S

seaborn

This project shows an example of how a collaborative collected FAIR dataset can be extended (using Merge Requests), curated (using automated testing), analyzed (using a Snakemake workflow), and visualized (using Python).

Este repositorio contiene los dos casos de estudio realizados como parte del Diplomado de Extensión en Data Science. En ambos casos, se utilizaron técnicas de programación en Python y Data Science para analizar conjuntos de datos y extraer información valiosa.

Creation of a trading robot for data analysis and manipulation of the libraries Pandas, Numby, MatplotLib, Yfinance, Seaborn

Code from mechanic physics labs (university course)

My attempts to solve homework from the Moscow Institute of Physics and Technology course

All left code from labs 1-3 years(university studing)

Collection of completed data-mining (university course) on python

Final project of course ML junior by Skillbox. Goal: Build model to predict client's loan default. Metrics: ROC-AUC

Финальная работа по курсу Введение в DS от Skillbox. Цель: анализ данных сервиса СберАвтоподписка, предсказание совершения целевого действия пользователя и подготовка модели к деплою в продакшн. Метрика: ROC-AUC