https://github.com/prashver/customer-segmentation

This project utilizes unsupervised machine learning to segment bank customers for targeted marketing campaigns. It covers tasks like data exploration, determining optimal clusters, and applying k-means for segmentation. Ideal for marketing departments in banking and retail industries.
https://github.com/prashver/customer-segmentation

customer-segmentation matplotlib numpy pandas seaborn sklearn

Last synced: 2 days ago
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This project utilizes unsupervised machine learning to segment bank customers for targeted marketing campaigns. It covers tasks like data exploration, determining optimal clusters, and applying k-means for segmentation. Ideal for marketing departments in banking and retail industries.

• Host: GitHub
• URL: https://github.com/prashver/customer-segmentation
• Owner: prashver
• Created: 2022-05-21T21:09:02.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2022-05-27T10:47:37.000Z (over 2 years ago)
• Last Synced: 2024-11-14T17:12:04.228Z (2 months ago)
• Topics: customer-segmentation, matplotlib, numpy, pandas, seaborn, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 667 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Customer-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. In this project, I have trained 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'.

The notebook covers the following tasks:

- Import libraries and datasets

- Visualize and explore datasets

- Use Scikit-Learn library to find the optimal number of clusters using elbow method

- Apply k-means using Scikit-Learn to perform customer segmentation