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.
Installing Cerbo:
pip install cerbo
or
python -m pip install cerbo
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.
- Karthik Bhargav
- Siddharth Sharma
- Sauman Das
- Andy Phung
- Felix Liu
- Anaiy Somalwar
- Nathan Z.
- Aurko Routh
- Keshav Shah
- Navein Suresh
- Ayush Karupakula