🚀 Supervised Learning for Customer Segmentation using KMeans Clustering
📌 Project Overview
This project focuses on customer segmentation and provides personalized banking recommendations based on customer financial behavior. Using KMeans clustering, customers are grouped into distinct segments, and tailored financial services are recommended for each segment. The goal is to offer personalized banking services to enhance customer engagement and satisfaction.
📂 Dataset
- Features: Account Balance, Loan Amount, Interest Rate, Credit Limit, Rewards Points
- Total Customers: 10,000
- Number of Segments: 5
- Clusters:
1️⃣ High-Value Investors (Wealth Builders)
2️⃣ Credit Reliant Borrowers
3️⃣ Stable Savers
4️⃣ High-Spending Borrowers
5️⃣ Low Engagement Customers
🛠️ Methodology
✅ Data Preprocessing: Data cleaning, normalization, and handling missing values.
✅ Clustering Method: KMeans Clustering (with 5 clusters) to segment customers.
✅ Feature Engineering: Account Balance, Loan Amount, Interest Rate, Credit Limit, Rewards Points.
✅ Recommendations: Tailored banking services based on cluster type.
✅ Evaluation: Visualized using cluster-specific data, with interpretation of customer behavior in each cluster.
📊 Results & Performance
Best Segmentation Result: 5 distinct customer clusters
Cluster Details:
1️⃣ High-Value Investors: Wealth management services, premium banking offers.
2️⃣ Credit Reliant Borrowers: Debt consolidation, low-interest loans, credit score improvement.
3️⃣ Stable Savers: High-interest savings accounts, mid-risk investment opportunities.
4️⃣ High-Spending Borrowers: Personalized loan products, credit card rewards.
5️⃣ Low Engagement Customers: Simple banking products, financial wellness tools.
🚀 How to Run
1️⃣ Clone the repository
git clone https://github.com/your-username/customer-segmentation-recommendations.git cd customer-segmentation-recommendations
2️⃣ Install dependencies pip install -r requirements.txt
3️⃣ Run the script python customer_segmentation.py
📌 Future Improvements 🏆 Try other clustering algorithms like DBSCAN or Hierarchical Clustering. 📊 Use more advanced feature engineering and dimensionality reduction techniques like PCA. ☁️ Deploy the model: Integrate recommendations into a real-world banking system using Flask/FastAPI.