From e498a3eee88b7798f5d4209adcca189ca55fa99a Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Mon, 29 Sep 2025 17:49:22 +0200 Subject: [PATCH] =?UTF-8?q?Mise=20=C3=A0=20jour=20du=20README.md=20pour=20?= =?UTF-8?q?renommer=20le=20projet=20en=20ArtStudies=20et=20ajouter=20la=20?= =?UTF-8?q?section=20M2=20avec=20des=20projets=20de=20Machine=20Learning?= =?UTF-8?q?=20et=20SQL.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 115f4b0..7eb13d4 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ -# Studies +# ArtStudies -[Studies Projects](https://github.com/ArthurDanjou/studies) is a curated collection of academic projects completed throughout my mathematics studies. The repository showcases work in both _Python_ and _R_, focusing on mathematical modeling, data analysis, and numerical methods. +[ArtStudies Projects](https://github.com/ArthurDanjou/artstudies) is a curated collection of academic projects completed throughout my mathematics studies. The repository showcases work in both _Python_ and _R_, focusing on mathematical modeling, data analysis, and numerical methods. The projects are organized into two main sections: - **L3** – Third year of the Bachelor's degree in Mathematics - **M1** – First year of the Master's degree in Mathematics +- **M2** – Second year of the Master's degree in Mathematics ## 📁 File Structure @@ -27,6 +28,10 @@ The projects are organized into two main sections: - `Portfolio Management` - `Statistical Learning` +- `M2` + - `Machine Learning` + - `SQL` + ## 🛠️ Technologies & Tools - [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing. @@ -38,6 +43,8 @@ The projects are organized into two main sections: - [Scikit-learn](https://scikit-learn.org): A robust library offering simple and efficient tools for machine learning and statistical modeling, including classification, regression, and clustering. - [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models. - [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python. +- [Plotly](https://plotly.com): An interactive graphing library for creating dynamic visualizations in Python and R. +- [Seaborn](https://seaborn.pydata.org): A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics. - [RMarkdown](https://rmarkdown.rstudio.com): A dynamic tool for combining code, results, and narrative into high-quality documents and presentations. - [FactoMineR](https://factominer.free.fr/): An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA). - [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R.