Metadata-Version: 2.1
Name: bigbrain
Version: 0.0.2
Summary: A toolbox for learning anything and everything from data.
Home-page: https://github.com/gmischl1/bigbrain
Author: ('Joanna Guo', 'Gavin Mischler')
Author-email: gavin.m.mischler@gmail.com
License: Apache License 2.0
Description: # bigbrain
        [![Python](https://img.shields.io/badge/python-3.7-blue.svg)]()
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        
        bigbrain is a toolbox for learning anything and everything from data.
        
        # No machine learning knowledge needed!
        
        With this toolbox, all your data science problems can be solved with a
        single command. No longer will you need to know anything about machine
        learning in order to use its power to solve all your problems.
        
          - Learn the best models from your data without any background knowledge!
          - Let the AI learn the best algorithm for your data, and then use what it learned on other data!
        
        ### Tech
        
        bigbrain wraps [scikit-learn](https://github.com/scikit-learn/scikit-learn) to try many different models on your data and find the best performing algorithm for you.
        
        ### Installation
        
        bigbrain can be installed using pypi, either from the [website](https://pypi.org/project/bigbrain), or from the command line:
        
        ```sh
        $ pip install bigbrain
        ```
        
        ### Example Usage
        
        With this package, you can easily create regression or classification models for supervised learning techniques.
        
        ```py
        >>> from bigbrain import AI
        >>> from sklearn.datasets import iris, load_boston
        >>> from sklearn.model_selection import train_test_split
        >>> from sklearn.metrics import accuracy_score,
        ...     mean_squared_error
        ####### Classification #######
        >>> X, y = iris(return_X_y=True)
        >>> X_train, X_test, y_train, y_test = train_test_split(
        ...     X, y, test_size=0.3)
        >>> machine = AI(model_type='classification')
        >>> machine.learn(X_train, y_train)
        	"My big brain has learned everything."
        >>> predictions = machine.go(X_test)
        >>> print(accuracy_score(y_test, predictions)
        	"0.97"
        ####### Regression #######
        >>> X, y = load_boston(return_X_y=True)
        >>> X_train, X_test, y_train, y_test = train_test_split(
        ...     X, y, test_size=0.3)
        >>> machine = AI(model_type='regression')
        "My big brain has learned everything."
        >>> predictions = machine.go(X_test)
        >>> print(mean_squared_error(y_test, predictions)
        	"14.03"
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
