Merge branch 'master' into feature_es

app/pages/portfolio/index.vue

@@ -27,7 +27,7 @@ const tags: Array<{ label: string, icon: string } & Tag> = [
     icon: 'i-ph-books-duotone',
     color: 'black',
   },
-  ...TAGS,
+  ...TAGS.filter(tag => tag.sort).sort((a, b) => a.label.localeCompare(b.label)),
 ]
 
 function updateTag(index: number) {
@@ -50,8 +50,7 @@ function updateTag(index: number) {
       icon="i-ph-warning-duotone"
       variant="outline"
     />
-    <UTabs :items="tags" class="hidden md:block" @change="updateTag" />
-    <UTabs :items="tags" orientation="vertical" class="md:hidden" @change="updateTag" />
+    <UTabs :items="tags" @change="updateTag" />
     <ul class="grid grid-cols-1 md:grid-cols-2 gap-8">
       <NuxtLink
         v-for="(writing, id) in writings"
@@ -80,16 +79,21 @@ function updateTag(index: number) {
             </h3>
           </article>
           <div class="flex gap-2 mt-4 flex-wrap">
-            <UBadge
-              v-for="tag in writing.tags"
-              :key="tag"
-              :color="TAGS.find(color => color.label.toLowerCase() === tag)?.color || 'black'"
-              variant="soft"
-              size="sm"
-              :ui="{ rounded: 'rounded-full' }"
-            >
-              {{ TAGS.find(color => color.label.toLowerCase() === tag)?.label }}
-            </UBadge>
+            <ClientOnly>
+              <UBadge
+                v-for="tag in writing.tags.sort((a: any, b: any) => a.localeCompare(b))"
+                :key="tag"
+                :color="TAGS.find(color => color.label.toLowerCase() === tag)?.color"
+                variant="soft"
+                size="sm"
+                :ui="{ rounded: 'rounded-full' }"
+              >
+                <div class="flex gap-1 items-center">
+                  <UIcon :name="TAGS.find(icon => icon.label.toLowerCase() === tag)?.icon" size="16" />
+                  <p>{{ TAGS.find(color => color.label.toLowerCase() === tag)?.label }}</p>
+                </div>
+              </UBadge>
+            </ClientOnly>
           </div>
         </li>
       </NuxtLink>

content/portfolio/monte-carlo-project.md

@@ -0,0 +1,26 @@
+---
+slug: monte-carlo-project
+title: Monte Carlo Methods Project
+description: ⚠️ Still in progress - A project to demonstrate the use of Monte Carlo methods in R.
+publishedAt: 2024/11/24
+readingTime: 3
+tags:
+  - project
+  - r
+  - maths
+---
+
+This is the report for the Monte Carlo Methods Project. The project was done as part of the course `Monte Carlo Methods` at the Paris-Dauphine University. The goal was to implement different methods and algorithms using Monte Carlo methods in R.
+
+Methods and algorithms implemented: 
+- Plotting graphs of functions
+- Inverse c.d.f. Random Variation simulation
+- Accept-Reject Random Variation simulation
+- Random Variable simulation with stratification
+- Cumulative density function
+- Empirical Quantile Function
+
+You can find the code here: [Monte Carlo Project Code](https://github.com/ArthurDanjou/Studies/blob/0c83e7e381344675e113c43b6f8d32e88a5c00a7/M1/Monte%20Carlo%20Methods/Project%201/003_rapport_DANJOU_DUROUSSEAU.rmd)
+
+<iframe src="/portfolio/monte-carlo-project/Report.pdf" width="100%" height="1000px">
+</iframe>

content/portfolio/python-data-ml.md

@@ -0,0 +1,32 @@
+---
+slug: python-data-ml
+title: Python Data & ML
+description: 🧠 A repository dedicated to learning and practicing Python libraries for machine learning. 
+publishedAt: 2024/11/01
+readingTime: 1
+tags:
+  - project
+  - data
+  - ml
+  - python
+  - r
+---
+
+[Python Data & ML](https://github.com/ArthurDanjou/Python-Data-Machine-Learning) is a repository dedicated to learning and practicing Python libraries for machine learning. It includes a variety of projects and exercises that cover the following topics.
+This project explores tools like NumPy, Pandas, scikit-learn, and others to understand and master key machine learning concepts. Perfect for strengthening skills in data processing, modeling, and algorithm optimization. 
+
+The goal is to improve my level of understanding of machine learning and data science concepts, as well as to practice Python programming and using libraries like NumPy, Pandas, scikit-learn, etc., to manipulate and analyze data, during my free time.
+
+## Tech Stack
+
+- [Python](https://www.python.org/)
+- [NumPy](https://numpy.org/)
+- [Pandas](https://pandas.pydata.org/)
+- [scikit-learn](https://scikit-learn.org/stable/)
+- [Matplotlib](https://matplotlib.org/)
+- [Seaborn](https://seaborn.pydata.org/)
+- [Jupyter Notebook](https://jupyter.org/)
+- [TensorFlow](https://www.tensorflow.org/)
+- [Keras](https://keras.io/)
+- [PyTorch](https://pytorch.org/)
+

content/portfolio/schelling-segregation-model.md

@@ -0,0 +1,18 @@
+---
+slug: schelling-segregation-model
+title: Schelling Segregation Model
+description: 📊 A Python implementation of the Schelling Segregation Model using Statistics and Data Visualization.
+publishedAt: 2024/05/03
+readingTime: 4
+tags:
+  - project
+  - python
+  - maths
+---
+
+This is the French version of the report for the Schelling Segregation Model project. The project was done as part of the course `Projet Numérique` at the Paris-Saclay University. The goal was to implement the Schelling Segregation Model in Python and analyze the results using statistics and data visualization.
+
+You can find the code here: [Schelling Segregation Model Code](https://github.com/ArthurDanjou/Studies/blob/e1164f89bd11fc59fa79d94aa51fac69b425d68b/L3/Projet%20Num%C3%A9rique/Segregation.ipynb)
+
+<iframe src="/portfolio/schelling/Projet.pdf" width="100%" height="1000px">
+</iframe>

content/portfolio/what-is-machine-learning.md

@@ -7,6 +7,7 @@ publishedAt: 2024/11/26
 tags:
   - article
   - ml
+  - maths
 ---
 
 ## Introduction
@@ -81,8 +82,8 @@ For regression problems, the **R² score** measures the proportion of the target
 
 $$R2 = 1 - \frac{\text{SS}_{\text{residual}}}{\text{SS}_{\text{total}}}$$ where:
 
-- $$\text{SS}\_{\text{residual}}$$ : Sum of squared residuals between actual and predicted values.
-- $$\text{SS}\_{\text{total}}$$ : Total sum of squares relative to the target’s mean.
+- $$\text{SS}_{\text{residual}}$$ : Sum of squared residuals between actual and predicted values.
+- $$\text{SS}_{\text{total}}$$ : Total sum of squares relative to the target’s mean.
 
 A $$R^2$$ close to 1 indicates a good fit.
 

types.ts

@@ -52,18 +52,21 @@ export interface Tag {
   label: string
   icon: string
   color: BadgeColor
+  sort?: boolean
 }
 
-export const TAGS = [
+export const TAGS: Array<Tag> = [
   {
     label: 'Article',
     icon: 'i-ph-pencil-line-duotone',
     color: 'red',
+    sort: true,
   },
   {
     label: 'Project',
     icon: 'i-ph-briefcase-duotone',
     color: 'blue',
+    sort: true,
   },
   {
     label: 'R',
@@ -90,4 +93,9 @@ export const TAGS = [
     icon: 'i-vscode-icons-file-type-python',
     color: 'amber',
   },
-].sort((a, b) => a.label.localeCompare(b.label))
+  {
+    label: 'Maths',
+    icon: 'i-ph-calculator-duotone',
+    color: 'pink',
+  },
+]