README_SPACE.md

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

   git clone https://github.com/HussainM899/AI-Data-Processing-Analytics.git
   cd AI-Data-Processing-Analytics
   

2. Create and activate virtual environment

   python -m venv venv
   source venv/bin/activate  # For Linux/Mac
   venv\Scripts\activate     # For Windows
   

3. Install dependencies

   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
GOOGLE_APPLICATION_CREDENTIALS=path/to/credentials.json
GOOGLE_API_KEY=your_api_key_here

Usage

1. Start the application

   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 file for details.

Contact

Hussain - hussainmurtaza899@gmail.com Project Link: https://github.com/HussainM899/AI-Data-Processing-Analytics

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Built using Streamlit and Gemini AI