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jupyter-notebook

The project is an interactive, living book dedicated to reconstructing Archimedes’ Measurement of a Circle (Circuli Dimensio) using the original geometric proportions. By employing SageMath for symbolic validation, alongside Python, Julia and Perl for high-precision numerical calculations using libraries for decimal and arbitrary-precision arithmetic, the project bypasses modern algebraic transformations and demonstrates the accuracy of ancient geometric concepts.

Beyond Archimedes' original derivations, the project bridges the gap to modern approaches for calculating π. It demonstrates how Archimedes' four determination equations can be translated into modern notation like a super-radical, a product formula and a summation formula. To achieve this, the four equations were systematically reduced to a single unified equation. This step opens the door to further developments and serves as the link between geometry and algebra.

Using a specialized mathematical gain function, the system creates a dynamic bridge between ancient geometry and modern infinite series such as those of Ramanujan and Chudnovsky or the π formula of Saha and Sinha. Particular emphasis is placed on this new π formula, as it is more than it appears at first glance. By precisely handling the λ parameter the optimized approach achieves absolute numerical stability in getting hundreds or more of error-free decimal places. It is demonstrated, among other things, that the maximum gain in decimal places is achieved when the chosen value of λ equals the maximum number of iterations.

A key highlight is that the derived concept based on Archimedes allows the fundamental re-examination of every existing approach. E.g. the equations found on Wikipedia have been consolidated into a single equation as well as the BPS algorithm can also be reduced to a single equation. Furthermore, the work of Snellius and Dörrie can also be incorporated into the geometry of Archimedes.

And much more!

Aston University DG1AID lab repository with AI and data science notes, Python notebooks, NumPy, Pandas, search algorithms and machine learning practice.

A production-grade machine learning system for classifying celestial objects — stars, galaxies, and quasars — from 500,000 photometric observations sourced from the Sloan Digital Sky Survey (SDSS DR18). The pipeline covers LOF-based outlier detection, SMOTEENN class balancing, and SelectKBest feature selection, with six classifier configurations benchmarked against each other. Random Forest achieved the highest accuracy at ~99.51%, while LightGBM was selected for deployment due to its faster inference, smaller footprint, and clean ONNX export path. The system is fully containerized with Docker, backed by a CI/CD pipeline, and served live via a Gradio interface on Hugging Face Spaces.

A data mining project analyzing hate crime patterns in the United States from 2017 to 2025, using clustering, predictive modeling, and association rule mining.

End-to-end data cleaning of healthcare provider claims for fraud detection — includes referential checks, outlier removal, feature engineering & integrity validation.

📊 End-to-end marketing data analysis project using Python and Jupyter Notebook. 🔍 Focused on customer demographics, spending behavior, and campaign performance. 📈 Includes data cleaning, EDA, visualization, and actionable business insights.

My notebook images based on Jupyter Docker Stacks, with useful libraries and extensions preloaded

Turn fenced code blocks into interactive Jupyter cells inside your Jekyll site.

A introductory course created to teach students basic processes for Data Analysis and Data Science.

Book Recommendation System is a flask based mini project which uses collaborative filtering to recommend related books to the user based on author's rating. Book recommendation app – Live: https://book-recommendation-system-je8a.onrender.com/

This repository provides a few demonstration notebooks for the IEOTO (https://ieo.to) service to showcase the functionality that is available via the service API. The API, like the service web UI allows to search simultaneously for remote sensing and in-situ data, download the data. There is also functionality to define data streams (networks) and store own data and perform hosted processing.

This project focuses on extracting and visualizing stock data using Python libraries such as yfinance for historical stock prices and web scraping techniques to gather company revenue data. It provides a comprehensive analysis by plotting both stock prices and revenues over time for companies like Tesla and GameStop.

This project predicts house prices using machine learning models based on the King County House Sales dataset. It explores Simple Linear, Multiple Linear, Polynomial, and Ridge Regression models, comparing their performance in terms of accuracy. The best model identified is Polynomial Regression, achieving an R² score of 0.75.

A very simple set of scripts in order to test multiple LLM models through Jan.ai

Project to evaluate the results and performance of the clustering and rescheduling algorithms for the Social Internet of Energy. Related scientific manuscripts: https://ieeexplore.ieee.org/abstract/document/8784194 https://www.mdpi.com/1424-8220/20/13/3704

Project to evaluate the results and performance of the clustering and rescheduling algorithms for the Social Internet of Energy. Related scientific manuscripts: https://ieeexplore.ieee.org/abstract/document/8784194 https://www.mdpi.com/1424-8220/20/13/3704

A pre-commit hook used to clean ipywidgets in jupyter-notebooks that is primarily written by ChatGPT AI.