ArtStudies/GEMINI.md

Gemini Project Context: Academic Project Repository

Directory Overview

This directory is a curated collection of academic projects completed throughout your mathematics and data science studies. It serves as a portfolio of your work, showcasing projects from your Bachelor's (L3) and Master's (M1, M2) degrees. The projects are implemented in Python (using Jupyter Notebooks) and R (using R Markdown), with some SQL scripts included.

Key Files

This directory contains a variety of projects that demonstrate your skills in different areas of mathematics and data science. Here are some of the key files and the topics they cover:

• L3/Analyse Matricielle/TP1_Methode_de_Gauss.ipynb: A Jupyter notebook on numerical analysis, implementing the Gauss method for solving linear systems.
• M1/Statistical Learning/TP1_A_first_example.ipynb: An introduction to statistical learning with Python, covering linear and polynomial regression using scikit-learn.
• M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb: A machine learning project in Python, focused on data preparation and analysis for modeling.
• M1/General Linear Models/Projet/GLM Code - DANJOU & DUROUSSEAU.rmd: A project in R on Generalized Linear Models (GLMs), analyzing a dataset of bike rentals.
• M2/Linear Models/Biaised Models/Code_Lec3.Rmd: An R Markdown file exploring biased regression models like Lasso, Ridge, and ElasticNet.
• M2/SQL/scripts/DANJOU_Arthur.sql: A SQL script with exercises on database queries.
• pyproject.toml: This file lists the Python dependencies for the projects, including libraries like numpy, pandas, scikit-learn, matplotlib, seaborn, tensorflow, and keras.

Usage

This directory is a valuable resource for understanding your skills and experience in data analysis, statistical modeling, and machine learning. The projects can be reviewed to see your approach to problem-solving and your proficiency with various tools and technologies.

When working in this directory, you can ask me to:

• Explain a specific project or piece of code.
• Help you with a new project or assignment.
• Find specific information within the files.
• Generate summaries or reports based on the project content.