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.

A land cover classification task performed with machine learning modelling on Sentinel-2 data for Gibraltar.

Utilities for easy use of custom losses in CatBoost, LightGBM, XGBoost.

Training and deploying CatBoost Model with MLFlow, Flask, AWS ECS and github actions

[Competition] 2021 Big-Contest 정형 데이터 분석 분야 : 홍수 예방을 위한 댐 유입량 예측 모델 개발

В данном репозитории хранятся выполненные мною проекты, в рамках обучения на курсе Яндекс. Практикума "Специалист по Data Science"

A REST API that classifies resumes into occupation fields and seniority levels using machine learning. Trained on 3,000+ resumes across 26 occupations, the API provides accurate classifications with efficient PDF text extraction.

Health Profile Analysis:Revealing Disorder Paterns,Medication Guidance and Risk Classification-ML Project

This assignment contain information on the contributions to the campaigns of the US politicians at the state and the federal level. The contribution data has been collected from various sources and covers the 1989-2017 period

Artificial Intelligence and Cybersecurity

🏆1st solution in web ad CTR predict competition🏆

300 features selection, CatBoost, NannyML to monitor concept drift, auto-retrain, daily load h5 trick

Кейсы решенные на курсах Яндекс Практикума.

Playground for common python ml libraries

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

PUBG player data (4.5million+) processed using Pandas, NumPy in Python for preprocessing, and CatBoost for match predictions. Achieved RMSE 0.08, R² close to 1, optimizing gameplay metrics.

Build a Web App called AI-Powered Heart Disease Risk Assessment App

Учебные проекты из Яндекс Практикума "Специалист по Data Science+"