data-dashboard 0.1.1

Dashboard to explore the data and to create baseline Machine Learning model.

data-dashboard

Short description

Creates a simple static HTML dashboard with provided X, y data to help users see what's going on in their data, assists in making decisions regarding features and finds the best "baseline" Machine Learning Model.

Longer Description

Installation and Usage

data_dashboard library allows you to build HTML Dashboard visualizing not only the data and relationships between features but also automatically search for the best 'baseline' sklearn compatible Model.

You can install the package via pip:

pip install data-dashboard

To create a Dashboard you need the data: X, y and the output_directory where the HTML files will be placed. You can use toy datasets from examples (e.g. iris dataset) included in the library as well:

from data_dashboard import Dashboard
from data_dashboard.examples import iris
output_directory = "your_path/dashboard_output"
X, y, descriptions = iris()

# descriptions is additional argument described further in docs
dsh = Dashboard(X, y, output_directory, descriptions)
dsh.create_dashboard()

Created HTML Dashboard will contain 3 subpages for you to look:

• Overview with summary statistics of the data
• Features where you can dig deeper into each feature in the data
• Models showing search results and models performances

Description

data_dashboard aims to help you in those initial moments when you get your hands on new data and you ask yourself a question "Now what?".

Instead of going through Jupyter Notebook tables and visualizations, data_dashboard gathers all that information in one place in a user-friendly interface. What is more, automated 'baseline' sklearn Model is created - you can then adjust not only parameters and algorithms, but also see how your changes affect the performance.

There is also an educational twist to data_dashboard, as explanations on correlations and transformations might alleviate pains that beginner Data Scientists might encounter (e.g. what do Transformers really do with my features?). Furthermore, provided visualizations might also assist in manual feature engineering.

Last but not least, data_dashboard tries to put more emphasis on the design of both HTML and Visualization elements. If you are not the biggest fan of default matplotlib plots, then you can give data_dashboard a try - perhaps the styling will suit your taste!

Working Example

Example with Titanic dataset can be found here: https://example-data-dashboard.herokuapp.com/

Documentation

Documentation can be found here: https://data-dashboard.readthedocs.io/

Author

MIT License Copyright (c) 2021 Maciej Dowgird

For contact use: [e-mail]