🏦 Banking Dashboard - End-to-End Data Analysis Project

This project focuses on building an interactive Banking Dashboard using Power BI. It involves the complete data analysis lifecycle — from data cleaning and transformation to exploratory data analysis (EDA) and visualization.

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📌 Project Workflow

Data ➡️ MySQL ➡️ Data Cleaning & Preparation ➡️ EDA ➡️ Power BI Dashboard

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📊 Dataset Information

• Number of columns: 24
• Stored in: MySQL

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🔧 Steps Involved

1. Data Cleaning & Preparation

• Categorized Income into bands:
  • Low
  • Mid
  • High
• Standardized gender, nationality, and other categorical variables.
• Used conditional columns in Power BI to create income bands.
• Replaced branch codes ('1', '2', etc.) with readable branch names.
• Mapped gender codes:
  • '1' → Male
  • '2' → Female

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2. Exploratory Data Analysis (EDA)

• Categorical analysis on:
  • Gender
  • Nationality
• Numerical analysis on:
  • Credit Card Balance
  • Bank Loans
  • Bank Deposits
  • Checking Account
  • Saving Account
  • Estimated Income
  • Superannuation Savings

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3. Key Insights from EDA

• Strong positive correlation between:
  • Bank Deposits, Checking Account, Saving Account, and Foreign Currency Account.
• Customers with high balance in one account type tend to hold substantial funds in other accounts as well.

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📈 Dashboard Pages (Power BI)

1. Home
2. Loan Analysis
3. Deposit Analysis
4. Summary

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🚀 Tools & Technologies

• Database: MySQL
• Visualization: Power BI
• Languages: SQL, DAX (in Power BI)

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🧠 Learnings

• Data wrangling using SQL
• Power BI conditional columns
• Deriving insights through EDA
• Building multi-page dashboards for presentation

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✅ Page 1: Home

Overview of the banking data with summary statistics and key visuals.

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✅ Page 2: Loan Analysis

Insights into loan distribution, types, and customer segments.

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✅ Page 3: Deposit Analysis

Breakdown of account balances, deposit types, and correlation patterns.

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✅ Page 4: Summary

Final insights from EDA, including correlations and demographic trends.