📌 Project Overview

In today’s data-driven financial environment, organizations require real-time visibility into transaction patterns, customer demographics, and revenue trends to support informed decision-making.

This project focuses on building an interactive Credit Card Transaction Dashboard that:

• Stores structured data in a SQL database
• Models and transforms data in Power BI
• Uses DAX measures for KPIs and time intelligence
• Provides weekly, quarterly, and YTD insights

The dashboard enables stakeholders to monitor performance, identify trends, and evaluate risk efficiently.

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🖼️ Dashboard Screenshots

📊 Credit Card Transaction Dashboard

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👥 Customer Transaction Analysis Dashboard

🎯 Project Objectives

• Build an interactive Power BI dashboard for credit card analytics
• Provide insights into:
  • Revenue & interest earned
  • Transaction amount & transaction count
  • Customer demographics & behavior
  • Card category & usage patterns
• Enable Weekly, Quarterly, and YTD performance tracking
• Support data-driven business decisions

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🏗️ Project Architecture & Workflow

End-to-End Workflow

1. CSV data preparation
2. SQL database creation
3. Table design & schema definition
4. Data loading using LOAD DATA INFILE
5. Data validation & corrections
6. Power BI data modeling
7. DAX calculations & KPI creation
8. Interactive dashboard development
9. Insight extraction & reporting

flowchart LR
    A[CSV Data Collection] --> B[SQL Database Creation]
    B --> C[Table Design: cc_details & cust_details]
    C --> D[Load Data using LOAD DATA INFILE]
    D --> E[Data Validation & Cleaning in SQL]
    E --> F[Connect SQL Database to Power BI]
    F --> G[Data Modeling & Relationships]
    G --> H[DAX Measures & Calculations]
    H --> I[Interactive Dashboard Design]
    I --> J[Insights, Export & Sharing]

    %% Styles
    style A fill:#FFD54F,stroke:#F57F17,stroke-width:2px,color:#000;
    style B fill:#4FC3F7,stroke:#0277BD,stroke-width:2px,color:#fff;
    style C fill:#AED581,stroke:#33691E,stroke-width:2px,color:#000;
    style D fill:#FFCC80,stroke:#EF6C00,stroke-width:2px,color:#000;
    style E fill:#81D4FA,stroke:#01579B,stroke-width:2px,color:#000;
    style F fill:#BA68C8,stroke:#4A148C,stroke-width:2px,color:#fff;
    style G fill:#90CAF9,stroke:#0D47A1,stroke-width:2px,color:#000;
    style H fill:#CE93D8,stroke:#6A1B9A,stroke-width:2px,color:#000;
    style I fill:#FF8A65,stroke:#BF360C,stroke-width:2px,color:#fff;
    style J fill:#B39DDB,stroke:#311B92,stroke-width:2px,color:#fff;

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🗂️ Data Source

• Data stored and managed in a SQL Database
• Imported from CSV files into SQL tables
• Two primary tables:
  • cc_details – Credit card transaction data
  • cust_details – Customer demographic data

🛠️ Tech Stack & Tools Used

• Power BI – Dashboard development & visualization
• SQL (MySQL / PostgreSQL) – Data storage & querying
• DAX – Measures, KPIs & time intelligence
• Data Modeling – Relationships & schema design
• Data Visualization – KPIs, charts, slicers
• CSV Files – Raw data source

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🧱 Database Schema

🗄️ Database Creation (SQL)

CREATE DATABASE ccdb;
USE ccdb;

1️⃣ Credit Card Details Table (cc_details)

CREATE TABLE cc_details (
    Client_Num INT,
    Card_Category VARCHAR(20),
    Annual_Fees INT,
    Activation_30_Days INT,
    Customer_Acq_Cost INT,
    Week_Start_Date DATE,
    Week_Num VARCHAR(20),
    Qtr VARCHAR(10),
    current_year INT,
    Credit_Limit DECIMAL(10,2),
    Total_Revolving_Bal INT,
    Total_Trans_Amt INT,
    Total_Trans_Ct INT,
    Avg_Utilization_Ratio DECIMAL(10,3),
    Use_Chip VARCHAR(10),
    Exp_Type VARCHAR(30),
    Interest_Earner DECIMAL(10,3),
    Delinquent_Acc VARCHAR(5)
);

🔹 Customer Details Table (cust_details)

CREATE TABLE cust_details (
    Client_Num INT,
    Customer_Age INT,
    Gender VARCHAR(10),
    Dependent_Count INT,
    Education_Level VARCHAR(50),
    Maritial_Status VARCHAR(20),
    State_cd VARCHAR(50),
    Zipcode VARCHAR(20),
    car_Owner VARCHAR(10),
    House_Owner VARCHAR(5),
    Personal_Loan VARCHAR(5),
    Contact VARCHAR(20),
    Customer_Job VARCHAR(20),
    Income INT,
    Customer_Satisfaction_Score INT
);

🔄 Data Loading Process

🔹 Load Credit Card Transaction Data

LOAD DATA INFILE 'C:/ProgramData/MySQL/MySQL Server 8.0/Uploads/credit_card.csv'
INTO TABLE cc_details
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;

🔹 Load Customer Details Data

The following SQL command is used to import customer demographic data from a CSV file into the cust_details table.

LOAD DATA INFILE 'C:/ProgramData/MySQL/MySQL Server 8.0/Uploads/customer.csv'
INTO TABLE cust_details
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
IGNORE 1 ROWS;

📊 Dashboard Features

🔹 Key KPIs

• 💰 Total Revenue
• 💳 Total Transaction Amount
• 🔢 Transaction Count
• 💸 Interest Earned
• 👥 Customer Count
• ⚠️ Delinquency Rate
• ✅ Activation Rate

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📈 Key Insights

• 💰 Total Revenue: 55M
• 💳 Blue & Silver cards contribute ~93% of total transactions
• 🔄 Swipe transactions dominate overall usage
• 👥 Significant variation across age, income, and job groups
• 🌎 TX, NY & CA together contribute ~68% of total revenue
• ⚠️ Delinquent Rate: 6.06%
• ✅ Activation Rate: 57.5%

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📤 Export & Sharing

Dashboard outputs were shared as:

• PDF reports
• Power BI Service dashboards

Used for:

• Weekly performance reviews
• Business & strategic discussions

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✨ 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗙𝗶𝗹𝗲𝘀 (𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗿𝗶𝘃𝗲)

🔗 https://drive.google.com/drive/folders/16T65EOoXPGIZTcLpNTc-AbGHeKpTW9zG?usp=sharing

✨ GitHub Repository

🔗 https://github.com/shivareddy2002/Credit_Card_Transaction_Report

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🚀 Future Enhancements

• Predictive analytics for:
  • Revenue forecasting
  • Delinquency prediction
• Automated data refresh using Power BI Service
• Drill-through customer-level analysis
• Integration with Machine Learning models

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👨‍💻 Author

Lomada Siva Gangi Reddy

• 🎓 B.Tech CSE (Data Science), RGMCET (2021–2025)
• 💡 Interests: Python | Machine Learning | Deep Learning | Data Science
• 📍 Open to Internships & Job Offers

Contact Me:

• 📧 Email: lomadasivagangireddy3@gmail.com
• 📞 Phone: 9346493592
• 💼 LinkedIn 🌐 GitHub 🚀 Portfolio

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