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PunditKit: A GUI for Scikit-Learn Models

Project description


Simplify. Visualise. Learn.


PunditKit is a toolkit for training machine learning models on tabular data simply and without code. It is intended for students and experienced data scientists who are looking to quickly obtain some initial predictive insights from data.

Included Features:

  • Machine learning using scikit-learn
  • Interpretable explanations of predictions using lime
  • Exploratory data summaries and model performance on holdout data for checks
  • Partial dependence plots

PunditKit is under active development. The goal is to develop an opinionated modelling framework with best practice modelling and visualisation. If you encounter any problems, please raise an issue.

Installation (via pip)

PunditKit is developed using Python. First download and install a Python 3.x distribution such as Anaconda

PunditKit can then be installed using pip from the command line (if pip is added to PATH during installation) or using Anaconda prompt.

pip install punditkit

This adds the punditkit command.

Modelling a dataset

Suppose you have a file called iris.csv that you would like to model.

To use punditkit on the dataset, run:

punditkit iris.csv

Currently only Comma Separated Values (CSV) datasets are supported. For Excel spreadsheets, please Save As CSV to analyse using punditkit.

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Files for punditkit, version 0.0.1
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