Fred-Data-Analysis-Project-with-API-Python

This project is focused on analyzing economic data using the FRED API, which provides access to a wealth of economic data from the Federal Reserve Bank of St. Louis. The key steps in the project are:

1. API Setup: The project begins by loading a secret API key from a JSON file to interact with the FRED API. The API key is essential for accessing the data.

2. FredPy Class: A custom Python class FredPy is created to manage interactions with the FRED API. This class includes methods to set the API token and to fetch economic data series based on specific parameters such as seriesID, date range, and units.

3. Fetching Data: The project retrieves two specific economic data series:

   • GDP (Gross Domestic Product): Data from January 2005 to December 2021, presented as a percentage change (annualized).
   • Unemployment Rate: Data from the same period, presented as a linear percentage.

4. Data Processing: The data fetched from the API is converted into a Pandas DataFrame, with dates parsed into a datetime format and values converted to floats for further analysis.

5. Visualization: The project concludes with visualizing the GDP and Unemployment Rate data on a single plot, showing trends over the specified period. The plot is labeled appropriately, with a title, axis labels, and a legend to distinguish the two data series.

This project provides a foundation for analyzing and visualizing economic indicators, useful for understanding economic trends and making informed decisions based on historical data.

Tools Used:

1. Python: The primary programming language used for scripting, data processing, and visualization.
2. Pandas: A Python library used for data manipulation and analysis. It's utilized to create DataFrames, handle date conversions, and manage data types.
3. Matplotlib: A plotting library in Python used to visualize the economic data (GDP and Unemployment Rate) in a time-series plot.
4. FRED API: An API provided by the Federal Reserve Bank of St. Louis to access a wide range of economic data. It's used to retrieve GDP and Unemployment Rate data.

Technical Concepts:

1. API Integration: The project demonstrates how to integrate with a RESTful API (FRED API) using Python's requests library. It involves forming URL requests with the appropriate parameters and handling JSON responses.

2. Class-Based Design: The FredPy class encapsulates API interactions, making it easy to reuse and extend for fetching different economic data series. This design promotes modularity and code reuse.

3. Data Processing: The project showcases essential data processing techniques:

   • Converting JSON data into a Pandas DataFrame.
   • Parsing and formatting dates using pd.to_datetime.
   • Handling numeric data by converting strings to floats for analysis.

4. Data Visualization: The project uses Matplotlib to create a line plot that visually compares two economic indicators over time. This includes adding titles, labels, legends, and customizing the plot's appearance.

5. Error Handling: The project includes basic error handling, checking for successful API responses and raising exceptions if the API key is missing or the response is unsuccessful.