Clean It

Inspiration

The idea for Clean It was born out of the frustration that people all over the internet were showing on how disappointed they are that LLMs are automating things they want to do so that they can do what they don't want to do. And how much they want LLM to do what they DON'T want to do. Which also made sense.

So, I thought let's make a change, and if I want to get started why not from my favourite "data industry". Traditional data cleaning methods as we all are aware often involve manual labor and can be time-consuming. So why not leverage the power of AI to automate this tedious task?

And here we have it. Clean It: An app that aims to revolutionize the way data professionals handle dirty data.

What it does

Clean It is designed to be user-friendly. Simply upload your dirty dataset, and the application will take care of the rest. A sophisticated pipeline analyzes the data for common issues such as missing values, inconsistencies, and outliers. Using advanced machine learning algorithms, Clean It identifies and rectifies these problems, delivering a clean and usable dataset.

Key Features

Automated Data Cleaning: Clean It handles various data cleaning tasks, including:
- Missing Value Imputation: Fills in missing data points using appropriate techniques.
- Outlier Detection and Removal: Identifies and removes abnormal data points that can skew results.
- Data Standardization: Ensures consistency in data formats and units.
- Error Correction: Corrects errors and inconsistencies in data entries.
Efficiency and Accuracy: Clean It is powered by the state-of-the-art Llama3 large language model and Groq LPU, enabling it to accurately identify and address data quality issues efficiently.
User-Friendly Interface: The application boasts a simple and intuitive interface, making it accessible to users of all backgrounds.

How Clean It is built

Clean It is built using a combination of cutting-edge technologies. Streamlit, a popular Python library, provides the framework for the user interface, ensuring a seamless experience.

The heart of the application lies in its AI model, which is powered by a powerful language model like Llama. This model is responsible for understanding the data, identifying issues, and selecting the appropriate cleaning techniques.

How Clean It is built

Clean It is built using a combination of cutting-edge technologies. Streamlit, a popular Python library, provides the framework for the user interface, ensuring a seamless experience.

Challenges we ran into

Developing Clean It wasn't without its challenges. One of the primary hurdles was ensuring that the AI model could accurately identify and address various data-cleaning tasks. As I was creating an agent-based application, I started by trying to develop it by utilizing multi-agent libraries and tools like Crew AI and LangChain but after trying different approaches, multiple infinite loops, and wrong tool selection, and the lack of customization I finally decided to do it all from scratch.

Accomplishments that we're proud of

It is said that problems are put into our way to make us realize how capable we are.

The process of overcoming challenges and developing clean it after two failed attempts with 2 widely used tools, it was not easy to keep pushing myself to try one more time with a different approach. But here we are and that's what we are proud of, by the grace of Almighty.

What we learned

The journey of developing Clean It was filled with valuable insights, and I have learned a lot in the process of building it. I have gained extensive knowledge about various tools and technologies, including crew AI, LangChain, LangGraph, and their monitoring tool, LangSmith. And it was the first time I got the chance to try Nvidia AI Workbench as well.

But most importantly, that many things work best without the use of AI. Like the selection of tools in our case.

What's next for Clean It

Clean It is just the beginning. I am excited to continue enhancing the application and adding new features. Future plans include:

Advanced Data Quality Assessment: Providing detailed reports on data quality metrics.
Customizable Cleaning Rules: Allowing users to define their own cleaning rules for specific scenarios.
Integration with Data Analysis Tools: Seamlessly integrating Clean It with popular data analysis platforms.

Conclusion

Clean It is a powerful tool for data professionals who want to spend less time cleaning data and more time extracting valuable insights. By automating the data cleaning process, Clean It empowers users to make informed decisions and drive innovation.