбПУ по дисциплине "Обработка и анализ данных"

scikit-learn compatible tools to work with GBM models

Automatic short-term covid-19 spread prediction by countries and Russian regions

LightGBM + Optuna: Auto train LightGBM directly from CSV files, Auto tune them using Optuna, Auto serve best model using FastAPI. Inspired by Abhishek Thakur's AutoXGB.

This provides a python based response generator for the Gamma-ray Burst Monitor (GBM). Additionally, a 3ML plugin is provided which allows for the simultaneous fitting of GRB locations and spectra.

Routines to handle GBM geometry and plotting

Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method

A Python package which implements several boosting algorithms with different combinations of base learners, optimization algorithms, and loss functions.

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

A full pipeline AutoML tool for tabular data

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.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

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.