diff --git a/content/projects/arthome.md b/content/projects/arthome.md index be55127..0ca7944 100644 --- a/content/projects/arthome.md +++ b/content/projects/arthome.md @@ -14,16 +14,15 @@ tags: icon: i-ph-house-duotone --- -[ArtHome](https://go.arthurdanjou.fr/arthome) is a customizable browser homepage that lets you organize all your favorite links in one place. +[**ArtHome**](https://go.arthurdanjou.fr/arthome) is a customizable browser homepage that lets you organize all your favorite links in one place. 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. -## πŸ› οΈ Built with +## πŸ› οΈ Technology Stack -- [Nuxt](https://nuxt.com): An open-source framework for building performant, full-stack web applications with Vue. -- [NuxtHub](https://hub.nuxt.com): A Cloudflare-powered platform to deploy and scale Nuxt apps globally with minimal latency and full-stack capabilities. -- [NuxtUI](https://ui.nuxt.com): A sleek and flexible component library that helps create beautiful, responsive UIs for Nuxt applications. -- [ESLint](https://eslint.org): A linter that identifies and fixes problems in your JavaScript/TypeScript code. -- [Drizzle ORM](https://orm.drizzle.team/): A lightweight, type-safe ORM built for TypeScript, designed for simplicity and performance. -- [Zod](https://zod.dev/): A TypeScript-first schema declaration and validation library with full static type inference. -- and a lot of ❀️ +- **[Nuxt](https://nuxt.com)**: An open-source framework for building performant, full-stack web applications with Vue. +- **[NuxtHub](https://hub.nuxt.com)**: A Cloudflare-powered platform to deploy and scale Nuxt apps globally with minimal latency and full-stack capabilities. +- **[NuxtUI](https://ui.nuxt.com)**: A sleek and flexible component library that helps create beautiful, responsive UIs for Nuxt applications. +- **[ESLint](https://eslint.org)**: A linter that identifies and fixes problems in your JavaScript/TypeScript code. +- **[Drizzle ORM](https://orm.drizzle.team/)**: A lightweight, type-safe ORM built for TypeScript, designed for simplicity and performance. +- **[Zod](https://zod.dev/)**: A TypeScript-first schema declaration and validation library with full static type inference. diff --git a/content/projects/artlab.md b/content/projects/artlab.md index 0dcd672..1917316 100644 --- a/content/projects/artlab.md +++ b/content/projects/artlab.md @@ -18,8 +18,7 @@ icon: i-ph-flask-duotone [**ArtLab**](https://go.arthurdanjou.fr/status) is my personal homelab, where I experiment with self-hosting and automation. -My homelab is a self-hosted environment where I deploy, test, and maintain personal services. Everything is securely exposed **only through a private VPN** using [Tailscale](https://tailscale.com/), ensuring encrypted, access-controlled connections across all devices. -For selected services, I also use **Cloudflare Tunnels** to enable secure external access without opening ports or exposing my public IP. +My homelab is a self-hosted environment where I deploy, test, and maintain personal services. Everything is securely exposed **only through a private VPN** using [Tailscale](https://tailscale.com/), ensuring encrypted, access-controlled connections across all devices. For selected services, I also use **Cloudflare Tunnels** to enable secure external access without opening ports or exposing my public IP. ## πŸ› οΈ Running Services @@ -36,7 +35,7 @@ For selected services, I also use **Cloudflare Tunnels** to enable secure extern - **Beszel**: Self-hosted, lightweight alternative to Notion for notes and knowledge management. - **Palmr**: Personal logging and journaling tool. -## πŸ–₯️ Hardware +## πŸ–₯️ Hardware Specifications - **Beelink EQR6**: AMD Ryzen mini PC, main server host. - **TP-Link 5-port Switch**: Network connectivity for all devices. diff --git a/content/projects/artstudies.md b/content/projects/artstudies.md index bbd54c6..6ee10d9 100644 --- a/content/projects/artstudies.md +++ b/content/projects/artstudies.md @@ -15,11 +15,9 @@ tags: icon: i-ph-book-duotone --- -# ArtStudies +[**ArtStudies Projects**](https://github.com/ArthurDanjou/artstudies) 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. -[ArtStudies Projects](https://github.com/ArthurDanjou/artstudies) 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. - -The projects are organized into two main sections: +The projects are organized into three main sections: - **L3** – Third year of the Bachelor's degree in Mathematics - **M1** – First year of the Master's degree in Mathematics - **M2** – Second year of the Master's degree in Mathematics @@ -55,20 +53,19 @@ The projects are organized into two main sections: ## πŸ› οΈ Technologies & Tools -- [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing. -- [R](https://www.r-project.org): A statistical computing environment, perfect for data analysis and visualization. -- [Jupyter](https://jupyter.org): Interactive notebooks combining code, results, and rich text for reproducible research. -- [Pandas](https://pandas.pydata.org): A data manipulation library providing data structures and operations for manipulating numerical tables and time series. -- [NumPy](https://numpy.org): Core package for numerical computing with support for large, multi-dimensional arrays and matrices. -- [SciPy](https://www.scipy.org): A library for advanced scientific computations including optimization, integration, and signal processing. -- [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. -- [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models. -- [Keras](https://keras.io): A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation. -- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python. -- [Plotly](https://plotly.com): An interactive graphing library for creating dynamic visualizations in Python and R. -- [Seaborn](https://seaborn.pydata.org): A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics. -- [RMarkdown](https://rmarkdown.rstudio.com): A dynamic tool for combining code, results, and narrative into high-quality documents and presentations. -- [FactoMineR](https://factominer.free.fr/): An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA). -- [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R. -- [RShiny](https://shiny.rstudio.com): A web application framework for building interactive web apps directly from R. -- and my 🧠. +- **[Python](https://www.python.org)**: A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing. +- **[R](https://www.r-project.org)**: A statistical computing environment, perfect for data analysis and visualization. +- **[Jupyter](https://jupyter.org)**: Interactive notebooks combining code, results, and rich text for reproducible research. +- **[Pandas](https://pandas.pydata.org)**: A data manipulation library providing data structures and operations for manipulating numerical tables and time series. +- **[NumPy](https://numpy.org)**: Core package for numerical computing with support for large, multi-dimensional arrays and matrices. +- **[SciPy](https://www.scipy.org)**: A library for advanced scientific computations including optimization, integration, and signal processing. +- **[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. +- **[TensorFlow](https://www.tensorflow.org)**: A comprehensive open-source framework for building and deploying machine learning and deep learning models. +- **[Keras](https://keras.io)**: A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation. +- **[Matplotlib](https://matplotlib.org)**: A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python. +- **[Plotly](https://plotly.com)**: An interactive graphing library for creating dynamic visualizations in Python and R. +- **[Seaborn](https://seaborn.pydata.org)**: A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics. +- **[RMarkdown](https://rmarkdown.rstudio.com)**: A dynamic tool for combining code, results, and narrative into high-quality documents and presentations. +- **[FactoMineR](https://factominer.free.fr/)**: An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA). +- **[ggplot2](https://ggplot2.tidyverse.org)**: A grammar-based graphics package for creating complex and elegant visualizations in R. +- **[RShiny](https://shiny.rstudio.com)**: A web application framework for building interactive web apps directly from R. diff --git a/content/projects/bikes-glm.md b/content/projects/bikes-glm.md index 99353fe..87fd84d 100644 --- a/content/projects/bikes-glm.md +++ b/content/projects/bikes-glm.md @@ -14,10 +14,6 @@ tags: icon: i-ph-bicycle-duotone --- -# Generalized Linear Models for Bikes Prediction - -## Overview - This project was completed as part of the **Generalized Linear Models** course at Paris-Dauphine PSL University. The objective was to develop and compare statistical models to predict the number of bicycle rentals in a bike-sharing system based on various environmental and temporal characteristics. ## πŸ“Š Project Objectives @@ -47,8 +43,7 @@ The analysis identified critical factors influencing bike-sharing demand: ## πŸ“š Resources -- **Code Repository**: [GLM Bikes Code](https://go.arthurdanjou.fr/glm-bikes-code) -- **Full Report**: See embedded PDF below +You can find the code here: [GLM Bikes Code](https://go.arthurdanjou.fr/glm-bikes-code) ## πŸ“„ Detailed Report diff --git a/content/projects/breast-cancer.md b/content/projects/breast-cancer.md index 1cc97c2..5171fff 100644 --- a/content/projects/breast-cancer.md +++ b/content/projects/breast-cancer.md @@ -14,33 +14,36 @@ tags: icon: i-ph-heart-half-duotone --- -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. +This project was carried out as part of the **Statistical Learning** course at Paris-Dauphine PSL University. The 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. -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. +## πŸ“Š Project Objectives + +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. The dataset contains 116 observations divided into two classes: - -- 1: healthy individuals (controls) - -- 2: patients diagnosed with breast cancer +- **1**: healthy individuals (controls) +- **2**: patients diagnosed with breast cancer There are 9 explanatory variables, including clinical measurements such as age, insulin levels, leptin, insulin resistance, among others. +## πŸ” Methodology + The project follows a comparative approach between several algorithms: - Logistic Regression - - k-Nearest Neighbors (k-NN) - - Naive Bayes - - Artificial Neural Network (MLP with a 16-8-1 architecture) 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). This project illustrates a concrete application of data science techniques to a public health issue, while implementing a rigorous methodology for supervised modeling. +## πŸ“š Resources + You can find the code here: [Breast Cancer Detection](https://go.arthurdanjou.fr/breast-cancer-detection-code) +## πŸ“„ Detailed Report + diff --git a/content/projects/data-visualisation.md b/content/projects/data-visualisation.md index 5298404..7e1a4fa 100644 --- a/content/projects/data-visualisation.md +++ b/content/projects/data-visualisation.md @@ -14,16 +14,14 @@ tags: 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. ## πŸ› οΈ 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. +- **[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. \ No newline at end of file diff --git a/content/projects/loan-ml.md b/content/projects/loan-ml.md index 8ff0f12..761dc39 100644 --- a/content/projects/loan-ml.md +++ b/content/projects/loan-ml.md @@ -15,10 +15,6 @@ tags: icon: i-ph-money-wavy-duotone --- -# Machine Learning for Loan Prediction - -## Overview - This project focuses on building machine learning models to predict loan approval outcomes and assess default risk. The objective is to develop robust classification models that can effectively identify creditworthy applicants. ## πŸ“Š Project Objectives @@ -38,16 +34,6 @@ The study employs various machine learning approaches: - **Hyperparameter Tuning** - Optimizing model performance - **Cross-validation** - Ensuring robust generalization -## πŸ“ Key Findings - -[To be completed with your findings] - -## πŸ“š Resources - -- **Code Repository**: [Add link to your code] -- **Dataset**: [Add dataset information] -- **Full Report**: See embedded PDF below - ## πŸ“„ Detailed Report diff --git a/content/projects/schelling-segregation-model.md b/content/projects/schelling-segregation-model.md index bcbbe3c..4c8d27c 100644 --- a/content/projects/schelling-segregation-model.md +++ b/content/projects/schelling-segregation-model.md @@ -15,9 +15,13 @@ tags: icon: i-ph-city-duotone --- -This is the French version of the report for the Schelling Segregation Model project. The project was done as part of the course `Projet NumΓ©rique` 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. +This report presents the Schelling Segregation Model project completed as part of the **Projet NumΓ©rique** course at Paris-Saclay University. The goal was to implement the Schelling Segregation Model in Python and analyze the results using statistics and data visualization. + +## πŸ“š Resources You can find the code here: [Schelling Segregation Model Code](https://go.arthurdanjou.fr/schelling-code) +## πŸ“„ Detailed Report + diff --git a/content/projects/sevetys.md b/content/projects/sevetys.md index 53df8f0..7f0a037 100644 --- a/content/projects/sevetys.md +++ b/content/projects/sevetys.md @@ -15,17 +15,19 @@ tags: icon: i-ph-dog-duotone --- -[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. +[**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. 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. +## 🎯 Internship Objectives + During my two-month internship as a Data Engineer, I focused primarily on cleaning and standardizing customer and patient data β€” 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. 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. -## βš™οΈ Stack +## βš™οΈ Technology Stack -- [Microsoft Azure Cloud](https://azure.microsoft.com/) -- [PySpark](https://spark.apache.org/docs/latest/api/python/) -- [Python](https://www.python.org/) -- [GitLab]() +- **[Microsoft Azure Cloud](https://azure.microsoft.com/)**: Cloud infrastructure platform +- **[PySpark](https://spark.apache.org/docs/latest/api/python/)**: Distributed data processing framework +- **[Python](https://www.python.org/)**: Primary programming language +- **[GitLab](https://gitlab.com)**: Version control and CI/CD platform