e-panourgia / economic_connectedness_analysis_visualizations

Visualizations - Python

Purpose

This repository contains an analysis of Economic Connectedness, that was done in the context of the university course: Applied Machine Learning.

Tools and Packages

The analysis was executed on Jupyter.

Additional packages required for the project to run are:

• pandas
• numpy
• seaborn
• matplotlib
• Plotly
• plotnine-ggplot
• urlib
• warnings
• json
• adjustText
• os
• turtle/color
• cprofile/label

All the packages above can be installed using the pip install command-line command.

Data

The data used can be found in the data folder. * Running the jupyter notebook, you will find detailed description-documentation.

How to Run

1. Clone the project: Execute the command https://github.com/e-panourgia/economic_connectedness_analysis_visualizations.git
2. Unzip folder t8190130_Assignment_1st.7z.
3. Move into the folder t8190130_Assignment_1st.
4. Run jupyter notebook named: Economic_Connectedness_Analysis_Visualizations.ipynb.

---

Generated Visualizations

• Q1 : The Geography of Social Capital in the United States
• Q2 : Economic Connectedness and Outcomes
• Q3: Upward Income Mobility, Economic Connectedness, and Median House Income
• Q4 Friending Bias and Exposure by High School
• Q5: Friending Bias vs. Racial Diversity¶

---

Note: The purpose behind this analysis was to performed it in order to get comfortable with using pandas, visualizations and Jupyter Notebook.