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AI-powered data analysis tool that lets you explore and visualize your data through natural language queries. Built with Streamlit and PandasAI.

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AI Data Analyst

A Streamlit-based web application that leverages AI to analyze and visualize data through natural language queries. Upload your dataset and get instant insights through conversational interactions.

Features

  • Easy Data Upload: Supports CSV and Excel file formats
  • Natural Language Queries: Ask questions about your data in plain English
  • Interactive Visualizations: Generates relevant charts and graphs
  • Conversation History: Maintains context of your analysis session
  • Responsive Design: Works on both desktop and mobile devices

Prerequisites

  • Python 3.11 or higher
  • pip (Python package manager)
  • An OpenAI API key (or other supported LLM provider)

Installation

  1. Clone the repository:

    git clone https://github.com/muhammadhamzaazhar/Data-Analysis.git
    cd Data-Analysis
  2. Create and activate a virtual environment (recommended):

    # Windows
    python -m venv venv
    .\venv\Scripts\activate
    
    # macOS/Linux
    python -m venv venv
    source venv/bin/activate
  3. Install the required packages:

    pip install -r requirements.txt
  4. Set up your environment variables: Create a .env file in the root directory and add your API key:

    OPENROUTER_API_KEY=your_api_key_here
    LLM_MODEL=openrouter/deepseek/deepseek-chat-v3.1:free
    LLM_TEMPERATURE=0.5
    

Usage

  1. Run the application:

    streamlit run app.py
  2. Upload your dataset using the sidebar

  3. Start asking questions about your data in natural language

Example Queries

  • "Show me the distribution of sales by region"
  • "What are the top 5 products by revenue?"
  • "Create a scatter plot of price vs. sales"
  • "What's the average order value?"
  • "Show me trends in customer purchases over time"

Acknowledgments

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AI-powered data analysis tool that lets you explore and visualize your data through natural language queries. Built with Streamlit and PandasAI.

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