🔭 I’m currently working on perse an experimental, high level package to leverage best of three giants Pandas, Polars and Duckdb.

Here are some of my notable open-source projects:

• – A basic, reproducible Random Forest implementation in C with real dataset scripts, Makefile, and CI. Forked from dobroshynski/random-forests-c by Andrii Dobroshynski. Extended with real dataset scripts, Makefile, CI, and critical bug fixes by Sermet Pekin.

• – A minimalistic Neural Network C++ library inspired by Andrej Karpathy's micrograd python package.

• – C++ library to retrieve data from CBRT API.

• – A simple, header-only C++ library for loading environment variables from a .env file, similar to dotenv in other languages. This library allows you to set and retrieve environment variables in C++ projects without needing any external dependencies.

• – Runs a Python script or Jupyter notebook by automatically installing all required packages. Dependencies are installed into the current environment before execution. Jupyter notebooks can optionally be exported as HTML after running.

• – A minimalistic Neural Network python library extended from Andrej Karpathy's micrograd python package.

• – A high level framework to leverage best of three giants, Pandas, Polars and Duckdb

• – A tool to check if a package name is available on PyPI, complete with similarity checks and BigQuery integration.

• – An open-source RAG application for data aggregation with PyPI.

• – python package to retrieve data from CBRT API.

• – A utility package to backup folders with a .gitignore file as git does.

• – eseas is a seasonal adjustment Python package that acts as a wrapper for the jwsacruncher Java package. This tool allows users to process Demetra workspace XML files, create batch files, execute them, and collect the desired outputs into individual Excel files.

• – A utility package to extract tables from PDF files.

• – R package to retrieve economic data from APIs (currently only from CBRT and Fed).