Data Science: Forecasting Policy Impacts on Economic Freedom

Overview

This repository contains code for the final project for the course "Data Science, Prediction, and Forecasting" at Aarhus University in Denmark, as part of the MSc degree in Cognitive Science. The project involves the Economic Freedom Index Summary (EFIS) data analysis, including data preprocessing, analysis, modelling, and visualization using various data science techniques. The topic of the project is "Forecasting Policy Impacts on Economic Freedom: Machine Learning Approach." The analysis is based on data from the "Economic Freedom of the World" index, published by the Fraser Institute in 2021, which evaluates the extent to which the policies and institutions of countries support economic freedom.

Keywords

Data Science, Economic Freedom, Machine Learning, Policy Impact, Forecasting, Neural Networks, Random Forest Regressor, Support Vector Regressor, K-Nearest Neighbors, Gradient Boosting Regressor

Project Structure

Directory	File/Script Name	Description
data	EFIS_data.xlsx	The original dataset.
	cleaned_data.csv	Cleaned data; low VIF, no missing values, skewed dataset, no outliers.
	target_data.csv	Data with the y variable used for prediction.
src	EFIS_DA.ipynb	Data analysis code.
	EFIS_Model_Final.ipynb	Prediction models.
-	LICENSE	License information.
-	README.md	Project documentation.

Usage

To replicate the study or explore the data with our analyses, follow these steps:

1. Prepare Your Environment:

   • Ensure you have Python and the necessary libraries installed.

2. Download the Project Files:

   • Clone this repository or download the files directly.

3. Run the Analysis:

   • Open the relevant Jupyter notebooks in the src/ directory to view the primary analysis and models.

4. Explore the Dataset:

   • Load the dataset into your preferred data analysis tool (e.g., pandas in Python) to explore the dataset independently.

Mount Google Drive (if using Google Colab):

• If you are running the notebooks on Google Colab, ensure you mount your Google Drive to access the data files. Note that the EFIS_Model_Final.ipynb notebook includes mounting Google Drive files. If you want to run it locally, you should change the data directory paths accordingly.

Authors

• Rafał Prus - 202100779@post.au.dk
• Milena Chołożyńska - 202100776@post.au.dk

Dataset Source

• Fraser Institute - Economic Freedom of the World 2021 Annual Report
• Fraser Institute - Dataset