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In this walkthrough, we dive into using data science to improve understanding customers by using KMeans clustering to classify online retail customers. Leveraging powerful libraries like sklearn and pandas, we'll walk through the entire process of analyzing customer data to uncover meaningful patterns and segments.
You will learn how to:
- Import and preprocess customer data using pandas.
- Apply KMeans clustering to identify unique customer clusters.
- Visualize the clustering results to gain insights into customer behavior.
- Interpret the outcomes to drive improvement in customer experience.
Whether you're a beginner looking to expand your data science skills or an experienced analyst wanting to refresh your knowledge, this tutorial provides a comprehensive guide to KMeans clustering in Python.
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#DataScience #KMeans #Clustering #Pandas #Sklearn #Python #CustomerSegmentation #OnlineRetail #DataAnalysis
📖CHAPTERS
00:00:00 - Intro
00:02:35 - Setup
00:05:27 - Exploratory Data Analysis
00:24:23 - Data Cleaning
00:33:20 - How Does KMeans Clustering Work?
00:37:55 - Feature Engineering
01:11:59 - KMeans Clustering
01:25:40 - Cluster Analysis
01:33:31 - Outlier Analysis
01:41:34 - Visualisation
01:46:27 - Outro and Thanks!
🔗LINKS
- Online Retail Dataset: https://archive.ics.uci.edu/dataset/502/online+retail+ii
- Online Retail Mining Whitepaper: https://link.springer.com/article/10.1057/dbm.2012.17
- Github Link: https://github.com/trentpark8800/online-retail-data-clustering
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