K-Means Clustering

Explore k-means clustering to group similar data points without labels, implement the algorithm step-by-step, and assess cluster quality using silhouette scores to improve unsupervised learning skills.

We'll cover the following...

Implementing k-means clustering
Sample answer
Evaluating cluster quality
- Sample answer

K-means clustering is a fundamental tool in unsupervised learning for grouping similar data points without prior labels. In this lesson, we'll practice implementing the algorithm step-by-step, understand how to form meaningful clusters, and evaluate clustering performance with silhouette scores. Let’s get started.

Implementing k-means clustering

You are given a dataset containing various data points representing customer transactions. Your task is to group these transactions into different clusters based on their similarities using the k-means clustering algorithm. The dataset is represented as a list of tuples, where each tuple contains transaction details like the amount spent and the number of items purchased.

Implement a function k_means_clustering(data, k) that clusters the given dataset into k clusters using the k-means algorithm. The function should return a list of clusters, where each cluster is a list of transaction points. ...

1.Getting Started

2.Handling Diverse Real-World Data

3. Preparing and Transforming Data for Machine Learning Pipelines

4.Understanding Supervised Learning Algorithms

5.Understanding Unsupervised Learning Algorithms

6.Advanced Machine Learning Concepts

7.ML Applications and Deployment in the Real World

8.Responsible Machine Learning: Ethics, Fairness, and Privacy

9.ML Interview Preparation and Case Studies

K-Means Clustering

Implementing k-means clustering