Welcome to the Bank Performance Analysis repository! This project focuses on analyzing banking data from the Berka Dataset, covering the years 1993 to 1998. Our goal is to calculate and visualize key performance indicators (KPIs) that help us understand banking performance over time.
- Introduction
- Features
- Technologies Used
- Getting Started
- Usage
- Visualizations
- Key Performance Indicators (KPIs)
- Contributing
- License
- Contact
In the ever-evolving world of finance, understanding performance metrics is crucial. This repository provides tools to analyze historical banking data effectively. By utilizing various technologies, we aim to make data analysis straightforward and insightful.
You can download the latest release here. Please follow the instructions in the Getting Started section to execute the files.
- Data Analysis: Analyze banking data from 1993 to 1998.
- Data Visualization: Create interactive visualizations using Plotly and Recharts.
- Dashboard: Build a dashboard using Next.js for an engaging user experience.
- Key Performance Indicators: Calculate essential KPIs to evaluate banking performance.
This project leverages a variety of technologies to provide a comprehensive analysis:
- Python: For data manipulation and analysis.
- Pandas: To handle data frames and perform complex data operations.
- Plotly Express: For creating interactive visualizations.
- Next.js: To build a responsive dashboard.
- Recharts: For charting in React applications.
- SQLAlchemy: For database interactions.
- PyMongo: To work with MongoDB for data storage and retrieval.
To get started with the Bank Performance Analysis project, follow these steps:
-
Clone the Repository:
git clone https://github.com/Kezzaku/Bank-performance-analysis.git cd Bank-performance-analysis -
Install Required Packages: Make sure you have Python installed. Then, run:
pip install -r requirements.txt
-
Download the Dataset: You can find the Berka Dataset here. Download it and place it in the
datafolder. -
Run the Application: To run the application, use:
python app.py
You can also visit the Releases section for more information on the latest updates.
After running the application, you can access the dashboard in your web browser. The dashboard will provide options to select various KPIs and visualizations. Use the interactive features to explore the data.
The project includes various visualizations that represent the banking data effectively. Some examples include:
- Line Charts: To show trends over time.
- Bar Charts: To compare different KPIs.
- Pie Charts: To illustrate market share.
These visualizations help stakeholders understand the data at a glance.
In this project, we focus on several KPIs to measure banking performance:
- Return on Assets (ROA): Indicates how profitable a bank is relative to its total assets.
- Return on Equity (ROE): Measures the profitability of a bank in relation to shareholders' equity.
- Net Interest Margin (NIM): Shows the difference between interest income and interest paid, relative to interest-earning assets.
- Cost-to-Income Ratio: Compares operating expenses to operating income, indicating efficiency.
These KPIs provide valuable insights into the bank's performance over the analyzed period.
We welcome contributions from the community. If you would like to contribute, please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/YourFeature). - Make your changes and commit them (
git commit -m 'Add new feature'). - Push to the branch (
git push origin feature/YourFeature). - Open a pull request.
Your contributions help improve the project and make it more useful for everyone.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or suggestions, feel free to reach out:
- GitHub: Kezzaku
- Email: your-email@example.com
Thank you for visiting the Bank Performance Analysis repository! We hope you find it useful for your data analysis needs. Don't forget to check the Releases for the latest updates and features.