feat: mettre à jour les projets avec des modifications de balisage et ajouter de nouveaux fichiers PDF

app/composables/projects.ts

@@ -3,7 +3,7 @@ export function useProjectColors() {
     'Active': 'blue',
     'Completed': 'green',
     'Archived': 'neutral',
-    'In Progress': 'amber'
+    'In progress': 'amber'
   }
 
   const typeColors: Record<string, string> = {

content/projects/artsite.md

@@ -11,7 +11,6 @@ tags:
   - Nuxt
   - NuxtHub
   - Cloudflare Workers
-  - Wrangler
   - TypeScript
 icon: i-ph-globe-hemisphere-west-duotone
 ---

content/projects/artstudies.md

@@ -11,7 +11,6 @@ tags:
   - Python
   - R
   - Data Science
-  - Machine Learning
   - Mathematics
 icon: i-ph-book-duotone
 ---

content/projects/bikes-glm.md

@@ -9,7 +9,6 @@ status: Completed
 tags:
   - R
   - Statistics
-  - Data Analysis
   - GLM
   - Mathematics
 icon: i-ph-bicycle-duotone
@@ -53,5 +52,5 @@ The analysis identified critical factors influencing bike-sharing demand:
 
 ## 📄 Detailed Report
 
-<iframe src="/projects/bikes-glm/Report.pdf" width="100%" height="1000px">
+<iframe src="/projects/bikes-glm.pdf" width="100%" height="1000px">
 </iframe>

content/projects/breast-cancer.md

@@ -9,7 +9,6 @@ status: Completed
 tags:
   - Python
   - Machine Learning
-  - Data Science
   - Classification
   - Healthcare
 icon: i-ph-heart-half-duotone
@@ -43,5 +42,5 @@ This project illustrates a concrete application of data science techniques to a
 
 You can find the code here: [Breast Cancer Detection](https://go.arthurdanjou.fr/breast-cancer-detection-code)
 
-<iframe src="/projects/breast-cancer/report.pdf" width="100%" height="1000px">
+<iframe src="/projects/breast-cancer.pdf" width="100%" height="1000px">
 </iframe>

content/projects/data-visualisation.md

@@ -0,0 +1,29 @@
+---
+slug: data-visualisation
+title: Data Visualisation Project
+type: Academic Project
+description: An interactive data visualization project built with R, R Shiny, and ggplot2 for creating dynamic, explorable visualizations.
+publishedAt: 2026-01-05
+readingTime: 1
+status: In progress
+tags:
+  - R
+  - R Shiny
+  - Data Visualization
+  - ggplot2
+icon: i-ph-chart-bar-duotone
+---
+
+# Data Visualisation Project
+
+This project involves creating an interactive data visualization application using R and R Shiny. The goal is to develop dynamic and explorable visualizations that allow users to interact with the data in meaningful ways.
+
+## 🛠️ Technologies & Tools
+
+- [R](https://www.r-project.org): A statistical computing environment, perfect for data analysis and visualization.
+- [R Shiny](https://shiny.rstudio.com): A web application framework for R that enables the creation of interactive web applications directly from R.
+- [ggplot2](https://ggplot2.tidyverse.org): A powerful R package for creating static and dynamic visualizations using the Grammar of Graphics.
+- [dplyr](https://dplyr.tidyverse.org): An R package for data manipulation, providing a consistent set of verbs to help you solve common data manipulation challenges.
+- [tidyr](https://tidyr.tidyverse.org): An R package for tidying data, making it easier to work with and visualize.
+
+The project is currently in progress, and more details will be added as development continues.

content/projects/dropout-reduces-underfitting.md

@@ -4,23 +4,16 @@ title: Dropout Reduces Underfitting
 type: Research Project
 description: TensorFlow/Keras implementation and reproduction of "Dropout Reduces Underfitting" (Liu et al., 2023). A comparative study of Early and Late Dropout strategies to optimize model convergence.
 publishedAt: 2024-12-10
-readingTime: 4
+readingTime: 6
 status: Completed
 tags:
   - Python
   - TensorFlow
-  - Machine Learning
   - Deep Learning
   - Research
 icon: i-ph-share-network-duotone
 ---
 
-📉 [Dropout Reduces Underfitting](https://github.com/arthurdanjou/dropoutreducesunderfitting): Reproduction & Analysis
-
-![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)
-![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)
-![License](https://img.shields.io/badge/License-MIT-green.svg)
-
 > **Study and reproduction of the paper:** Liu, Z., et al. (2023). *Dropout Reduces Underfitting*. arXiv:2303.01500.
 
 The paper is available at: [https://arxiv.org/abs/2303.01500](https://arxiv.org/abs/2303.01500)
@@ -79,7 +72,8 @@ pip install tensorflow numpy matplotlib seaborn scikit-learn
 ## 📊 Usage
 
 The main notebook pipeline.ipynb contains all necessary code. Here is how to run a typical experiment via the pipeline API.
-1. Initialization
+
+### 1. Initialization
 
 Choose your dataset (cifar10, fashion_mnist, mnist) and architecture (cnn, dense).
 ```python
@@ -89,7 +83,7 @@ from pipeline import ExperimentPipeline
 exp = ExperimentPipeline(dataset_name="fashion_mnist", model_type="cnn")
 ```
 
-2. Learning Curves Comparison
+### 2. Learning Curves Comparison
 
 Compare training dynamics (Loss & Accuracy) of the three strategies.
 
@@ -102,7 +96,7 @@ exp.compare_learning_curves(
 )
 ```
 
-3. Ablation Studies
+### 3. Ablation Studies
 
 Study the impact of the "Early" phase duration or Dropout intensity.
 
@@ -124,7 +118,7 @@ exp.compare_drop_rates(
 )
 ```
 
-4. Data Regimes (Data Scarcity)
+### 4. Data Regimes (Data Scarcity)
 
 Verify the paper's hypothesis that Early Dropout shines on large datasets (or limited models) while Standard Dropout protects small datasets.
 
@@ -155,3 +149,8 @@ According to the paper, you should observe:
 M.Sc. Statistical and Financial Engineering (ISF) - Data Science Track at Université Paris-Dauphine PSL
 
 Based on the work of Liu, Z., et al. (2023). Dropout Reduces Underfitting.
+
+## 📄 Detailed Report
+
+<iframe src="/projects/dropout-reduces-underfitting.pdf" width="100%" height="1000px">
+</iframe>

content/projects/loan-ml.md

@@ -9,9 +9,9 @@ status: Completed
 tags:
   - Python
   - Machine Learning
-  - Classification
-  - Data Science
+  - Regression
   - Finance
+  - Data Science
 icon: i-ph-money-wavy-duotone
 ---
 
@@ -50,5 +50,5 @@ The study employs various machine learning approaches:
 
 ## 📄 Detailed Report
 
-<iframe src="/projects/loan-ml/Report.pdf" width="100%" height="1000px">
+<iframe src="/projects/loan-ml.pdf" width="100%" height="1000px">
 </iframe>

content/projects/monte-carlo-project.md

@@ -12,6 +12,7 @@ tags:
   - Statistics
   - Monte Carlo
   - Numerical Methods
+  - Estimation
 icon: i-ph-dice-five-duotone
 ---
 
@@ -27,5 +28,5 @@ Methods and algorithms implemented:
 
 You can find the code here: [Monte Carlo Project Code](https://go.arthurdanjou.fr/monte-carlo-code)
 
-<iframe src="/projects/monte-carlo-project/Report.pdf" width="100%" height="1000px">
+<iframe src="/projects/monte-carlo.pdf" width="100%" height="1000px">
 </iframe>

content/projects/schelling-segregation-model.md

@@ -19,5 +19,5 @@ This is the French version of the report for the Schelling Segregation Model pro
 
 You can find the code here: [Schelling Segregation Model Code](https://go.arthurdanjou.fr/schelling-code)
 
-<iframe src="/projects/schelling/Projet.pdf" width="100%" height="1000px">
+<iframe src="/projects/schelling.pdf" width="100%" height="1000px">
 </iframe>