Homogenize all project files to match base format

Co-authored-by: ArthurDanjou <29738535+ArthurDanjou@users.noreply.github.com>

content/projects/artstudies.md

@@ -15,11 +15,9 @@ tags:
 icon: i-ph-book-duotone
 ---
 
-# ArtStudies
+[**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.
 
-[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:
+The projects are organized into three 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
@@ -55,20 +53,19 @@ The projects are organized into two main sections:
 
 ## 🛠️ Technologies & Tools
 
-- [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
-- [R](https://www.r-project.org): A statistical computing environment, perfect for data analysis and visualization.
-- [Jupyter](https://jupyter.org): Interactive notebooks combining code, results, and rich text for reproducible research.
-- [Pandas](https://pandas.pydata.org): A data manipulation library providing data structures and operations for manipulating numerical tables and time series.
-- [NumPy](https://numpy.org): Core package for numerical computing with support for large, multi-dimensional arrays and matrices.
-- [SciPy](https://www.scipy.org): A library for advanced scientific computations including optimization, integration, and signal processing.
-- [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.
-- [Keras](https://keras.io): A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation.
-- [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.
-- [RShiny](https://shiny.rstudio.com): A web application framework for building interactive web apps directly from R.
-- and my 🧠.
+- **[Python](https://www.python.org)**: A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
+- **[R](https://www.r-project.org)**: A statistical computing environment, perfect for data analysis and visualization.
+- **[Jupyter](https://jupyter.org)**: Interactive notebooks combining code, results, and rich text for reproducible research.
+- **[Pandas](https://pandas.pydata.org)**: A data manipulation library providing data structures and operations for manipulating numerical tables and time series.
+- **[NumPy](https://numpy.org)**: Core package for numerical computing with support for large, multi-dimensional arrays and matrices.
+- **[SciPy](https://www.scipy.org)**: A library for advanced scientific computations including optimization, integration, and signal processing.
+- **[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.
+- **[Keras](https://keras.io)**: A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation.
+- **[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.
+- **[RShiny](https://shiny.rstudio.com)**: A web application framework for building interactive web apps directly from R.