customer_segment_prediction

K-Means Clustering

K-Means clustering is an unsupervised machine learning algorithm that divides the given data into the given number of clusters. Here, the “K” is the given number of predefined clusters, that need to be created.It is a centroid based algorithm in which each cluster is associated with a centroid. The main idea is to reduce the distance between the data points and their respective cluster centroid. The algorithm takes raw unlabelled data as an input and divides the dataset into clusters and the process is repeated until the best clusters are found. K-Means is very easy and simple to implement. It is highly scalable, can be applied to both small and large datasets. There is, however, a problem with choosing the number of clusters or K. Also, with the increase in dimensions, stability decreases. But, overall K Means is a simple and robust algorithm that makes clustering very easy

Mall Customer Data:

Mall Customer data is an interesting dataset that has hypothetical customer data. It puts you in the shoes of the owner of a supermarket. You have customer data, and on this basis of the data, you have to divide the customers into various groups.

The data includes the following features:

1. Customer ID

2. Customer Gender

3. Customer Age

4. Annual Income of the customer (in Thousand Dollars)

5. Spending score of the customer (based on customer behaviour and spending nature)