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jupyter notebook

A collection of notebooks about maths, machine learning, and whatever...

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

A production-oriented Machine Learning pipeline that predicts whether an active user session will result in a purchase.

Model: XGBoost Classifier optimized for class imbalance.

Performance: ROC AUC 0.936 | F1-score 0.71 (at 0.30 threshold).

Key Features: Reproducible environment (uv), modular CLI for training/inference, leakage-free preprocessing, and SHAP interpretability analysis.

Data: UCI Online Shoppers Purchasing Intention Dataset.

Tech Stack: Python, XGBoost, Scikit-learn, Pandas, SHAP.

Leonardus is an open source project. It is a minimalist, stack-based programming system designed as a flexible framework for implementing and exploring algorithms. The syntax and semantics of its scripting language, LeoScript, are inspired by PostScript and Forth. It is extended with a prototype-based, object-oriented paradigm, has seamless integration into JupyterLab and Docker, and provides a concise and expressive environment for learning and experimentation.

The project name is a nod to Leonardus Pisanus, who was named and became known as Fibonacci.

Consult the project's GitLab Pages for documentation.

Lecture note of Numerical Analysis and Practice

A Python-based quantum simulation platform. Runs ProjectQ quantum algorithms in Docker for fast, portable, and consistent testing.

Training various machine learning models for NFLX stock price prediction with data collection, cleaning, and visualization tools.

big-test-pretrainedklaas-shuffle

This project explores the use of big data analytics and machine learning techniques to predict the likelihood of ICU admission among COVID-19 patients. It includes data cleansing, exploratory analysis, classification models, and clustering, implemented using Python (Pandas, Scikit-learn, Imbalanced-learn) and PySpark for distributed processing.

Team project for the Master's course Data Storage and Preparation at the BUT FIT. The aim of the second part of the project was to analyze the selected dataset and modify it into a form suitable for mining algorithms. The dataset chosen for the preparation was All countries details.

Project for the Master's course Statistics and Probability at the BUT FIT. Bayesian estimation and regression, statistical methods, prediction, approximation. Created using Jupyter notebook.

Project for the Master's course Statistics and Probability at the BUT FIT. The project analyses the running times of six different algorithm configurations. A total of 200 independent runs were produced for each configuration, the logs of which are available in the logfiles.zip file. The project is created in Python Jupyter notebook.

nbgrader_setup.py helps you create jupyter nbgrader courses locally from the information in canvas courses. canvas2nbgrader.py will fetch student submissions from Canvas and package them appropriately to your jupyter nbgrader project. After you have graded the student material nbgrader2canvas.py is used to upload grades and feedback to Canvas again.

This template provides a structured way to set up a Python project using Jupyter Notebooks on macOS. It follows best practices, including virtual environment management, dependency tracking, and VSCode integration.

Training project at DataQuest about historical numbers of helicopter assisted prison breaks in different countries.