Unsupervised Machine Learning for Customer Market Segmentation

Overview

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In this hands-on guided project, we will train unsupervised machine learning algorithms to perform customer market segmentation. Market segmentation is crucial for marketers since it enables them to launch targeted ad marketing campaigns that are tailored to customer's specific needs. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Syllabus

Unsupervised Machine Learning for Customer Segmentation

In this hands-on project, we will train an unsupervised machine learning algorithm to perform bank customer segmentation. This project could be practically applied at any marketing department in the banking and retail industries to segment customers into 'clusters' or 'groups'. In this hands-on project we will go through the following tasks: (1) Understand the problem statement and business case, (2) Import libraries and datasets, (3) Visualize and explore datasets, (4) Understand the theory and intuition behind k-means clustering machine learning algorithm, (5) Learn how to obtain the optimal number of clusters using the elbow method, (6) Use Scikit-Learn library to find the optimal number of clusters using elbow method, (7) Apply k-means using Scikit-Learn to perform customer segmentation, (8) Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization.