Overview
Clustering is an unsupervised learning technique that groups similar data points together based on certain similarities.
K-means is one of the simplest and most popular clustering algorithms that partitions data into K distinct clusters based on distance to the centroid of a cluster.
Key Concepts
- Unsupervised learning principles
- K-means algorithm steps
- Determining the optimal number of clusters
- Silhouette score and elbow method
- Limitations of K-means
Practice Exercise
Exercise: Customer Segmentation
Using a retail customer dataset:
- Preprocess and normalize the data
- Determine the optimal number of clusters using the elbow method
- Apply K-means clustering
- Visualize the clusters in 2D or 3D
- Interpret the characteristics of each customer segment
Resources
YouTube Guide
Main resource for today
K-means Clustering
In-depth explanation with Python code
Finding the Optimal K
Methods to determine the best number of clusters
Clustering Metrics
Evaluating clustering performance
Complete Today's Task
Mark today's task as complete to track your progress and earn achievements.