Credit Card Analysis Dashboard 📊

Project Overview 🚀

This project focuses on Credit Card Usage Data Analysis and provides insights into customer transactions, age groups, and total revenue generated. The analysis is done using SQL, DAX, and Power BI, where the processed data is visualized through interactive dashboards. The data is sourced from SQL, processed using DAX formulas, and represented with Power BI dashboards.

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Dataset 📁

The dataset consists of credit card transactions and customer details from various CSV files. The key CSV files used are:

• cc_add.csv
• credit_card.csv
• cust_add.csv
• customer.csv

Data Preparation 🛠️

• Imported raw data from CSV files.
• Cleaned and processed the data.
• Created relevant SQL tables and relations in SQL Server.
• Exported clean data to SQL for analysis and used for Power BI visualization.

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Data Processing and DAX 🧮

The data processing involved creating relevant metrics using DAX (Data Analysis Expressions) within Power BI. Below are some of the key DAX Queries implemented:

Last Week's Transaction Amount:

Last_Week_Amt = 
CALCULATE( 
   SUM('Credit Card Details (2)'[Total_Trans_Amt]), 
   FILTER( 
      ALL('Credit Card Details (2)'), 
      'Credit Card Details (2)'[Week_Num2] = MAX('Credit Card Details (2)'[Week_Num2]) - 1 
   ) 
)

This Week's Transaction Amount:

This_Week_Amt = 
CALCULATE( 
   SUM('Credit Card Details (2)'[Total_Trans_Amt]), 
   FILTER( 
      ALL('Credit Card Details (2)'), 
      'Credit Card Details (2)'[Week_Number] = MAX('Credit Card Details (2)'[Week_Number]) 
   ) 
)

Revenue Calculation:

Revenue = 
SUM('Credit Card Details'[Total_Trans_Amt]) + 
SUM('Credit Card Details'[Annual_Fees]) + 
SUM('Credit Card Details'[Interest_Earned])

Customer Age Group Categorization:

Age_Group = 
SWITCH( 
   TRUE(), 
   'Customer Details'[Customer_Age] < 30, "20-30", 
   'Customer Details'[Customer_Age] >= 30 && 'Customer Details'[Customer_Age] < 40, "30-40", 
   'Customer Details'[Customer_Age] >= 40 && 'Customer Details'[Customer_Age] < 50, "40-50", 
   'Customer Details'[Customer_Age] >= 50 && 'Customer Details'[Customer_Age] < 60, "50-60", 
   'Customer Details'[Customer_Age] >= 60, "60+", 
   "Unknown" 
)

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Dashboards and Insights 🖥️

The final Power BI Dashboard contains several visuals including:

• Revenue Analysis: Showcases total revenue generated through transactions, annual fees, and interest earned.
• Transaction Trends: Visualizations to track weekly transactions and compare them with the previous week's data.
• Customer Segmentation: Breakdown of customers by age groups to analyze how different age brackets use credit cards.

These insights provide valuable information for business strategies and decision-making.

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Files 📦

• CreditCardDashboard.pdf: Documentation of the Power BI dashboard.
• CreditCardInsightsPPT.pdf: PowerPoint presentation with insights and findings.
• Credit_card_analysis.pbix: Power BI file containing the full analysis and visualizations.
• README.md: Project documentation.
• CSV Files: Raw data files for analysis.
  • cc_add.csv
  • credit_card.csv
  • cust_add.csv
  • customer.csv