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How to Visualize Your Model's Analytical Results: Hidden Groupings, Data Classifications, and Outliers

Visualization of the results of your predictive analysis really helps the stakeholders understand the next steps. Here are some ways to use visualization techniques to report the results of your models to the stakeholders.

How to visualize hidden groupings in your data

Data clustering is the process of discovering hidden groups of related items within your data. In most cases, a cluster (grouping) consists of data objects of the same type such as social network users, text documents, or e-mails.

One way to visualize the results of a data-clustering model is a graph that represents social communities (clusters) that were discovered in data collected from social network users. The data about customers was collected in a tabular format; then a clustering algorithm was applied to the data, and the three clusters (groups) were discovered: loyal customers, wandering customers, and discount customers.

Here the visual relationship among the three groups already suggests where enhanced marketing efforts might do the most good.

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How to visualize data classification results

A classification model assigns a specific class to each new data point it examines. The specific classes, in this case, could be the groups that result from your clustering work. The output highlighted in the graph can define your target sets. For any given new customer, a predictive classification model attempts to predict which group the new customer will belong to.

After you've applied a clustering algorithm and discovered groupings in the customer data, you come to a moment of truth: Here comes a new customer — you want the model to predict which type of customer he or she will be.

Here is one example of how a new customer's information is fed to your predictive analytics model, which in turn predicts which group of customers this new customer belongs to. New Customers A, B, and C are about to be assigned to clusters according the classification model.

Applying the classification model resulted in a prediction that Customer A would belong with the loyal customers, Customer B would be a wanderer, and Customer C was only showing up for the discount.

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How to visualize outliers in your data

In the course of clustering or classifying new customers, every now and then you run into outliers — special cases that don't fit the existing divisions.

In this example, a few outliers don't fit well into the predefined clusters. Six outlier customers have been detected and visualized. They behave differently enough that the model can't tell whether they belong to any of defined categories of customers. (Is there such a thing as, say, a loyal wandering customer who's only interested in the discount? And if there is, should your business care?)

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