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 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 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

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

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.

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

Routines to handle GBM geometry and plotting

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.

Comparing gradient and Newton boosting

Utilizing Fermi GBM as a pulsar timing tool

scikit-learn compatible tools to work with GBM models

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.

TQT 2nd GBM: Introducing TQT In House Competition!?; Pipelining Research and Specialist + MINI SEMINAR

An easy to use Snowflake-based text clustering or LLM, tool/framework

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

Geometric Brownian Motion simulator for Sensex or index price forecasting.

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

A machine learning-based model that predicts English Premier League standings using historical match data, player stats, and team performance metrics. The project leverages Random Forest for match outcome predictions and is deployed with Streamlit for interactive analysis.