michaelwozniak

ML-in-Finance-I-case-study-forecasting-tax-avoidance-rates

trendecon

Create Long Daily Series from Google Trends

CatBoostLSS

An extension of CatBoost to probabilistic forecasting.

paper_ul3_wozniak_wrzesinski

reproducible_research_project

roulette_simulator

Roulette simulator in Julia

spatial_econometric_project

unsupervised-machine-learning-project-number-1

git2018

Website prepared for Girls do IT 2018 event

magapi-wrapper

Wrapper around Microsoft Academic Knowledge API to retrieve MAG data

ml1_project

ml2_tools

orbit

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

pythonsql_project

RegressionSteps

The three functions explained below work together to explore and present further actions and possibilities of linear regression models. My goal is to help users experiment with the explanatory variables of a linear regression model. As a result, these experiments will help the user reach conclusions about how the initial dependent variable will change, if one of the explanatory variables is changed as wished.

rit_preliminaries

RR_classes

Reproducible Research repo template

scrapy_ws

Stock-Prediction-Models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

web_scraping

Web scraping project

XGBoostLSS

An extension of XGBoost to probabilistic forecasting

zmod_dynamic_programming

Repozytorium z programem rozwiązyjącym dwukryterialne zagadnienie alokacji za pomocą programowania dynamicznego. Autorami są: Michał Woźniak oraz Michał Wrzesiński z WNE UW.