This project focuses on analyzing personal finance data to understand how individuals can optimize their spending across different expense categories in order to increase their savings rate without reducing essential expenses.
Many individuals earn a stable income but still struggle to save money. This project explores spending behavior patterns and identifies which expense categories contribute most to reduced savings. The goal is to provide data-driven insights that can help improve financial decision-making.
This project is developed as part of a B.Tech (Computer Science & AI) capstone assignment, using data analytics techniques and Google Sheets for dashboard creation.
How can individuals optimize their spending across different expense categories to increase their savings rate without reducing essential spending?
- Analyze income and expense distribution across individuals
- Categorize expenses into essential and non-essential spending
- Measure savings performance using defined KPIs
- Identify areas of overspending
- Suggest optimization opportunities based on data insights
The dataset contains structured personal finance data including income, expenses across multiple categories, savings targets, and potential savings opportunities.
- Income
- Groceries
- Eating_Out
- Entertainment
- Desired_Savings
- Disposable_Income
- Potential_Savings_* columns
The dataset contains more than 10,000 rows and multiple financial variables, making it suitable for meaningful analysis and dashboard visualization.
-
Savings Rate (%) Desired_Savings ÷ Income × 100
-
Expense-to-Income Ratio (%) Total Expenses ÷ Income × 100
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Non-Essential Spending Share (%) (Eating_Out + Entertainment + Miscellaneous) ÷ Income × 100
-
Potential Savings Opportunity Sum of all Potential_Savings_* columns
- Google Sheets (Data Cleaning and Dashboard Creation)
- Pivot Tables
- Calculated Fields
- Charts and Visualizations
It is expected that higher spending in non-essential categories such as eating out and entertainment is associated with lower savings rates, even when income levels are similar. Small reductions in discretionary spending are likely to significantly improve savings without impacting essential expenses such as rent, groceries, and healthcare.
- Income vs Total Expense comparison
- Savings Rate distribution
- Essential vs Non-Essential spending breakdown
- Category-wise expense analysis
- Potential savings visualization
This project demonstrates how data analytics can be applied to personal finance to uncover actionable insights. By analyzing spending patterns and savings behavior, individuals can make more informed financial decisions and improve long-term financial stability.