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autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.
About autoai
autoai (BlobCity AutoAI) is an open-source Python framework that automates model building for classification and regression problems on numerical data. It infers the problem type from the target column, runs model search and hyper-parameter tuning, and exports the trained model as Jupyter Notebook or Python code. Built-in preprocessing handles missing values, column encoding and data scaling, and the same preprocessing is applied when predicting on new data. The project is in beta release with active development still in progress.
Key features
- Automatic inference of regression or classification from the target column
- Trains from local CSV/XLSX files, a CSV/XLSX URL, or a Pandas DataFrame
- Built-in preprocessing for missing values, column encoding and data scaling
- Automatic feature selection that also removes ID and primary-key columns
- Model search and hyper-parameter tuning across regression and classification model libraries
- Exports model code as .ipynb or .py with optional detailed documentation
Best for: Data scientists and analysts working with numerical tabular data who want automated model selection plus exportable, documented model code they can inspect and reuse.
Good for
- Open source, installed with pip and driven by a short train/spill API
- Produces readable code artifacts instead of only an internal trained model
- Preprocessing choices can be reviewed by exporting the model configuration to YAML
Watch out for
- Released as beta, with development still in progress
- Limited to classification and regression on numerical data
- TensorFlow and other DNN exports contain only test/final-use code, not training code
autoai at a glance
- Website
- github.com/blobcity/autoai
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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autoai FAQ
What data inputs can be used for training?
A local CSV or XLSX file, a URL pointing to a CSV or XLSX file, or a Pandas DataFrame, passed with the file or df argument alongside the target column name.
How does the framework decide between regression and classification?
It performs automatic inference of the problem type; the only required input is the name of the target column, which must exist in the data.
What does the spill function generate?
It writes the model code to an .ipynb or .py file, with options to enable or disable detailed documentation. scikit-learn models export with training code included, while TensorFlow and other DNN models produce only test or final-use code.
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