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VizPool: A Python Library with Low-Code support for Basic to Advance Static & Interactive Visualizations

Overview

Welcome to getting started with VizPool; in this introduction to VizPooL, you learn how to use vizpool static API for creating static but complex visuals in one line of code.
In case you are looking for interactive visuals, please visit VizPool Interactive API

Introduction

In this notebook, you will learn how to perform EDA on the Tips dataset. You will create quite complex visuals for the EDA process by simply calling a class method on an instance of the EDA class.

In the second section, we will see how to plot machine learning model evaluation metrics by simply calling the class methods on the instance of the Evaluation class.

This notebook is divided into 3 major sections which are as follows:

  1. Installations & Imports
  2. Exploratory Data Analysis (EDA)

    a ) - Pie Bar Chart

    b ) - Box Plot

    c ) - Count Plot

    d ) - Histogram

    e ) - Bar Chart

    f ) - Violin Plot

    g ) - Correlation Heatmap

    h ) - Joint Plot

    i ) - Pair Plot
  3. Model Evaluation

    a ) - Feature Importance

    b ) - Confusion Matrix

    c ) - AUC ROC Plot

    d ) - Residual Plot

1. Installations & Imports

!pip install --upgrade --quiet vizpool 
#Restart the kernel after package upgrade
#import IPython
#IPython.Application.instance().kernel.do_shutdown(True)
import vizpool as vizpool
vizpool.__version__
'0.0.9'