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An app which allows analysts to train machine learning models and make predictions on large datasets without having to deal with any code, with a built-in data quality analysis toolkit

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prathgan/AnaLazy

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AnaLazy

AnaLazy is a graphical web or desktop application which enables analysts to train a machine learning model off of a spreadsheet of training data and then make useful predictions, all without having to write a single line of code. AnaLazy also has a built-in data quality analysis tool to allow you to ensure your data is up to par before training a model with it.

Installation

Download the code from the GitHub repository and install all dependencies by cding to AnaLazy's root directory and

pip install -r requirements.txt

Then create internal directories for backend use

mkdir uploads
mkdir models

Usage

Navigate in a terminal to the root of the folder with the AnaLazy app code.

To run AnaLazy in the standard configuration (GUI will automatically open)

python index.py

Use the --web flag to open app in default browser instead of as a standalone GUI and the --debug flag to enable hot reloading and disable automatic page opening on restart.

Images

Home Page Upload Page
home upload
Quality Analysis Page Train Page
quality train
Models Page
models

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT

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An app which allows analysts to train machine learning models and make predictions on large datasets without having to deal with any code, with a built-in data quality analysis toolkit

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