Inspiration
We wanted to make data cleaning easier by helping users quickly understand and fix common data quality problems.
What it does
DataDoctor-LLM analyzes CSV/Excel files, detects missing values, duplicates, inconsistent categories, and invalid values, then provides cleaning suggestions and allows users to download cleaned data.
How we built it
We built it with Python, Pandas, Gradio, and an LLM, combining rule-based validation with LLM-powered data analysis and cleaning recommendations.
Challenges we ran into
Handling different data formats, detecting incorrect values reliably, and making the LLM recommendations safe and consistent were our main challenges.
Accomplishments that we're proud of
We built a working end-to-end data quality tool that can analyze, validate, clean, and export datasets through a simple interface.
What we learned
We learned how to combine LLMs with traditional data processing and validation rules to build a more reliable AI application.
What's next for DataDoctor-LLM
We plan to improve automatic cleaning, support more data-quality rules, enhance LLM reasoning, and add more advanced data insights.
