Today, more and more companies are using machine learning to analyze large amounts of data. It's great to be able to do this quickly and effectively, but it is also important to be able to provide information that is understandable by everyone.
Data Visualization can be an effective way to successfully explain data to those who do not have an background in data science. If the results of a complex algorithm are presented as visuals rather than an array of text and numbers, it will be much easier to understand and will make it easier to gain insights from the data.
Humans tend to largely rely on vision among all of the five senses. The same applies when looking at data, as people tend to look for visual aids to understand information. When it comes to understanding data and gaining insights from it, the right visuals can be of great help.
There are many examples where data visualization can help in real life. When trying to find a way with real-time navigation, the current road situation is often indicated by color. Jammed traffics are shown in red, while light traffic shown in blue. This enables more immediate response than when traffic is expressed in text or numbers alone. In most cases, visualizing the data in charts or graphs as well as colors can help viewers' understanding.
The following example is a visualization of bank customers' churn rate results. This model predicts which customers will cancel their services based on bank customer data. It visualizes the difference between the two distributions by showing the distribution of the balance and age of churning and non- churning customers, making it easy to see at a glance what differences exist.