- Author: Manai Mortadha
This project aims to perform a comprehensive analysis of the differences in money spent on "Equity Crowdfunding" projects across OECD countries. The analysis will delve into various independent variables that may contribute to these differences, including economic factors, open-mindedness, social awareness, sense of security, and technology awareness.
- GDP Value: Gross Domestic Product value of each country.
- PPP (Purchasing Power Parity): Measure of economic strength considering cost of living and inflation.
- Number of Foreign Languages Spoken: Indicator of cultural diversity and open-mindedness.
- Electoral Turnout: Percentage of eligible voters participating in elections.
- Percentage of People Working in R&D Sectors: Reflects the focus on innovation and progress.
- European Patent Office Patents Gained (per Habitant): Patents filed for innovation.
- Number of Violations Reported by Residents: Indicator of governance and safety.
- Number of Purchases Made Online: Illustrates technology adoption and e-commerce.
- Percentage of People Working in IT: Demonstrates technology proficiency.
The project utilizes data from the Eurostat database, encompassing the latest available years (with the oldest data, specifically electoral turnout, extending to 2011).
The analysis unfolds gradually and employs a range of methods:
- Basic Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Random Forest Regression
- Logistic Regression
- Kernel Support Vector Machine (SVM)
- Apriori Algorithm (for market basket analysis)
- Clustering Techniques
The project's analysis is conducted using both Python and R programming languages to ensure a comprehensive examination.
The project solely focuses on "Equity Crowdfunding," which involves the issuance of securities. This form of crowdfunding is distinct due to its regulation in most countries. As such, it provides trustworthy and credible sources of information for analysis.
Project Name: Comparative Analysis of Equity Crowdfunding Expenditure Across OECD Nations: Python and R Approach