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.

Python package that predicts the appeal topic, topic group and the executor to process the appeal based on its contents

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

Machine Learning – HSE MMLDS 22/23

MLOps – HSE MMLDS 23/24 [python service for training and inferring Catboost classification model on Iris dataset]

Clustering employee performances to predict resignation likelihood and develop strategies for employee retention

A repository for almost every machine learning algorithms

R package for automation of machine learning, forecasting, model evaluation, and model interpretation

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

Behavior-Based Malware Detection using GBDTs

Amazon SageMaker Local Mode Examples

"Wind Power Predictor" is a machine learning project that forecasts turbine output using real-time data from Turkish wind farms. Its web app interface offers convenient access to predictions, enabling informed decisions for maximizing energy production and advancing renewable energy usage.

Forecasting Ethereum return quantiles using a handful of different statistical learning models and selecting the best based on out of sample error. Hopsworks feature store and model registry is used to automate the process. Ethereum quantile returns are predicted daily and displayed on a Streamlit dashboard.

creation of an interface and algorithm for forecasting demand for 14 days for goods of own production

HSE ML Project - Psychotypes analysis by Myers–Briggs Type Indicator

Playground for common python ml libraries

Traveling Ionospheric Disturbances Forecasting System, funded by the European Community, Horizon Europe

TSForecasting - Automated Time Series Forecasting Framework

Almost and AutoML around xgb, catboost and lightgbm

This code demonstrates the use of machine learning to model the multimodal nature of a single cell. Using machine learning to predict RNA from DNA, that is, using chromatin accessibility data to predict the RNA gene expression and to predict surface protein from RNA, that is, using RNA sequence data to predict surface protein levels in a cell