📄 EDA-LLM-Project (README.md)

📊 LLM-Powered Exploratory Data Analysis (EDA)

This project automates Exploratory Data Analysis (EDA) by combining Pandas, Seaborn, Matplotlib with Google Gemini LLM for AI-driven insights.
It generates:

• 📝 Summary statistics
• 🧩 Missing values report
• 📈 Visualizations (histograms, heatmaps, etc.)
• 🤖 AI-powered insights from Gemini
• 📑 Downloadable PDF Report

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🚀 Features

• Upload a CSV file and instantly get an EDA Report
• Structured summary + missing values in tabular format
• Automated visualizations for numeric features
• AI insights powered by Gemini
• Export results as a PDF report

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📂 Project Structure

LLM-Powered-EDA/
│── app.py                     # Main Gradio app
│── requirements.txt           # Dependencies
│── README.md                  # Project description

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🔧 Installation

1. Clone the repository:

   git clone https://github.com/your-username/LLM-Powered-EDA.git
   cd LLM-Powered-EDA
   

2. Install dependencies:

   pip install -r requirements.txt

3. Set up your Gemini API Key:

   export GOOGLE_API_KEY="your_api_key_here"    # Mac/Linux
   set GOOGLE_API_KEY="your_api_key_here"       # Windows PowerShell

4. Run the app:

   python app.py

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🤖 Gemini Setup

• Get your free API key from Google AI Studio
• Students usually get $300 free credits (3 months)
• Each request costs only a few tokens — more than enough for EDA projects

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📊 Example Workflow

1. Upload sample.csv
2. View summary + missing values + AI insights
3. Explore histograms + correlation heatmap
4. Download full PDF report

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📜 License

MIT License. Use freely for education and research.

📦 requirements.txt

gradio==4.36.1
pandas==2.2.2
numpy==1.26.4
matplotlib==3.9.2
seaborn==0.13.2
reportlab==4.2.2
google-generativeai==0.5.4