View in EDS

Expense tracker management system using machine learning.

Saved in:
Bibliographic Details
Title: Expense tracker management system using machine learning.
Authors: THAKUR, Rishiraj Singh, JADHAV, Akshay
Source: Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi; Aug2025, Vol. 43 Issue 4, p1265-1275, 11p
Subject Terms: MACHINE learning, FINANCIAL management, USER interfaces, COST control, BUDGET management, PREDICTION models, ENSEMBLE learning
Abstract: The transition from "Income and Expense Tracker" to "Expense Tracker Management System" marks a strategic evolution in financial management tools. This shift underscores a more focused approach towards efficiently tracking and managing expenses. While the former emphasized both income and expenses, the latter places a heightened emphasis on expense management specifically. This refined system aims to streamline expense tracking processes, offering users a robust platform to meticulously monitor and analyze their spending habits. Through customizable features, intuitive interfaces, and comprehensive reporting functionalities, the "Expense Tracker Management System" empowers users to take full control of their finances, fostering greater financial awareness and facilitating smarter spending decisions which makes it better than the existing state-of-the-art. The article also emphasizes predicting future expenses based on prior experience of the user, using machine learning techniques. Different machine learning techniques are used for prediction purposes such as multi-layer perceptron, extreme gradient boosting, support vector machine and ensemble techniques such as bagging, boosting. Extreme Boost (XGBoost) outperforms other individual models with highest R-square value and voting ensemble regressor outperforms ensemble techniques with highest R-square value of 78.11% and lowest relative absolute error of 0.6121. [ABSTRACT FROM AUTHOR]
Copyright of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi is the property of Sigma: Journal of Engineering & Natural Sciences / Mühendislik ve Fen Bilimleri Dergisi and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:The transition from "Income and Expense Tracker" to "Expense Tracker Management System" marks a strategic evolution in financial management tools. This shift underscores a more focused approach towards efficiently tracking and managing expenses. While the former emphasized both income and expenses, the latter places a heightened emphasis on expense management specifically. This refined system aims to streamline expense tracking processes, offering users a robust platform to meticulously monitor and analyze their spending habits. Through customizable features, intuitive interfaces, and comprehensive reporting functionalities, the "Expense Tracker Management System" empowers users to take full control of their finances, fostering greater financial awareness and facilitating smarter spending decisions which makes it better than the existing state-of-the-art. The article also emphasizes predicting future expenses based on prior experience of the user, using machine learning techniques. Different machine learning techniques are used for prediction purposes such as multi-layer perceptron, extreme gradient boosting, support vector machine and ensemble techniques such as bagging, boosting. Extreme Boost (XGBoost) outperforms other individual models with highest R-square value and voting ensemble regressor outperforms ensemble techniques with highest R-square value of 78.11% and lowest relative absolute error of 0.6121. [ABSTRACT FROM AUTHOR]
ISSN:13047191
DOI:10.14744/sigma.2025.00119