As a Master's student in Economics, I am passionate about using data to solve complex economic and social challenges. I enjoy exploring methods such as machine learning and statistics to provide practical solutions.
Let’s build amazing together ! 🎉
I am Aurel VEHI, an Economics student at the Université Côte d'Azur. My background in economic data expertise and analysis has provided me with a strong foundation in economics, complemented by skills in programming, data analysis, and machine learning. This knowledge has allowed me to carry out innovative projects and solve complex problems. Through my experiences in finance and teaching, I have strengthened my adaptability, teamwork, and problem-solving abilities.
Driven by ambition and a passion for economic and social sciences, I aim to contribute to impactful research and propose solutions that are both equitable and sustainable. I am always eager to collaborate, learn, and offer innovative perspectives to those around me.
Here are some of my projects and case studies that demonstrate my skills and experience.
📅 Sept, 2024
Python
Excel
Power BI
The objective of this work is to measure the importance given to environmental issues in Preferential Trade Agreements (PTAs) using the Environmental Attention Index (EAI). This indicator is calculated from data provided by the Trade & Environment Database (TREND), the most comprehensive database to date on environmental clauses in international trade agreements.
📅 Jan, 2024
Python
Machine Learning
This project applies machine learning techniques to predict house prices based on a dataset containing various features related to housing and area demographics. The goal is to build a predictive model using linear regression and evaluate its performance in terms of how well it can estimate house prices.
📅 Oct, 2024
Python
SQL
Ever wondered how you can extract and analyze TV series data straight from the web? This project is all about scraping TV series data from AlloCiné, organizing it into a structured format, and conducting cool analyses! We scraped data like the series title, period, duration, genre, ratings, and more to create a comprehensive dataset.
📅 Dec, 2023
R
Factor Analysis
This activity focuses on applying Factorial Analysis of Mixed Data (FAMD) to the "adult" dataset from the UCI Machine Learning Repository. FAMD is a dimension reduction technique that allows for the simultaneous analysis of both quantitative and qualitative variables.
📅 April, 2024
Eviews
R
The study examines the time series of Belgian foreign trade to understand historical trends, assess the relationships between variables, and provide forecasts over a period of 10 quarters. The report is divided into two main parts: a univariate time series analysis to explore individual characteristics and a multivariate analysis to examine the interactions between the different series.
📅 Dec, 2023
R
This analysis focuses on analyzing economic indicators, specifically GDP and unemployment rates, using data from Eurostat. The primary goal is to understand the relationships between GDP growth and unemployment rates through the lens of Okun's Law.
📅 Nov, 2023
Python
This Python project involves an econometric analysis of inflation and unemployment rates for five European countries (France, Germany, Italy, Netherlands, United Kingdom) for the year 2023, with projections for 2024.
Here’s an overview of my technical and analytical skills, developed through my studies and projects, that allow me to transform data into actionable solutions.
Experiences
Oct - Dec 2024 • Contract
Monaco
Mar 2023 - Jun 2024 • Contract
Nice, France
Jul - Aug 2023 • Intern
Nice, France
Python
R Language
MySQL
Power BI
Jupyter Notebook
Microsoft Office
HTML
CSS
Julia
Ghitub Codespace
Git
Feel free to contact me for any questions or collaborations.
Nice, France.
