📊 Analytics AI – AI-Powered Data Analysis Tool

Analytics AI is a user-friendly, Streamlit-based web application that uses Groq AI (LLaMA 3 model) to help you analyze your CSV and Excel datasets using natural language questions.

🔍 Upload your data → Ask a question → Get insights powered by AI!

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🚀 Features

• 📁 Upload CSV, XLSX, or XLS files
• 🧠 Ask data-related questions in natural language
• 📈 Get AI-generated insights with statistics and summaries
• 🧮 View descriptive statistics, column types, and missing values
• 🤖 Powered by Groq LLaMA3-70b for high-speed AI processing

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🛠️ Installation

1. Clone the repository

git clone https://github.com/<your-username>/analytics_ai.git
cd analytics_ai

2. Install dependencies

pip install -r requirements.txt

3. Set up environment variables

Create a .env file in the project root and add your Groq API key:

GROQ_API_KEY=your_groq_api_key_here

4. Run the Streamlit app

streamlit run app.py

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📂 File Structure

analytics_ai/
├── app.py               # Main Streamlit application
├── .env                 # API Key (not committed)
├── requirements.txt     # Python dependencies
└── README.md            # Project documentation

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🧠 How to Use

1. Launch the app: streamlit run app.py

2. Upload your dataset (CSV or Excel)

3. Explore:

   • View data preview
   • Check dataset shape and data types
   • See statistical summaries
   • Check missing values

4. Ask questions like:

   • "What are the key trends?"
   • "What's the average salary by department?"
   • "Which columns are most correlated?"

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📌 Example Questions

• What are the top 5 most frequent values in column X?
• What is the average, min, and max of column Y?
• Show trends based on year or category.
• Which columns have missing values?

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📦 Dependencies

• streamlit
• pandas
• chardet
• groq
• python-dotenv

Install with:

pip install -r requirements.txt

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⚠️ Notes

• CSV files should preferably be UTF-8 encoded.
• Avoid uploading very large datasets (recommended < 10MB).
• Requires a valid Groq API key to access the LLaMA 3 model.

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🙌 Acknowledgments

• Groq – for providing blazing-fast inference with LLaMA 3.
• Streamlit – for easy web app deployment.
• Open-source community for continuous inspiration.

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👨‍💻 Author

Prince Thummar Connect with me on GitHub