Predictive Analytics and Segmentation using Clustering in data science

Clustering in data science

What is predictive analytics? As the name suggests, predictive analytics analyzes the data history and predicts the outcome for the future. As the data became more extensive, the importance of predictive analytics increased manifolds. Earlier handling big data using human-handled algorithms was a very tedious and time-consuming task, but with artificial intelligence, machine learning, and Clustering in data science, handling huge data is no scarier. Today predictive analytics is doing wonders and achieving unprecedented goals in many industries like banking, healthcare, dating, astrology, and many more. Predictive analytics is a very strong computing tool. By prioritizing the data, predictive analytics aims to bring desired outcomes. Predictive analytics is no threat to human’s capability of planning, strategizing, and the art of creatively executing the solutions but is a great aid.

Data Clusters and predictive analytics, Clustering in data science

Data Cluster is an algorithm that is based on machine learning. Based on similar characteristics, it groups the data into certain data sets. Predictive analytics is used to predict a particular cluster set’s future outcome and behavior. Various methods do clustering. 

Clustering Algorithms, Clustering in data science

In business terminology, a Clustering algorithm can be understood as a method that helps the consumer segmentation. Consumer segmentation means a process of identifying similar consumers in the same segment. The clustering algorithm helps to better understand consumers regarding both static demographics and dynamic behaviors. A consumer with similar characteristics often tends to interact with the business in the same way. Thus a business can benefit from this technique by creating a specially tailored marketing strategy for each segment. Thus clustering algorithms prove to be of a huge benefit.

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Let’s talk about data science, then in data science. A clustering algorithm is explained as an unsupervised machine learning algorithm that identifies groups of data points that are closely related. 

Segmentation and clustering

In predictive analysis, sometimes, clustering and segmentation are confused with each other. But to put it in clear perspective, we should know that segmenting is the process of putting consumers into groups based on similarities. In contrast, clustering is the process of finding out the similarities in consumers so that they can be grouped and hence segmented. They might seem a lot similar, but they are not quite the same.

Segmentation- In segmentation, we know who our target is. For example, if we want to sell an expensive dress, our target is women with high household income and who show trends of purchase history in that product category in the past. Hence, the identification and group making of women who have a high income is called segmentation. Later a customized market was constructed for this segment.

Clustering- Clustering is the process of using machine learning and algorithms to identify different types of data and how they are related. Based on this relationship, new segments are created. Clustering detects the relationship between data points in order to segment them. Clustering the data helps us to discover new segments of customers on the basis of their buying behavior. The Customer Data Platform employs clustering models and data sets to predict a customer’s likelihood to make a purchase. Professionals nowadays are taking up pg data science courses online in order to enhance their skills and understand their markets better.

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Segmentation and cluster analysis

Clustering is a technique to stack the data into various segments. Each segment has similar data, whereas, with different segments, data differ. Analysis by means of clustering has numerous applications. Some of its major applications are seen in identifying customer segments or competitive sets of the products, products having co- risen, segmentation of geo-demographic data, etc. 

Clustering and segmentation are done in the following steps:

  • Data must be in metric
  • Data must be scaled
  • Segmentation variables are selected
  • Similarity measures are defined
  • Pairwise distances are visualized
  • The number of segments and methods are assessed
  • Profiling and interpretation of the segments
  • Analysis of robustness

K Means Clustering

K means clustering is one of the simplest unsupervised machine learning algorithms. This popular center-based clustering algorithm aims to assign data points to clusters based on distances. 

Working of K means clustering can be understood with these following procedures:

  • The number of clusters are specified
  • Assigning centroid to each cluster by initiating K random centroids
  • Assign data points to the closest centroids and form K  clusters
  • Computation of the centroids for each cluster
  • Centroids stop changing and reach convergence


Predictive analysis and segmentation using clusters in data science is a knowledge that adds as a key skill in every concerned professional and aspiring professional’s resume. Many are taking up PG courses in data science to rise up the ladder of success in their respective fields. All the world-class universities worldwide are providing online courses that the students and the working professionals can take up readily to enhance their skills.

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