Performing data visualization
When applying ML techniques to analyze a dataset, the first step must be understanding the available data because every algorithm has advantages that are closely related to the underlying data. The key aspects of data that data scientists need to understand include data formats, distributions, and relationships among the features. When the amount of data is small, necessary information can be collected by analyzing each entry manually. However, as the amount of data grows, visualization plays a critical role in understanding the data.
Many tools for data visualization are available in Python. Matplotlib and Seaborn are the most popular libraries for statistical data visualization. We will introduce these two libraries one by one in this section.
Performing basic visualizations using Matplotlib
In the following example, we will demonstrate how to generate bar charts and pie charts using Matplotlib. The data we use represents the weekly distribution...
