Budget Dashboard — Analysis Features
The budget dashboard provides comprehensive financial analysis with multiple specialized modules. This guide covers all available analysis features and how to use them.
Core Analysis Modules
1. Monthly Trends Analysis
Tracks income, expenses, and savings over time with trend detection.
# Included in main analysis
python main.pyFeatures:
- Monthly income vs expenses comparison
- Net savings calculation with trends
- Transaction volume analysis
- Seasonal pattern detection
Key Insights:
- Identify months with highest/lowest spending
- Track savings rate improvements
- Detect income volatility
- Spot seasonal spending patterns
2. Category Breakdown
Comprehensive categorization of all transactions with visual breakdowns.
Categories Include:
- Food & Dining
- Transportation
- Shopping
- Entertainment
- Housing
- Utilities
- Subscriptions
- Healthcare
- And more…
Analysis Includes:
- Pie chart with percentage breakdown
- Bar chart showing absolute amounts
- Transaction count per category
- Category trend analysis over time
3. Merchant Analysis
Deep dive into spending patterns by merchant/vendor.
Features:
- Top merchants by total spending
- Top merchants by transaction frequency
- Merchant spending trends over time
- Duplicate merchant detection and standardization
Use Cases:
- Identify primary spending locations
- Find merchants to negotiate with for discounts
- Track loyalty program opportunities
- Monitor subscription services
4. Income Analysis
Detailed analysis of income sources and patterns.
# View income breakdown in main dashboard
python main.pyFeatures:
- Income source identification
- Monthly income stability analysis
- Income vs expenses correlation
- Irregular income detection
Specialized Analyzers
Subscription Tracker
Automatically detects and analyzes recurring subscription charges.
# Included in main analysis
python main.pyFeatures:
- Monthly subscription detection
- Cost per subscription analysis
- Subscription growth over time
- Identifies canceled vs active subscriptions
Detection Algorithm:
- Identifies charges recurring every 28-35 days
- Groups similar amounts from same merchants
- Excludes one-time charges and bank fees
- Confidence scoring for subscription likelihood
Gas/Transportation Spending
Specialized analyzer for vehicle-related expenses.
python main.pyFeatures:
- Weekly gas spending patterns
- Cost per fill-up analysis
- Driving frequency estimation
- Seasonal usage patterns
Use Cases:
- Tax deduction calculations
- Budgeting for fuel costs
- Comparing fuel efficiency
- Mileage estimation
Dining Out Analyzer
Dedicated analysis for restaurant and food delivery spending.
Features:
- Restaurant vs delivery spending
- Average meal costs
- Frequency analysis
- Day-of-week patterns
Utility Bills Tracking
Monitors recurring utility payments and detects anomalies.
Features:
- Monthly utility costs
- Seasonal variation analysis
- Bill increase detection
- Budget vs actual comparison
Pattern Analysis
Daily Spending Patterns
Analyzes spending behavior by day of week.
Insights:
- Weekend vs weekday spending
- Peak spending days
- Regular schedule detection
- Impulse spending identification
Seasonal Analysis
Identifies recurring seasonal patterns in spending.
Detection:
- Holiday spending spikes
- Back-to-school expenses
- Summer activity costs
- Tax season impacts
Anomaly Detection
Automatically flags unusual transactions for review.
Triggers:
- Unusually large transactions
- New merchants
- Category changes
- Spending spikes
Reporting Features
Automated Reports
Text-based reports with key insights and recommendations.
# Generated automatically
python main.pyReport Sections:
- Executive summary
- Monthly breakdown
- Category analysis
- Key insights and recommendations
- Savings opportunities
Custom Analysis
For specific analysis needs, run the main script with different configurations.
python main.pyAdvanced Features
Duplicate Detection
Automatically identifies and flags potential duplicate transactions.
Detection Methods:
- Same amount, date, and merchant
- Similar descriptions within time window
- Cross-account transfer matching
Merchant Standardization
Cleans and standardizes merchant names for better analysis.
Examples:
- “AMAZON.COM*123456” becomes “Amazon”
- “SQ *COFFEE SHOP NYC” becomes “Coffee Shop”
- “TST* RESTAURANT NAME” becomes “Restaurant Name”
Category Learning
The system learns from corrections to improve future categorization.
Process:
- Review miscategorized items with
--other - Update category mappings
- Re-run cleaning to apply changes
- System remembers patterns for future
Data Export
Export processed data for external analysis.
Available Exports:
- Clean transaction data (CSV)
- Category summaries (CSV)
- Monthly aggregates (CSV)
- Merchant analysis (CSV)
Performance Tips
Large Datasets
For datasets with 10,000+ transactions:
# Run components separately for faster iteration
python main.pyMemory Optimization
- Process data in date ranges if memory is limited
- Use
--analyzeflag to skip data reprocessing - Consider removing very old transactions
Speed Improvements
- Keep raw data files organized by date
- Use SSD storage for faster file I/O
- Run analysis on subset first to validate
Troubleshooting
Common Issues
Missing Categories:
- Add new patterns to
category_mappings.py - Use
--otherflag to review uncategorized items
Incorrect Merchant Names:
- Update merchant standardization rules
- Add regex patterns for common formats
Performance Issues:
- Process smaller date ranges
- Use
--analyzeto skip data import - Check available system memory
API Reference
For programmatic access, all analyzers can be used directly:
from scripts.analytics.budget_analyzer import BudgetAnalyzer
from scripts.analytics.subscription_analyzer import SubscriptionAnalyzer
# Load data and analyze
analyzer = BudgetAnalyzer()
analyzer.load_data()
results = analyzer.run_full_analysis()
# Access specific analysis
monthly_data = analyzer.monthly_analysis()
category_data = analyzer.category_analysis()See API Reference for complete documentation.