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AI Data Analyst Chatbot
What it is
AI Data Analyst Chatbot is free, open-source software for exploring structured datasets through natural-language questions. It accepts uploaded CSV and Excel (XLSX) files and processes them during the session rather than storing them permanently. Automated profiling covers columns, distributions, summary statistics, missing values, duplicates, and data-quality issues. Statistical analysis can reveal variation, outliers, correlations, and patterns, while suggesting statistical tests. Interactive visualizations and explanations accompany the insights. Users can ask questions in plain English without SQL or extensive coding knowledge. The project is self-hosted and web-based, built with technologies including Streamlit, pandas, NumPy, SciPy, Plotly, LangChain, and Groq LLM (LLaMA 3.1). Running it requires a Groq API key in the GROQ_API_KEY environment variable. The README directs users to install dependencies and launch Streamlit at localhost:8501. It is MIT-licensed and accepts contributions through forks, pull requests, or issues.
For and against
For
- Free, MIT-licensed open-source software
- Accepts CSV and XLSX uploads
- Profiles missing values and duplicates
- Provides statistical insights and visualizations
- No permanent storage of uploaded data
Against
- Requires a Groq API key
- Self-hosted setup is required
- No SQL connections
- No team collaboration
The verdict
AI Data Analyst Chatbot combines dataset profiling, statistical analysis, and visual explanations in a natural-language interface. Its self-hosted setup and Groq key requirement matter when deciding whether it fits your workflow.
Who it suits
It suits analysts and data practitioners who want to examine CSV or XLSX datasets with natural-language questions. It also fits users comfortable with self-hosting and configuring a Groq API key.
Plans and prices
Open-source software
MIT-licensed · CSV and XLSX input · Requires a Groq API key
Read 8 Oct 2026 · github.com
On the record
- Data handling
- Uploaded data is processed in-session and is not permanently stored.github.com · read 8 Oct 2026
- Data inputs
- It supports uploaded CSV and Excel (XLSX) files.github.com · read 8 Oct 2026
- Data profiling
- The project describes automated profiling for columns, distributions, summary statistics, missing values, duplicates, and data quality issues.github.com · read 8 Oct 2026
- Deployment
- The README instructs users to install dependencies and launch the app with Streamlit, which makes it available at localhost:8501.github.com · read 8 Oct 2026
- Integration requirement
- Running the app requires a Groq API key set as the GROQ_API_KEY environment variable.github.com · read 8 Oct 2026
- License
- The repository provides the software under the MIT License.github.com · read 8 Oct 2026
- Maker
- The GitHub profile identifies the maker as Priyansh Khandelwal and says he is pursuing a Master’s in Computer Science at the University of Sydney.github.com · read 8 Oct 2026
- Natural language interface
- Users can ask questions in plain English without requiring SQL or extensive coding knowledge.github.com · read 8 Oct 2026
- Statistical analysis
- It can surface variation, outliers, correlations, patterns, and recommendations for statistical tests.github.com · read 8 Oct 2026
- Support and contributions
- The README invites users to contribute through forks, pull requests, or issues for enhancements and bug fixes.github.com · read 8 Oct 2026
- Technology
- The project lists Streamlit, pandas, NumPy, SciPy, Plotly, LangChain, and Groq LLM (LLaMA 3.1) among its technologies.github.com · read 8 Oct 2026
- Visualizations
- The chatbot provides interactive visualizations and explanations alongside statistical insights.github.com · read 8 Oct 2026
- What it does
- The chatbot lets users explore, analyze, and understand structured datasets by asking questions in natural language.github.com · read 8 Oct 2026
- Who it is for
- The project describes itself as built for analysts and data practitioners.github.com · read 8 Oct 2026
