Project Overview

Credit Card Customer Segmentation Analysis
I have used data-driven techniques to segment credit card customers and develop targeted marketing strategies.

Objective: Analyze 8,950 credit card customers to identify distinct segments and create actionable personas

Methodology
1.K-means clustering
2.Principal Component Analysis (PCA)
3.behavioral pattern analysis

Data
Credit card usage data includes:
1.Balance
2.Purchases
3.Cash advances
4.Credit utilization

Key Components
1.Data Preprocessing: Cleaning, handling missing values, and feature engineering
2.EDA: Visualize distribution and pattern in customer behavior; 
3.Feature Engineering: Create relevant features such as credit utilization
4.Dimensionality Reduction: Apply PCA for efficient clustering
5.K-means Clustering: Segmentation of customers into distinct groups
6.Visualization: Use UMAP for cluster visualization; Persona Creation
7.Developing the detailed customer personas based on cluster analysis
8.Key findings: 4 distinct customer segments with unique behavioral patterns identified; Key insights on credit utilization, balance distribution, and purchase behavior identified; Created actionable personas for targeted marketing strategies.

Repository Structure
1.code_customersegmentationbankingdata.py: Main Python script containing the analysis
2.Customer-segmentation-in-banking.pptx: Presentation of findings and recommendations
3.README.md: Overview of the project and usage instructions
4.Visualizations
5.Dataset