Unlocking Real-World Insights: Master K-Means and KNN through Practical Use Cases
Given: We have huge customer shopping data.
When: Business owners would like to have some actionable insights that can help to improve the business in terms of revenue
Then: Create a Model which can help to extract the details starting with the steps from inputting shopping data either through K means or KNN
What’s next: let’s try to solve the above use case through k means and KNN.
Before let’s understand some fundamental points of both
What is K means:
Unsupervised learning algorithm used for clustering that partitions data into K distinct clusters based on feature similarity.
When to Use K means
When you need to discover inherent groupings within your data.
When you have a large dataset and need a fast and scalable clustering method.
When the data is numeric since K-Means relies on mathematical means for clustering, which may not be applicable for categorical data.
Use case example: Customer Segmentation, Image segmentation, surveillance, Data preprocessing.
Implementation of flow diagram for the shopping data for K means:
