D

data science

https://app.prospect.energy/

A personal project to advance my career in data science as a whole.

SIMNL is een Python-tool die federatief consistente synthetische datasets genereert voor de drie belangrijkste Nederlandse overheidsregisters: BRP (personen), BAG (adressen) en HR (ondernemingen). De gegenereerde data is AVG-compliant, demografisch accuraat volgens CBS-cijfers, en bevat correcte kruisverwijzingen tussen registers — ideaal voor het testen van applicaties die met overheidsdata werken zonder echte persoonsgegevens te gebruiken. Met ondersteuning voor reproduceerbare output via seed-parameters en 30+ gevalideerde business rules levert SIMNL realistische testdata voor ontwikkel- en integratieomgevingen.

Recommend.Games blog: https://blog.recommend.games/

Bahn-Vorhersage - The best Train Delay Prediction System.

Python SDK for PaveDB’s /v1 API over HTTP, local embedded, or ephemeral mode, covering collections, ingestion, search, query replay, chunks, embedders, and archives.

Outflow is a framework that helps you create and execute sequential, parallel as well as distributed task workflows.

Mallard is a Python library for loading GTFS datasets into a DuckDB database.

Analysis of Kilter Board data, along with predictive models for V-grades based on holds and angle.

Analysis of Tension Board 2 data, along with predictive models for V-grades based on holds and angle.

Découverte de ce monde (Data / ML) via un projet perso. Basés sur des relevés météo de différentes sources, avec des outils comme Pandas, Dask, Spark, Polars, ..., du ML et du DL. Une couche de visualisation via PowerBI.

An introduction to coding and data analysis in Python and management & publication through git and dashboards.

Machine learning-based fraud detection system using Logistic Regression, Random Forest, and a Voting Classifier, achieving ~99% accuracy on a synthetic financial dataset.

Projet académique de Master 2 en Python portant sur l’analyse de la qualité de l’air en France et en Île-de-France. Le projet combine scraping de données, traitement de données environnementales, analyse statistique des concentrations de polluants atmosphériques et visualisation cartographique des indices de qualité de l’air. Les analyses portent notamment sur les polluants NO₂, O₃ et PM10, avec une étude temporelle des données Airparif de 2014 à 2017.

Mots-clés :

python data-science web-scraping data-analysis data-visualization air-quality environmental-data geospatial-analysis time-series pandas

A modular Clinical NLP Pipeline built to process and analyze unstructured medical text using both traditional machine learning and transformer-based approaches.

The project combines multiple components including OCR, text preprocessing, feature engineering, classification, named entity recognition, and visualization into a single end-to-end pipeline. It supports extracting clinical insights from raw documents and predicting medical categories using both TF-IDF + SVM and BERT-based models.

The system was designed and implemented as a structured Python project, with each stage separated into independent modules for scalability and maintainability.

Key Highlights

Built an end-to-end NLP pipeline for clinical text processing. Implemented SVM (≈51% accuracy) and BERT (≈77% accuracy) models. Integrated OCR for extracting text from medical documents. Performed Named Entity Recognition (NER) on clinical data. Designed modular architecture (src/) for clean code organization. Exported outputs for visualization and dashboard integration.

A LARA python-django app for managing projects and experiments in lab automation systems and scientific laboratories.

TheoryFlow backend's repository.

This repository holds code for the AskAnna Backend. Our backend stack primarily uses Django and the Django REST Framework.

Prédiction de la gravité des accidents de la route à partir des données BAAC (ONISR). Projet de data science combinant exploration, modélisation (LightGBM), API FastAPI et interface Streamlit.