Metadata-Version: 2.1
Name: autoregressor
Version: 1.0.0
Summary: Automated entire process of building and training regression based models
Home-page: https://github.com/jaydulera/autoregressor
Author: Jay Dulera
Author-email: jaydulera01@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
Requires-Dist: sklearn
Requires-Dist: pandas
Requires-Dist: numpy

# autoregressor

You can install autoregressor from PyPI

    $ pip install autoregressor

Once the library is installed, import the modules in your python notebook or any IDE of your choice.

    $ import autoregreessor
    from autoregressor import compute

We now have the package imported. To use the library, we use:

    prediction = autoregressor(X_train , y_train , X_test)

Where, 

* prediction - Predicted output of the Regression model.
* X_train - Training dataset
* y_train - Target variable of the training dataset
*X_test - Testing dataset

