https://github.com/anas436/customer-segmentation-and-generating-random-dataset-using-k-means-clustering-with-python

axes3d cluster jupyterlab kmeans matplotlib numpy pandas python3 random scikit-learn sklearn standardscaler

Last synced: 3 months ago
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Host: GitHub
URL: https://github.com/anas436/customer-segmentation-and-generating-random-dataset-using-k-means-clustering-with-python
Owner: Anas436
Created: 2022-09-06T18:03:04.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2022-09-06T18:05:23.000Z (almost 4 years ago)
Last Synced: 2025-10-26T16:03:19.317Z (9 months ago)
Topics: axes3d, cluster, jupyterlab, kmeans, matplotlib, numpy, pandas, python3, random, scikit-learn, sklearn, standardscaler
Language: Jupyter Notebook
Homepage:
Size: 252 KB
Stars: 2
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python

## Objectives

After completing this lab you will be able to:

* Use scikit-learn's K-Means Clustering to cluster data

## Introduction

There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered one of the simplest models amongst them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, it is especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you will learn how to use k-Means for customer segmentation.

Some real-world applications of k-means:

* Customer segmentation
* Understand what the visitors of a website are trying to accomplish
* Pattern recognition
* Machine learning
* Data compression

In this notebook we practice k-means clustering with 2 examples:

* k-means on a random generated dataset
* Using k-means for customer segmentation