Project Overview

Project Overview

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+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