CleanData

CleanData was born out of a recurring challenge we faced as startup founders: the lack of clean, affordable, and usable datasets. Whether it was a founder testing a prototype or a data scientist battling missing values, data was often incomplete, expensive, or entirely unavailable. Motivated by our personal struggles and the growing demand for reliable data, we created CleanData a platform that not only corrects missing data but also generates synthetic datasets tailored to specific industry needs. Our goal was to make high-quality data accessible to all innovators, regardless of budget.

Building CleanData was both challenging and rewarding. We used Python and Django for the backend, Streamlit for the UI, and PyTorch with scikit-learn for the AI engine. PostgreSQL served as our database, with everything deployed on AWS. A major focus was ensuring the precision and efficiency of data generation across diverse sectors like healthcare, education, and finance. We also prioritized creating a user-friendly interface for individuals with varying technical expertise.

We believe Intellidata will help businesses build MVPs and test AI models quickly without relying on costly datasets. Looking ahead, we plan to enhance customization and intelligence in our models to deliver ethical, realistic, and accessible synthetic data for all.

Built With

• amazon-web-services
• django-rest-framework
• django-restframwork
• docker
• git
• github
• postgresql
• python
• pytorch
• scikit-learn
• sdv
• streamlit