Merge branch 'master' into feature_es

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

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+---
+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.