Ajout du fichier Course1.xlsm et mise à jour de l'exécution des cellules dans le notebook TP_2 pour corriger le compteur d'exécution.

GEMINI.md

@@ -0,0 +1,28 @@
+# 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.**

M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb

@@ -1001,7 +1001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "8051b5f4",
    "metadata": {},
    "outputs": [