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# AI-Powered Excel Data Analysis App
A Streamlit application that automates Excel data processing, provides intelligent analysis using Google's Gemini AI, and offers interactive visualizations. Perfect for analyzing EOC (Emergency Operations Center) data with automated designation-to-cadre mapping.
## Features
- **File Upload & Processing**
- Supports CSV, XLS, XLSX formats
- Automatic data cleaning
- Smart designation to cadre mapping
- Handles multi-level headers
- **Interactive Data Preview**
- Column selection
- Global search functionality
- Advanced column-specific filters
- Customizable row display
- Hide/show index options
- **AI-Powered Analysis**
- Intelligent data insights using Gemini AI
- Natural language queries
- Automated data summaries
- Pattern recognition
- Follow-up question suggestions
- **Data Visualization**
- Dynamic charts and graphs
- Cadre distribution analysis
- District-wise visualizations
- Interactive dashboards
- Correlation analysis
## Setup & Installation
1. **Clone the repository**
```bash
git clone https://github.com/HussainM899/AI-Data-Processing-Analytics.git
cd AI-Data-Processing-Analytics
```
2. **Create and activate virtual environment**
```bash
python -m venv venv
source venv/bin/activate # For Linux/Mac
venv\Scripts\activate # For Windows
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Set up environment variables**
- Create a `.env` file in the root directory
- Add required credentials (see `.env.example`)
## Required Environment Variables
```.env
env
GOOGLE_APPLICATION_CREDENTIALS=path/to/credentials.json
GOOGLE_API_KEY=your_api_key_here
```
## Usage
1. **Start the application**
```bash
streamlit run app.py
```
2. **Upload Data**
- Use the file uploader to import your Excel/CSV file
- The app automatically processes and cleans the data
- Multi-level headers are automatically handled
3. **Analyze Data**
- Use the navigation sidebar to switch between modes:
- Data Processing
- Analysis & Visualization
- About
- Ask questions in natural language
- View automated insights and visualizations
4. **Export Results**
- Download processed data in Excel format
- Export updated designation mappings
- Save analysis reports
## Project Structure
```
AI-Data-Processing-Analytics/
├── app.py # Main application file
├── requirements.txt # Project dependencies
├── .env.example # Example environment variables
├── .gitignore # Git ignore rules
└── README.md # Project documentation
```
## Dependencies
- `streamlit`: Web application framework
- `pandas`: Data manipulation and analysis
- `plotly`: Interactive visualizations
- `google-generativeai`: Gemini AI integration
- `langchain-google-genai`: LangChain integration
- `python-dotenv`: Environment variable management
- `openpyxl`: Excel file handling
## Security Notes
- Never commit sensitive credentials
- Use environment variables for API keys
- Keep service account JSON file secure
- Regularly rotate credentials
- Avoid sharing API keys publicly
## Features in Detail
### Data Processing
- Automatic cleaning of data
- Handling of missing values
- Removal of duplicates
- Smart string cleaning
- Multi-level header handling
### AI Analysis
- District-wise analysis
- Cadre distribution insights
- Trend identification
- Anomaly detection
- Custom query handling
### Visualization
- Pie charts for distributions
- Bar charts for comparisons
- Histograms for numerical data
- Correlation matrices
- Interactive filters
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contact
Hussain - hussainmurtaza899@gmail.com
Project Link: [https://github.com/HussainM899/AI-Data-Processing-Analytics](https://github.com/HussainM899/AI-Data-Processing-Analytics)
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Built using Streamlit and Gemini AI
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