cerbo 0.0.1

Perform Efficient ML/DL Modelling

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

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

gb, preds = Boosting("r", data, algo="gb", seed=42) 

Which quickly trains a Gradient Boosting Regressor on this data.

You can also do

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