International Journal For Multidisciplinary Research

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ML Based Finance Advisor and Budget Optimizer

Author(s) Anurag Singh, Kiran Kumar Das, Pratik Mishra, Shahbaz Siddiki, Sharan Inamadar
Country India
Abstract With the rapid change in the financial world, particularly today, the urgent demand for intelligent solutions to optimize the budget was therefore in great demand, individually as well as corporately. The paper proposes an advisory system for finance based on a cross-platform mobile application of Android and Flutter implementation of ml algorithm, which is designed to analyze users' financial data and come up with a personalized recommendation for budget optimization. The system can identify spending patterns, predict financial directions, and even propose cost-cutting initiatives by using ml algorithms that incorporate clustering and predictive analytics. It uses such core programming languages as Python in the design of the ml models and Dart through Flutter to give users a smooth, high-performance interface for Android and iOS. This makes it accessible and very easy to use to allow users to make real-time, data-driven financial decisions. The solution allows the users to maximize budgets in effective ways by reducing wastefulness and eventually leading towards financial stability. Demonstrated here is how ml-enhanced financial advisory applications have the potential to redesign personal and business finance management
Keywords ANN, Machine Learning, Flutter, Prediction
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-07
Cite This ML Based Finance Advisor and Budget Optimizer - Anurag Singh, Kiran Kumar Das, Pratik Mishra, Shahbaz Siddiki, Sharan Inamadar - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32602
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32602
Short DOI https://doi.org/g8t3h8
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