Smart Expense Tracker: Data Mining Intelligence in Areas of Intelligent Personal Finance Management
Keywords:
Data mining, Expense tracking, Machine learning, Personal finance management, Spending pattern analysisAbstract
The development of online dealings and the increasing sophistication of personal financial management have contributed to an increased necessity for clever and automated tools that can help people track, differentiate, and simplify their expenditures. The paper is a description of a smart expense tracker system design and development that relies on data mining techniques to provide valuable insights into the individual spending behaviour. The given system that is proposed is a combination of classification, clustering and association rule mining methods in the process of determining the spending pattern, future spending anticipation, and individual budgetary advice. The financial information that is obtained from the users is pre-processed and goes through various data mining processes that include classification into a decision tree using the C4.5 algorithm, K-Means Clustering and association analysis using Apriorism algorithm. Results would be presented in the shape of user-friendly dashboards where one can view how they spend their money at a glance. The Smart Expense Tracker is not founded on manual inputs and bare summaries as traditional expense management applications are, but instead on machine learning and categorization that reduces the human input and primarily maximizes the accuracy. The data of the users will be stored securely, and measures will be taken to ensure that the data is not contravened by the data protection laws. System trials have been conducted using a collection of 4,800 actual expense items, and the experiment showed that the classification accuracy of the system was high, with an average of 91.4 and clusters formed by the system were meaningful and formed in a manner that is substantially related to common categories of expenditure, which include food, transport, entertainment and utilities. The paper is useful to the research on the history of personal finance technology in offering a repeatable and empirical model that could be created in subsequent academic and business ventures.
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