SmartSave: A Comprehensive, Machine Learning-Driven Financial Analysis, Clustering, and Visualization Platform for Tailored Budgeting, Expense Tracking, and Optimized Savings Strategies
Dhanunjay Jagu
, Pavan Sai Kiran Nerella , Surendra Thirlangi , Tanooj kumar Tumati , Dr. D.Ratna Giri
Personal Finance, Machine Learning, Clustering, K-Means, Budgeting, Data Visualization, Financial Analysis, Recommendation System, Streamlit
In today’s rapidly evolving financial landscape, effective personal financial management is paramount to achieving long-term economic stability. SmartSave introduces a novel, data-driven methodology that synergizes machine learning techniques with advanced interactive analytics to deliver personalized budgeting and expense tracking solutions. By processing user-uploaded financial datasets—including income, rent, loan repayments, groceries, transport, utilities, and discretionary spending—the platform first normalizes the data and then employs a K-Means clustering algorithm to segment users into distinct financial profiles. These profiles capture unique spending behaviors and savings potentials, allowing SmartSave to generate tailored budgeting recommendations drawn from methodologies such as the classic 50/30/20 rule, envelope budgeting, and zero-based budgeting. Complementing its analytical core, the platform features robust 2D and 3D visualizations that juxtapose actual spending patterns with idealized budget allocations. Built on Python frameworks such as Streamlit, Pandas, NumPy, Matplotlib, and Scikit-Learn, SmartSave not only enhances financial literacy but also promotes disciplined budgeting practices, ultimately contributing to improved personal economic stability and sustainable financial growth.
"SmartSave: A Comprehensive, Machine Learning-Driven Financial Analysis, Clustering, and Visualization Platform for Tailored Budgeting, Expense Tracking, and Optimized Savings Strategies", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.c52-c56, March-2025, Available :https://ijsdr.org/papers/IJSDR2503210.pdf
Volume 10
Issue 3,
March-2025
Pages : c52-c56
Paper Reg. ID: IJSDR_301140
Published Paper Id: IJSDR2503210
Downloads: 000138
Research Area: Science and Technology
Country: Kakinada rural, Andhra Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
