I'm passionate about coding and committed to turning my GitHub into a well-structured, valuable documentation hub.
I focus on creating clear, reusable, and insightful resources to help others learn and build effectively.
π¨βπ» Passionate about Data Science & AI
π§βπ Lifelong learner, always curious
βοΈ Obsessed with clean, well-structured documentation
- Data Scientist for smeg.mc (Internship)
- Data Scientist for Education Nationale (Freelance)
- Data Scientist for Orano (Freelance)
- Data Scientist for datacraft (Internship and freelance)
- Data Analyst for OMAJ (Internship)
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π SQL Fast Learner
A complete SQL course combining theory and hands-on practice in Jupyter Book. Covers core and advanced topics like SELECT, JOINs, GROUP BY, CTEs, window functions, CRUD operations, transactions, stored procedures, indexing, and data validation. Ideal for beginners, refreshers, and advanced learners aiming to strengthen their SQL skills for real-world data analysis. -
π SNCF Project
A Streamlit app that connects to the official SNCF API to monitor real-time train traffic on a specific commute route.
Displays upcoming departures, delays, and cancellations, and sends email alerts when disruptions occur β even those not shown in the station.
Designed for shared use among colleagues who take the same route daily, with an online deployment for easy access.
π€ NeaflFun Password An intelligent automation project in which an LLM is connected via Selenium to a dynamic password creation site. The aim: to generate a password that adapts in real time to the security rules that gradually appear on the screen.
π§ Analysis X: Trump and Squeezie on Twitter A project to analyse feelings and compare speeches based on scraping tweets. In it I explore the contrasts between political (Donald Trump) and entertainment (Squeezie) communication, applying NLP, text cleaning and visualisation techniques.
π Preparing for Scikit-learn advanced certification Exploration and advanced learning of the Scikit-learn library. This project involves an in-depth review of advanced concepts (model selection, pipelines, tuning, etc.) to aim for sklearn's most demanding supervised machine learning certification.
Domain | Technologies |
---|---|
Data Science & ML | |
Web & UI | |
Scraping & Automation | |
Databases | |
π CybersΓ©curitΓ© | |
Environments |