r/dataanalysis • u/Far_Blacksmith4879 • 9d ago

I built an open-source AI Data Analysis Assistant in Streamlit using Python & Groq (Llama-3.3-70b)

What it does:

A lightweight app that takes any CSV, automates data profiling/descriptive stats, renders multi-chart visualizations (Seaborn/Matplotlib), and uses Groq (llama-3.3-70b) to explain findings and answer ad-hoc questions in plain English.

Why I built it:

Built this for the AI Data Analysis Challenge (Track A) to explore integrating fast LLM inference with automated exploratory data analysis (EDA).

Tech Stack:

  • Python (Pandas, NumPy, Matplotlib, Seaborn)
  • Groq API (llama-3.3-70b-versatile)
  • Streamlit UI

Key Learnings:

  • Dynamically adapting charts based on detected data types.
  • Passing structured dataset summaries to LLMs for grounded answers without hallucination.

GitHub: https://github.com/binarylaiba/AI-Data-Analysis-Asistant
Live demo: https://ai-data-analysis-asistant.streamlit.app/

Would love feedback on how to improve the charting logic or prompt design!

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