Personal Finance Manager with Expense Prediction
Project Overview Personal Finance Manager is a command-line application designed to help users track their income and expenses, analyze spending patterns, and predict future expenses using machine learning. The application stores transaction data locally and provides visualization tools to better understand financial habits. Features
Transaction Management: Add and categorize income and expense transactions with dates and descriptions Financial Summary: View overall balance, total income, and expenses broken down by category Transaction History: Display the most recent 10 transactions Data Visualization: Generate monthly expense trend charts using matplotlib Expense Prediction: Use linear regression to forecast next month's expenses based on historical data Persistent Storage: All transactions are saved to a CSV file for long-term tracking
Technologies Used
Python 3.x: Core programming language pandas: Data manipulation and CSV file handling scikit-learn: Machine learning library for expense prediction matplotlib: Data visualization for expense trends datetime: Date and time handling
Installation and Setup Prerequisites Ensure you have Python 3.6 or higher installed on your system. Step 1: Install Required Libraries Install all dependencies using pip: bashpip install pandas scikit-learn matplotlib Step 2: Download the Script Save the provided Python script as finance_manager.py in your desired directory. Step 3: Run the Application Navigate to the directory containing the script and run: bashpython finance_manager.py The application will automatically create a transactions.csv file in the same directory to store your data. How to Use When you launch the application, you'll see a menu with the following options:
Add Transaction: Record a new income or expense entry View Summary: Display total income, expenses, balance, and category-wise breakdown View Last 10 Transactions: Show recent transaction history Plot Monthly Expenses: Generate a line chart showing expense trends over time Predict Next Month Expense: Use machine learning to forecast upcoming expenses Exit: Close the application
Instructions for Testing Test Case 1: Adding Transactions
Select option 1 from the menu Add sample income transactions:
Date: 2025-01-15, Type: income, Category: Salary, Description: Monthly salary, Amount: 5000
Add sample expense transactions:
Date: 2025-01-20, Type: expense, Category: Food, Description: Groceries, Amount: 150 Date: 2025-02-05, Type: expense, Category: Rent, Description: Monthly rent, Amount: 1200 Date: 2025-02-10, Type: expense, Category: Food, Description: Restaurant, Amount: 75
Test Case 2: Viewing Summary
Select option 2 to view the financial summary Verify that total income, expenses, and balance are calculated correctly Check that expenses are grouped by category
Test Case 3: Viewing Transactions
Select option 3 to display recent transactions Verify that transactions appear in reverse chronological order
Test Case 4: Plotting Monthly Expenses
After adding transactions across multiple months, select option 4 A graph window should open displaying monthly expense trends Close the graph window to return to the menu
Test Case 5: Expense Prediction
Add transactions for at least 3 different months Select option 5 to predict next month's expenses Review the R² score and predicted amount Note: Prediction accuracy improves with more historical data
Test Case 6: Data Persistence
Add several transactions Exit the application (option 0) Restart the application Verify that all previous transactions are still available
File Structure project-directory/ │ ├── finance_manager.py # Main application script └── transactions.csv # Auto-generated data file (created on first run) Notes
All transaction data is stored locally in transactions.csv The prediction model requires at least 3 months of expense data for meaningful results Date format must be YYYY-MM-DD (or press Enter to use today's date) Transaction amounts should be positive numbers The application uses a simple linear regression model for predictions
Future Enhancements
Add budget setting and alerts Support for multiple currencies Export reports to PDF More sophisticated prediction models Database integration for better data management
Author: Manas Gursahani License: MIT Version: 1.0.0