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@article{174127,
        author = {Senapathi Bhanu Teja and Godi Brahmaji and Datla Vasantha Varma and Konduru Rohith and Munnangi Vijay Kumar},
        title = {A Smart Financial Management Application for Comprehensive Insights and Effective Budgeting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2761-2767},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174127},
        abstract = {This project introduces a personal finance application that leverages machine learning and data visualization to enhance financial decision-making for users, particularly individuals. Utilizing predictive analytics, the application identifies spending patterns, forecasts expenses, and provides personalized financial advice, enabling users to anticipate and manage future financial trends effectively. Transactional data, including "Category," "Subcategory," "Credit/Debit," and "Transaction Type," undergoes one-hot encoding, while transaction amounts are standardized to optimize clustering analysis through K-Means. The Elbow method and Silhouette scores validate cluster effectiveness, ensuring accurate financial segmentation. Dynamic data visualizations, including scatter plots and Principal Component Analysis (PCA), transform financial insights into accessible, actionable intelligence. The application's interactive design facilitates budgeting, expense tracking, and savings goal management, fostering financial literacy and informed decision-making. By integrating sophisticated analytical techniques with an intuitive user interface, this project delivers a proactive financial management experience, empowering users to cultivate budgeting discipline, enhance savings strategies, and develop long-term financial stability.},
        keywords = {Financial management, Machine learning, Data visualization, Budgeting, Predictive analytics.},
        month = {March},
        }