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#	content/portfolio/bikes-glm.md
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Arthur Danjou
2025-02-02 22:17:55 +01:00
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commit d70592b0c1
12 changed files with 23 additions and 24 deletions
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content/portfolio/artsite.md
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@@ -22,4 +22,4 @@ tags:
- **Langage** → [Typescript](https://www.typescriptlang.org/)
- **Deployment** → [NuxtHub](https://hub.nuxt.com/)
- **Styling** → [Sass](https://sass-lang.com/) & [Tailwind CSS](https://tailwindcss.com/)
- **Package Manager** → [pnpm](https://pnpm.io/)
- **Package Manager** → [pnpm](https://pnpm.io/)

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content/portfolio/monte-carlo-project.md
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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:
Methods and algorithms implemented:
- Plotting graphs of functions
- Inverse c.d.f. Random Variation simulation
- Accept-Reject Random Variation simulation
@@ -23,4 +23,4 @@ Methods and algorithms implemented:
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>
</iframe>

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content/portfolio/python-data-ml.md
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---
slug: python-data-ml
title: Python Data & ML
description: 🧠 A repository dedicated to learning and practicing Python libraries for machine learning.
description: 🧠 A repository dedicated to learning and practicing Python libraries for machine learning.
publishedAt: 2024/11/01
readingTime: 1
tags:
@@ -13,7 +13,7 @@ tags:
---
[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.
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.
@@ -29,4 +29,3 @@ The goal is to improve my level of understanding of machine learning and data sc
- [TensorFlow](https://www.tensorflow.org/)
- [Keras](https://keras.io/)
- [PyTorch](https://pytorch.org/)

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content/portfolio/schelling-segregation-model.md
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@@ -15,4 +15,4 @@ 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://github.com/ArthurDanjou/Studies/blob/e1164f89bd11fc59fa79d94aa51fac69b425d68b/L3/Projet%20Num%C3%A9rique/Segregation.ipynb)
<iframe src="/portfolio/schelling/Projet.pdf" width="100%" height="1000px">
</iframe>
</iframe>