Analysis of the Environmental Attention Index (EAI) in Preferential Trade Agreements (PTAs)
📅 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.
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Classification Models for Family History of Overweight
📅 April, 2024
R
Machine Learning
This project explores the use of various classification models to predict whether an individual has a family history of overweight. Three different machine learning algorithms are implemented and evaluated: Logistic Regression, Random Forest Classifier, and XGBoost Classifier.
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AlloCiné TV Series Scraping Project
📅 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.
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Factorial Analysis of Mixed Data (FAMD)
📅 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.
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Analysis of Belgian Exports and Imports (1995-2023)
📅 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.
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Analysis of Economic Indicators: Okun's Law
📅 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.
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Phillips Curve Analysis and Policy Guidance for Europe 2024
📅 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.
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House Price Prediction
📅 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.
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Trends in Twitter Activity of French Deputies
📅 Nov, 2024
Python
Seaborn
NLP
This project analyzes tweets from French deputies to understand their dynamics and structure. It involves collecting and merging tweet and retweet data, enriching it with deputies names and political affiliations, cleaning the data to handle missing values, and creating temporal variables to facilitate chronological analysis. The goal is to explore the distribution of tweets by political party, identify recurring themes, and visualize trends over time, providing insight into how French deputies use Twitter.
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Banking Fraud Data Collection Project
📅 Aug, 2024
HTML
JavaScript
PHP
SQL
This project simulates fictive banking transactions and records users' responses related to their attitude toward risk and money. The goal is to collect data from users and store it in a MySQL database to analyze potential indicators of fraudulent behavior.
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Data Visualization - Allociné
📅 Nov, 2024
Python
Seaborn
Networkx
This project aims to analyze a dataset from Allociné, a cinematic database (movies & series), in order to visualize various key information and understand the relationships between different attributes of movies and series.
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