This repository contains an analysis of the economic dynamics influencing American families, focusing on income, poverty, and family composition. The project was conducted by Solomon T. Tessema, a Ph.D. student at National University.
The objective of this project is to employ exploratory data analysis (EDA) techniques to uncover how income and poverty levels are affected by various family compositions in the United States. The goal is to identify patterns, trends, and insights that could inform policy improvements.
The analysis is performed using Python in a Jupyter Notebook environment and includes the following steps:
- Data Cleaning and Preprocessing: Ensuring data quality and consistency.
- Descriptive Statistics: Summarizing key aspects of the data.
- Data Visualization: Creating visual insights into relationships between variables.
- Inferential Analysis: Drawing conclusions about the data through statistical methods.
The project features multiple visualizations that illuminate the connections between family composition, income levels, and poverty rates. These charts and graphs provide critical insights into socio-economic patterns.
To run this analysis:
- Ensure you have Python 3 installed on your system.
- Install the required libraries listed in the notebook.
- Open the Jupyter Notebook or JupyterLab and execute the cells.
Contributions are welcome! Fork this repository, make your changes, and submit a pull request. Suggestions for new analyses or additional data visualizations are also encouraged.
This project is licensed under the MIT License. See the LICENSE.md file for more details.