This project focuses on analyzing Gross Domestic Product (GDP) data to uncover insights about economic growth, trends, and performance across countries or regions. Using data science techniques, we aim to identify patterns and draw meaningful conclusions to support economic decision-making.
- Import and preprocess GDP datasets.
- Visualize GDP trends over time using plots and charts.
- Compare GDP growth rates between different countries/regions.
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn, Plotly
- Development Environment: Google Colab
The project uses GDP datasets from reliable sources such as:
- World Bank Open Data
- IMF (International Monetary Fund)
- UN Data
- "USA, China experienced the highest GDP growth between 1960 and 2016."
- "GDP growth in developing countries such as India, China has outpaced developed countries during the last decade."
- Clone the repository:
git clone https://github.com/Braj-01/GDP-Analysis.git
- Navigate to the project directory:
cd GDP-Analysis - Install the required libraries:
pip install -r requirements.txt
- Open the Jupyter Notebook:
jupyter notebook
- Run the notebook file
GDP_Analysis.ipynbstep by step to see the results. - Visualize the GDP trends and download the analysis report.
gdp/- Contains raw and processed GDP datasets.GDP_ANALYSIS/- Jupyter notebooks with the analysis code.README.md- Documentation of the project.
Contributions are welcome! To contribute:
- Fork the repository.
- Create a new branch for your feature or bug fix:
git checkout -b feature-name
- Commit your changes and push the branch:
git push origin feature-name
- Open a pull request.
This project is licensed under the MIT License.
For questions or collaboration, feel free to connect:
- Name: Braj Narayan Awasthi
- LinkedIn: Braj Narayan Awasthi
Thank you for exploring the GDP Analysis project! Your feedback and suggestions are highly appreciated.