Overview: This project is about analyzing unemployment trends across different states using data from the Federal Reserve Economic Data (FRED) API and some web scraping. I used Python and its libraries to collect, clean, and analyze the data, then created visualizations to make the findings clear and useful.
- API Integration: Pulled data automatically using the FRED API.
- Web Scraping: Added extra data by scraping websites.
- Data Cleaning: Made sure the data was accurate and consistent using tools like Pandas.
- Exploratory Data Analysis (EDA): Looked for patterns and insights in the data.
- Visualization: Created clear visuals of unemployment trends by state using Matplotlib and Plotly.
- Stakeholder Insights: Highlighted the key takeaways and what they mean for decision-makers.
- Python: Main programming language used.
- Pandas: For cleaning and organizing data.
- NumPy: For working with numbers and calculations.
- Matplotlib & Plotly: For creating charts and graphs.
- Requests: To connect to the FRED API.
- BeautifulSoup: For web scraping extra data.
- How to automate data collection with APIs.
- How to clean and organize large sets of data.
- How to create visuals that make data easy to understand.
- How to explore data and find interesting trends.
- How to handle challenges in data analysis with creativity and persistence.
I love working with data and figuring out how to make sense of it. With this project, I aimed to simplify the data scraping process from FRED and experiment with new methods of using APIs. This experience has sparked my interest in exploring other projects where I can work with raw data and APIs. Additionally, I’ve included my project presentation as PDF file in the repository for those who want to learn more or see a summary of my findings. If you have any feedback for me, please don't hesitate to reach out to me!
- Adding more economic data for a deeper analysis.
- Building interactive dashboards to make the data even easier to explore.
- Using machine learning to predict future trends.