mirror of
https://github.com/ArthurDanjou/artchat.git
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feat: add new articles on AI agents and machine learning
- Created a new article on "Understanding AI Agents, LLMs, and RAG" detailing the synergy between AI agents, LLMs, and Retrieval-Augmented Generation. - Added an introductory article on "What is Machine Learning?" covering types, model selection, workflow, and evaluation metrics. chore: setup ESLint and Nuxt configuration - Added ESLint configuration for code quality. - Initialized Nuxt configuration with various modules and settings for the application. chore: initialize package.json and TypeScript configuration - Created package.json for dependency management and scripts. - Added TypeScript configuration for the project. feat: implement API endpoints for activity and stats - Developed API endpoint to fetch user activity from Lanyard. - Created a stats endpoint to retrieve Wakatime coding statistics with caching. feat: add various assets and images - Included multiple images and assets for articles and projects. - Added placeholder files to maintain directory structure. refactor: define types for chat, lanyard, time, and wakatime - Created TypeScript types for chat messages, Lanyard activities, time formatting, and Wakatime statistics.
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content/projects/arthome.md
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content/projects/arthome.md
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---
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slug: arthome
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title: 🏡 ArtHome
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description: Your personalized browser homepage
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publishedAt: 2024/09/04
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readingTime: 1
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cover: arthome/cover.png
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tags:
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- web
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---
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[ArtHome](https://home.arthurdanjou.fr) is a customizable browser homepage that lets you organize all your favorite links in one place.
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Create categories and tabs to group your shortcuts, personalize them with icons and colors, and make the page private if you want to keep your links just for yourself. The interface is clean, responsive, and works across all modern browsers.
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### 🛠️ Built with
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- [Nuxt](https://nuxt.com): An open-source framework for building performant, full-stack web applications with Vue.
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- [NuxtHub](https://hub.nuxt.com): A Cloudflare-powered platform to deploy and scale Nuxt apps globally with minimal latency and full-stack capabilities.
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- [NuxtUI](https://ui.nuxt.com): A sleek and flexible component library that helps create beautiful, responsive UIs for Nuxt applications.
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- [ESLint](https://eslint.org): A linter that identifies and fixes problems in your JavaScript/TypeScript code.
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- [Drizzle ORM](https://orm.drizzle.team/): A lightweight, type-safe ORM built for TypeScript, designed for simplicity and performance.
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- [Zod](https://zod.dev/): A TypeScript-first schema declaration and validation library with full static type inference.
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- and a lot of ❤️
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content/projects/artsite.md
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content/projects/artsite.md
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---
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slug: artsite
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title: 🌍 ArtSite
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description: My personal website, portfolio, and blog — all in one.
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publishedAt: 2024/06/01
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readingTime: 1
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cover: artsite/cover.png
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favorite: true
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tags:
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- web
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---
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[**ArtSite**](https://arthurdanjou.fr) is my personal space on the web — a portfolio, a blog, and a digital lab where I showcase my projects, write about topics I care about, and experiment with design and web technologies.
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It’s designed to be fast, accessible, and fully responsive. The site also serves as a playground to explore and test modern frontend tools.
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### ⚒️ Tech Stack
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- **UI** → [Vue.js](https://vuejs.org/): A progressive JavaScript framework for building interactive interfaces.
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- **Framework** → [Nuxt](https://nuxt.com/): A powerful full-stack framework built on Vue, perfect for modern web apps.
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- **Content System** → [Nuxt Content](https://content.nuxtjs.org/): File-based CMS to manage blog posts and pages using Markdown.
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- **Design System** → [Nuxt UI](https://nuxtui.com/): Fully styled, customizable UI components tailored for Nuxt.
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- **CMS & Editing** → [Nuxt Studio](https://nuxt.studio): Visual editing and content management integrated with Nuxt Content.
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- **Language** → [TypeScript](https://www.typescriptlang.org/): A statically typed superset of JavaScript.
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- **Styling** → [Sass](https://sass-lang.com/) & [Tailwind CSS](https://tailwindcss.com/): Utility-first CSS framework enhanced with SCSS flexibility.
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- **Deployment** → [NuxtHub](https://hub.nuxt.com/): Cloudflare-powered platform for fast, scalable Nuxt app deployment.
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- **Package Manager** → [pnpm](https://pnpm.io/): A fast, disk-efficient package manager for JavaScript/TypeScript projects.
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- **Linter** → [ESLint](https://eslint.org/): A tool for identifying and fixing problems in JavaScript/TypeScript code.
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- **ORM** → [Drizzle ORM](https://orm.drizzle.team/): A lightweight, type-safe ORM for TypeScript.
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- **Validation** → [Zod](https://zod.dev/): A TypeScript-first schema declaration and validation library with full static type inference.
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- **Deployment** → [NuxtHub](https://hub.nuxt.com/): A platform to deploy and scale Nuxt apps globally with minimal latency and full-stack capabilities.
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content/projects/bikes-glm.md
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content/projects/bikes-glm.md
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---
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slug: bikes-glm
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title: 🚲 Generalized Linear Models for Bikes prediction
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description: Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models.
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publishedAt: 2025/01/24
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readingTime: 1
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tags:
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- r
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- data
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- maths
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---
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The project was done as part of the course `Generalised Linear Model` at the Paris-Dauphine PSL University. The goal of the project is to determine the best model that predicts/explains the number of bicycle rentals, based on various characteristics of the day (temperature, humidity, wind speed, etc.).
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You can find the code here: [GLM Bikes Code](https://github.com/ArthurDanjou/Studies/blob/master/M1/General%20Linear%20Models/Projet/GLM%20Code%20-%20DANJOU%20%26%20DUROUSSEAU.rmd)
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<iframe src="/projects/bikes-glm/Report.pdf" width="100%" height="1000px">
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</iframe>
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content/projects/breast-cancer.md
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content/projects/breast-cancer.md
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---
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slug: breast-cancer
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title: 💉 Breast Cancer Detection
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description: Prediction of breast cancer presence by comparing several supervised classification models.
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publishedAt: 2025/06/06
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readingTime: 2
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tags:
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- python
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- data
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- maths
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---
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The project was carried out as part of the `Statistical Learning` course at Paris-Dauphine PSL University. Its objective is to identify the most effective model for predicting or explaining the presence of breast cancer based on a set of biological and clinical features.
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This project aims to develop and evaluate several supervised classification models to predict the presence of breast cancer based on biological features extracted from the Breast Cancer Coimbra dataset, provided by the UCI Machine Learning Repository.
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The dataset contains 116 observations divided into two classes:
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- 1: healthy individuals (controls)
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- 2: patients diagnosed with breast cancer
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There are 9 explanatory variables, including clinical measurements such as age, insulin levels, leptin, insulin resistance, among others.
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The project follows a comparative approach between several algorithms:
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- Logistic Regression
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- k-Nearest Neighbors (k-NN)
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- Naive Bayes
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- Artificial Neural Network (MLP with a 16-8-1 architecture)
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Model evaluation is primarily based on the F1-score, which is more suitable in a medical context where identifying positive cases is crucial. Particular attention was paid to stratified cross-validation and to handling class imbalance, notably through the use of class weights and regularization techniques (L2, early stopping).
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This project illustrates a concrete application of data science techniques to a public health issue, while implementing a rigorous methodology for supervised modeling.
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You can find the code here: [Breast Cancer Detection](https://github.com/ArthurDanjou/breast-cancer-detection)
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<iframe src="/projects/breast-cancer/report.pdf" width="100%" height="1000px">
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</iframe>
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content/projects/monte-carlo-project.md
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content/projects/monte-carlo-project.md
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---
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slug: monte-carlo-project
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title: 💻 Monte Carlo Methods Project
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description: A project to demonstrate the use of Monte Carlo methods in R.
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publishedAt: 2024/11/24
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readingTime: 3
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tags:
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- r
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- maths
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---
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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.
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Methods and algorithms implemented:
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- Plotting graphs of functions
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- Inverse c.d.f. Random Variation simulation
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- Accept-Reject Random Variation simulation
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- Random Variable simulation with stratification
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- Cumulative density function
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- Empirical Quantile Function
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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)
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<iframe src="/projects/monte-carlo-project/Report.pdf" width="100%" height="1000px">
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</iframe>
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content/projects/schelling-segregation-model.md
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content/projects/schelling-segregation-model.md
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---
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slug: schelling-segregation-model
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title: 📊 Schelling Segregation Model
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description: A Python implementation of the Schelling Segregation Model using Statistics and Data Visualization.
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publishedAt: 2024/05/03
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readingTime: 4
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tags:
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- python
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- maths
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---
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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.
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You can find the code here: [Schelling Segregation Model Code](https://github.com/ArthurDanjou/Studies/blob/e1164f89bd11fc59fa79d94aa51fac69b425d68b/L3/Projet%20Num%C3%A9rique/Segregation.ipynb)
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<iframe src="/projects/schelling/Projet.pdf" width="100%" height="1000px">
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</iframe>
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content/projects/sevetys.md
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content/projects/sevetys.md
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---
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slug: sevetys
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title: 🐶 Data Engineer Internship
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description: Summary of my internship as a Data Engineer at Sevetys
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publishedAt: 2025/07/31
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tags:
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- python
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- data
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favorite: false
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---
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[Sevetys](https://sevetys.fr) is a leading French network of over 200 veterinary clinics, employing more than 1,300 professionals. Founded in 2017, the group provides comprehensive veterinary care for companion animals, exotic pets, and livestock, with services ranging from preventive medicine and surgery to cardiology, dermatology, and 24/7 emergency care.
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Committed to digital innovation, Sevetys leverages centralized data systems to optimize clinic operations, improve patient data management, and enhance the overall client experience. This combination of medical excellence and operational efficiency supports veterinarians in delivering the highest quality care nationwide.
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During my two-month internship as a Data Engineer, I focused primarily on cleaning and standardizing customer and patient data — a critical task, as this data is extensively used by clinics, Marketing, and Performance teams. Ensuring data quality was therefore essential to the company’s operations.
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Additionally, I took charge of revising and enhancing an existing data quality report designed to evaluate the effectiveness of my cleaning processes. The report encompassed 47 detailed metrics assessing data completeness and consistency, providing valuable insights that helped maintain high standards across the organization.
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## ⚙️ Stack
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- [Microsoft Azure Cloud](https://azure.microsoft.com/)
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- [PySpark](https://spark.apache.org/docs/latest/api/python/)
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- [Python](https://www.python.org/)
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- [GitLab]()
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55
content/projects/studies.md
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content/projects/studies.md
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---
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slug: studies
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title: 🎓 Studies Projects
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description: A curated collection of mathematics and data science projects developed during my academic journey.
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publishedAt: 2023/09/01
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readingTime: 1
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favorite: true
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tags:
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- data
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- python
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- r
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---
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[Studies Projects](https://github.com/ArthurDanjou/studies) 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.
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The projects are organized into two main sections:
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- **L3** – Third year of the Bachelor's degree in Mathematics
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- **M1** – First year of the Master's degree in Mathematics
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## 📁 File Structure
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- `L3`
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- `Analyse Matricielle`
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- `Analyse Multidimensionnelle`
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- `Calculs Numériques`
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- `Équations Différentielles`
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- `Méthodes Numériques`
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- `Probabilités`
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- `Projet Numérique`
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- `Statistiques`
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- `M1`
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- `Data Analysis`
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- `General Linear Models`
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- `Monte Carlo Methods`
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- `Numerical Methods`
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- `Numerical Optimization`
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- `Portfolio Management`
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- `Statistical Learning`
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## 🛠️ Technologies & Tools
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- [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
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- [R](https://www.r-project.org): A statistical computing environment, perfect for data analysis and visualization.
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- [Jupyter](https://jupyter.org): Interactive notebooks combining code, results, and rich text for reproducible research.
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- [Pandas](https://pandas.pydata.org): A data manipulation library providing data structures and operations for manipulating numerical tables and time series.
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- [NumPy](https://numpy.org): Core package for numerical computing with support for large, multi-dimensional arrays and matrices.
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- [SciPy](https://www.scipy.org): A library for advanced scientific computations including optimization, integration, and signal processing.
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- [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.
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- [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models.
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- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
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- [RMarkdown](https://rmarkdown.rstudio.com): A dynamic tool for combining code, results, and narrative into high-quality documents and presentations.
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- [FactoMineR](https://factominer.free.fr/): An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA).
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- [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R.
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- and my 🧠.
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