Turn messy datasets into analysis-ready data.
What it does
Builds data-cleaning pipelines that produce cleaned datasets, quality metrics, and validation reports.
When to use it
Use when preparing datasets for analysis or modeling, correcting data quality issues, or automating preprocessing.
How to use it
Give it a raw dataset; it produces cleaning code, a cleaned dataset, and reports documenting transformations and data quality.
What you provide
- Your files
Latest review
What you need · 4
The implementation uses Python; no version is specified.
The cleaning implementation imports pandas for loading and transforming tabular data.
The implementation imports numpy and uses its numeric type selection.
The implementation imports scaling and missing-value imputation classes from sklearn.
Access · 0
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