Metadata-Version: 2.1
Name: cerbo
Version: 0.0.1
Summary: Perform Efficient ML/DL Modelling
Home-page: https://github.com/StartOnAI/Cerbo
Author: StartOnAI
Author-email: startonaicom@gmail.com
License: UNKNOWN
Description: # Cerbo
        
        Cerbo is "brain" in Esperanto. 
        
        It is a high-level API wrapping Scikit-Learn, Tensorflow and Keras. Allowing, you to efficiently perform ML modelling and preprocessing.
        
        ## Install
        
        Installing Cerbo:
        ```
        pip install cerbo
        ```
        
        or
        
        ```
        python -m pip install cerbo
        ```
        
        ## Writing your first program!
        
        Currently, Cerbo performs efficient ML/DL modelling in a couple lines with limited preprocessing capabilites, we are adding new ones daily. Currently, to train a model from a CSV file all you have to do is call 
        
        ```python
        from cerbo.preprocessing import *
        
        data, col_names = load_custom_data("path_to_csv", "column_you_want_to_predict", num_features=4, id=False)
        ```
        
        *data* is a dictionary containing X and y values, for training.
        
        
        *col_names* is a list of features 
        
        
        Note: set id to true when there is an Id column in the CSV File, and set Num_Features to any value(as long it is within the # of colunns in the file"
        
        
        After running this you will get 2 .png files labelled correlation, and features respectively.
        * Correlation.png
          * Will show a correlation matrix of all of the features in the CSV file
        * feature.png
          * Will show a Pandas Scatter Matrix of with a N x N grid with N being *num_features*.
         
        
        To train a model on this data just do
        
        
        ```python
        gb, preds = Boosting("r", data, algo="gb", seed=42) 
        ```
        
        
        Which quickly trains a Gradient Boosting Regressor on this data. 
        
        
        You can also do 
        
        ```python
        dt, preds = DecisionTree("c", data, seed=42)
        ```
        
        To train a quick DT Classifier. 
        
        
        ## Authors
        * Karthik Bhargav 
        * Siddharth Sharma
        * Sauman Das
        * Andy Phung
        * Felix Liu
        * Anaiy Somalwar
        * Nathan Z.
        * Aurko Routh
        * Keshav Shah
        * Navein Suresh
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
