********************** spacekit documentation ********************** This is the documentation for ``spacekit``, the Astronomical Data Science and Machine Learning Toolkit Overview ======== Spacekit is a python library designed to do the heavy lifting of machine learning in astronomy-related applications. The modules contained in this package can be used to assist and streamline each step of a typical data science project: 1. :doc:`Ingest/Extract ` import large datasets from a variety of file formats .csv, .hdf5, .fits, .json, .png (.asdf coming soon) 2. :doc:`Scrub/Preprocess ` scrub and preprocess raw data to prepare it for use in a machine learning model 3. :doc:`Modeling ` build, train and deploy custom machine learning models using classification, logistic regression estimation, computer vision and more 4. :doc:`Analysis ` evaluate model performance and do exploratory data analysis (EDA) using interactive graphs and visualizations 5. :doc:`Visualize ` deploy a web-based custom dashboard for your models and datasets via docker, a great way to summarize and share comparative model evaluations and data analysis visuals with others Applications ------------ The :doc:`Skøpes ` module includes real-world machine learning applications used by the Hubble and James Webb Space Telescopes in data calibration pipelines. These mini-applications are an orchestration of functions and classes from other spacekit modules to run automated analysis, training, and inference in real-time on a local server or in the cloud (AWS). Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex`