1 min readfrom Machine Learning

Built an AI tool that cleans datasets, fills missing values, and predicts unknown fields [P]

Our take

Introducing an innovative AI tool designed to streamline your data analysis process. Built using Streamlit, this tool intelligently fills missing values with machine learning models, predicts unknown columns using existing data, and detects anomalies. Additionally, it highlights correlations and feature importance, providing valuable insights for informed decision-making. Users can easily download the updated datasets for further exploration. I invite feedback on the model approach, accuracy, and potential improvements to enhance its effectiveness. Explore the tool on GitHub and share your thoughts!
Built an AI tool that cleans datasets, fills missing values, and predicts unknown fields [P]
Built an AI tool that cleans datasets, fills missing values, and predicts unknown fields [P]

I built a Streamlit-based AI data analysis tool that:

• Fills missing values using ML models (not just mean/median)

• Predicts any missing column using n-1 inputs

• Detects anomalies

• Shows correlations and feature importance

• Lets you download the updated dataset (Attached images show the UI and before vs after CSV file with a sample CSV available on the GitHub page, as well as an image showing the achieved performance metrics)

I wanted to test how well it works on real-world incomplete datasets.

Would love feedback on:

- model approach

- accuracy issues

- any improvements I should make

GitHub: https://github.com/WALKER00058/ML-data-analysis/tree/main

submitted by /u/walker98417
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