Why This Project Stands Out: A complete end-to-end Power BI solution that transforms raw credit card transaction data into actionable business insights through interactive dashboards, KPI tracking, and customer behavior analysis.
This project demonstrates an end-to-end Power BI dashboard analyzing credit card transactions, covering data integration, transformation, modeling, DAX measures, and interactive visualization. The dashboard enables tracking of revenue, customer behavior, transaction trends, and card category performance, helping financial institutions make data-driven decisions.
- Build a dynamic, interactive Power BI dashboard for credit card analysis
- Implement a full data analytics pipeline: SQL → Power BI → Insights
- Calculate KPIs using DAX measures for accurate performance tracking
- Provide actionable insights to optimize customer retention, revenue growth, and card performance
| Dataset/Table | Description |
|---|---|
Customer |
Customer demographics (age, gender, income group, marital status) |
Cust_Address |
Customer location (city, state, country) |
Credit_Card |
Card type, limits, status, category |
Transaction |
Transaction ID, amount, payment mode, date |
- Data Source & Connection: Imported datasets from SQL and CSV files. Connected Power BI to a relational database for efficient querying.
- Data Cleaning & Transformation: Used Power Query Editor to filter, merge, rename, and standardize data. Handled missing values, duplicates, and data type inconsistencies.
- Data Modeling: Built relationships between fact (
Transaction) and dimension (Customer,Credit_Card) tables. Designed a Star Schema for optimized analysis. - DAX Measures: Key metrics: ``` Total Revenue = SUM(Transaction[Revenue]) Average Transaction Value = DIVIDE([Total Revenue], [Transaction Count]) Monthly Growth = ([ThisMonth] - [LastMonth]) / [LastMonth] ```
- Dashboard Design: Interactive visuals include bar charts, donut charts, line charts, KPI cards, and matrix visuals. Filters/slicers for customer, time, and card category. Professional and consistent color palette.
| Category | Metric | Description |
|---|---|---|
| 💰 Revenue | Total Revenue | Sum of all credit card revenue |
| 💳 Transactions | Total Transaction Amount | Total transaction value |
| 🧾 Transactions | Transaction Count | Number of transactions |
| 📈 Revenue | Interest Earned | Total interest generated |
| 👥 Customers | Customer Count | Active cardholders |
| 📊 Trends | Quarterly/Monthly Trends | Revenue & spending growth |
| 💎 Card Category | Insights by Type | Revenue breakdown by card type |
| 🧔 Demographics | Customer Insights | Spend by gender, age, income group |
- High-value customers: Top 10% contribute ~45% revenue → target retention campaigns.
- Card performance: Platinum & Gold cards drive highest revenue.
- Customer behavior: 25–35 age group transacts most → marketing focus.
- Revenue growth: Healthy quarter-over-quarter increase.
- Interest income: Significant contributor → prioritize high-interest products.
Shows customer metrics: total spend, active users, category distribution
Displays revenue, transaction counts, and trends
Live Project Video: YouTube Full Power BI Project
| Challenge | Solution |
|---|---|
| Inconsistent data | Standardized columns, removed nulls, validated relationships |
| Dashboard performance | Optimized DAX, created aggregated tables |
| Complex KPI calculations | Step-by-step DAX measures for accuracy |
| Visual clarity | Consistent color scheme, labeling, and slicers |
| Tool / Technology | Purpose |
|---|---|
| Power BI Desktop | Dashboard development and visualization |
| SQL Database | Data storage and queries |
| DAX | KPI & measure calculations |
| Power Query (M) | Data cleaning and transformation |
- Inspired by: YouTube Power BI Full Project
- Dataset: Kaggle Credit Card Dashboard
- Developed by: Shradha Pol
👤 Shradha Pol