Overview

This project analyzes a sample personal finance dataset representing a single individual. The dataset is a case study to demonstrate income tracking, expense analysis, savings evaluation, and target-based financial monitoring. It does not represent my personal financial data.

Methodology

Financial transactions were structured by year, month, type (Income, Expense, Savings), category, and value. Using Power BI and DAX, key metrics were modeled, including total income, total expenses, total savings, savings rate, and savings target variance. Interactive visuals were designed to support trend analysis, allocation breakdowns, and performance monitoring.

Key Insights

• Total Income: ₦1,507,500

• Total Expenses: ₦1,182,000

• Total Savings: ₦325,500

• Expenses account for 78.41% of income, while savings represent 21.59%.

• Savings exceeded the target of ₦301,500 by approximately 8%.

• Expense concentration is driven mainly by fixed costs:

• House Rent: 40.44%

• Groceries & Food: 24.20%

• EMIs: 21.40%

• Discretionary expenses remain minimal (below 8%).

• Savings allocation favors long-term instruments:

• Mutual Funds: 71.58%

• Emergency Fund: 14.44%

• Fixed Deposit: 13.67%

• Liquid Cash: 0.31%

Interpretation

The analysis shows that high expense ratios are primarily necessity-driven rather than discretionary. Despite this structure, savings discipline remains strong, supported by consistent tracking against predefined targets.

Outcome

The dashboard provides clear financial visibility, supports informed decision-making, and demonstrates how structured analysis of can improve financial awareness and planning.