The Expense Management App is a full-stack web application designed to enhance personal finance management through advanced technology integration.
It offers tools to track expenses, categorize spending, and provide actionable insights based on AI-driven analytics.
This project serves as a learning platform for integrating Vue.js, Java, and various APIs.

• Gain proficiency in Vue.js fundamentals and advanced features for SPA development.
• Understand and get a feeling for UI/UX design and implementation.

• Deepen understanding of Java and Spring for creating robust backends.
• Learn a bit about CyberSecurity, safe logins and password storage, user authentication.
• Implement secure RESTful APIs.
• Utilize Hibernate for efficient database operations and transaction management.
• Apply Java’s concurrency and collections frameworks to optimize backend performance.

• Seamlessly integrate the Vue.js frontend with the Java Spring backend.
• Implement comprehensive error handling and data validation across the stack to ensure reliability and security.
• Optimize the interaction between the frontend and backend with efficient API calls and data transfer.

• Interactive Expense Logging: Users can easily log and categorize their expenses, with instant updates and edits.
• Budget Management: Set and track monthly budgets across various categories, with visual indicators of budget health.
• Financial Insights: Leverage AI to provide personalized financial advice and predictive spending trends.
• Responsive Dashboard: A fully responsive dashboard that adapts to different devices, enhancing user experience.

• Python/Flask: Supports AI functionalities, particularly for analyzing financial data to derive insights, as part of exploring AI's potential in web applications.
• Integrate natural language processing for categorizing expenses based on user input.
• Apply data analysis libraries like Pandas and NumPy to analyze and manipulate financial data.
• Experiment with Python's AI libraries such as TensorFlow or PyTorch for more advanced predictive models.