Mazen Asag

Project Details

💼 Customer Segmentation Using Clustering



📌 Problem Statement
The goal is to segment banking customers into distinct groups based on behavioral and financial data. This segmentation helps businesses better understand their customers and tailor services accordingly.

📊 Data Collection
Dataset Source: Banking CRM customer data (synthetic or anonymized)

Features Used:

• Age: Customer’s age
• Income: Annual income
• Spending Score: Behavior-based spending metric
• Other financial/behavioral metrics: (as included in the dataset)

🔍 Methods Applied:

• Data Preprocessing (scaling, cleaning)
• Feature reduction using PCA to reduce dimensionality
• Customer grouping via KMeans clustering
• Cluster validation using Davies-Bouldin Index = 0.81

📈 Results:
- Optimal number of clusters: 4
- Distinct groups identified based on income and spending score
- Useful insights for targeted marketing

🛠️ How to Run the Project:
1. Clone the repository:
git clone https://github.com/Mazenasag/Banking-Solutions-Customer-Segmentation-
2. Navigate to project folder:
cd customer-segmentation
3. Install dependencies:
pip install -r requirements.txt
4. Run the analysis:
python main.py

🚀 Future Enhancements:
- Visualize clusters using t-SNE
- Deploy as a dashboard using Streamlit
- Integrate with CRM system for real-time segmentation

👥 Contributor: Mazen Asag
📜 License: MIT License

Explore more insights by segmenting your customers efficiently! 🚀