Metadata-Version: 2.1
Name: mindsdb
Version: 0.8.1
Summary: MindsDB's goal is to make it very simple for developers to use the power of artificial neural networks in their projects. 
Home-page: https://github.com/mindsdb/main
Author: MindsDB Inc
Author-email: jorge@mindsdb.com
License: UNKNOWN
Description: 
        # MindsDB
        
        MindsDB's goal is to make it very simple for developers to use the power of artificial neural networks in their projects. 
        
        
        * [Installing MindsDB](docs/Installing.md)
        * [Config Settings](docs/Config.md)
        * [Learning from Examples](docs/examples/basic/README.md)
        * [Inside MindsDB](docs/InsideMindsDB.md)
        
        
        
        ## Quick Overview
        
        It's very simple to setup [(learn more)](docs/Installing.md)
        
        ```bash
         pip3 install mindsdb --user
        ```
        
        Once you have MindsDB installed, you can use it as follows [(learn more)](docs/examples/basic/README.md):
        
        
        To **train a model**:
        
        
        
        ```python
        
        from mindsdb import *
        
        
        # We tell mindsDB what we want to learn and from what data
        MindsDB().learn(
            from_data="https://raw.githubusercontent.com/mindsdb/main/master/docs/examples/basic/home_rentals.csv", # the path to the file where we can learn from, (note: can be url)
            predict='rented_price', # the column we want to learn to predict given all the data in the file
            model_name='home_rentals' # the name of this model
        )
        
        ```
        
        
        To **use the model**:
        
        
        ```python
        
        from mindsdb import *
        
        # use the model to make predictions
        result = MindsDB().predict(predict='rented_price', when={'number_of_rooms': 2,'number_of_bathrooms':1, 'sqft': 1190}, model_name='home_rentals')
        
        # you can now print the results
        print('The predicted price is ${price} with {conf} confidence'.format(price=result.predicted_values[0]['rented_price'], conf=result.predicted_values[0]['prediction_confidence']))
        
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
