Data Scientists and ML Engineers use clustering analysis while working with unlabelled datasets. Clustering analysis helps uncover hidden patterns, group similar data points, and drive smarter business decisions, all without needing labelled data. In this article, I’ll take you through a list of ML projects based on clustering that you should try to boost your resume.
ML Projects on Clustering for Resume
Below are some of the best ML projects based on clustering that you should try to boost your resume. All the projects mentioned below are solved & explained using Python.
Geospatial Clustering for Delivery Optimization
This project involves using clustering (like K-Means or DBSCAN) on delivery location coordinates (latitude and longitude) to group them into optimized delivery zones. These clusters can help assign orders to the nearest hub or route to minimize travel time and fuel costs.
Working on this project will show that you can work with geospatial data, clustering algorithms, and business optimization. Here’s what you need to do in this project:
- Cluster delivery points to optimize logistics
- Identify outlier deliveries (far-flung, inefficient)
- Recommend ideal delivery hubs based on centroids
Find a solved and explained example of geospatial clustering using Python here.
Smart Loan Recovery System
In this project, you’ll cluster loan defaulters based on behavior: repayment history, time since default, amount owed, employment type, and more, to prioritize recovery efforts. The idea is to separate high-risk, low-risk, and recoverable cases.
Working on this project will show your practical ML skills + domain understanding in finance and risk analytics. Here’s what you need to do in this project:
- Feature engineering from loan records
- Use clustering to group defaulters
- Map recovery strategy per cluster
Find a solved and explained example of a Smart Loan Recovery System using Python here.
User Profiling and Segmentation
Here, you need to cluster users based on behaviour: browsing activity, purchases, demographics, or usage frequency, to uncover distinct user personas for marketing, personalization, or feature rollout.
Working on this project will show that you understand unsupervised learning, business context, and how to convert raw behavioural data into strategy. Here’s what you need to do in this project:
- Identify power users vs casual users
- Find users with a high churn probability
- Create persona mapping for targeted strategies
Find a solved and explained example of User Profiling and Segmentation using Python here.
Working on these projects will require you to have a strong knowledge of Machine Learning algorithms. If you are learning ML Algorithms, my book will help you in your journey. Here are links to find the ebook and paperback versions:
Summary
So, here are some of the best ML projects based on clustering you should try to boost your resume:
- Geospatial Clustering for Delivery Optimization
- Smart Loan Recovery System
- User Profiling and Segmentation
I hope you liked this article on ML projects based on clustering you should try to boost your resume. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
