Customer Segmentation using K-Means Clustering

Overview

This project was developed as part of the Machine Learning Internship at SkillCraft Technology. The objective of this project is to segment customers into different groups based on their annual income and spending behavior using K-Means Clustering.

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Dataset

Dataset Used: Mall Customer Segmentation Dataset

Source:
https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python

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Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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Features Used

• Annual Income (k$)
• Spending Score (1-100)

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Project Workflow

1. Data Collection
2. Data Analysis
3. Feature Selection
4. Elbow Method
5. K-Means Clustering
6. Data Visualization

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Concepts Used

• Unsupervised Learning
• K-Means Clustering
• Elbow Method
• Customer Segmentation

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Results

The K-Means clustering algorithm successfully segmented customers into different groups based on spending patterns and annual income. The visualization clearly represented different customer categories and cluster centroids.

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Conclusion

This project helped in understanding the practical implementation of clustering techniques, customer segmentation, and unsupervised machine learning using Python.

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Author

Sowmya Bakka
Machine Learning Intern - SkillCraft Technology