Customer Segmentation Using Machine Learning
The GitHub repository for this project can be viewed here.
Clustering models categorize customers in the dataset based on purchasing behavior, preferences, and demographics. They leverage unsupervised machine learning to help businesses:
📈 Enhance marketing strategies — Target promotions and campaigns for specific customer groups.
🧠 Understand customer behavior — Reveal underlying patterns in purchasing habits.
⚙️ Optimize resource allocation — Focus on high-value customer segments.
📶 Drive growth through data — Inform strategic decisions with data-backed insights.
Project Overview
The project uses the Mall Customers dataset containing demographic and spending data, available on Kaggle.
Key Features:
- Data preprocessing — Cleaning, handling missing values, and scaling features for uniformity.
- Multiple clustering techniques:
- K-Means — Balanced segmentation for quick insights.
- Agglomerative Hierarchical Clustering — Dendrogram-based, revealing nested customer group structures.
- DBSCAN — Detects arbitrary-shaped clusters and identifies outliers.
- Visualization — Clear, color-coded cluster plots for easy interpretation.
- Evaluation metrics — Silhouette Score to assess cluster quality.
Findings & Insights
- K-Means identified 5 distinct customer groups ranging from high-income high-spenders to low-income budget-conscious shoppers.
- Agglomerative Clustering produced similar groupings but highlighted closer relationships between certain segments, useful for hierarchical targeting.
- DBSCAN successfully isolated a small set of outlier customers with unique spending patterns that could be targeted for special retention campaigns.
- Demographics such as age and annual income were strong differentiators, while spending score played a key role in segment separation.
Business Impact
By implementing Clustering models, businesses can:
- Create personalized marketing campaigns tailored to each segment.
- Identify premium customers for loyalty programs.
- Recognize under-served groups and tailor services to meet their needs.
- Allocate resources more effectively by targeting profitable clusters.
Tools & Libraries Used
- Python — Core programming language
- NumPy & Pandas — Data manipulation
- Matplotlib & Seaborn — Visualization
- Scikit-Learn — Clustering algorithms & evaluation metrics
Next Steps
- Integrate real-time segmentation for streaming customer data.
- Experiment with Gaussian Mixture Models for probabilistic clustering.
- Deploy the model as a web dashboard for business stakeholders.
