Abstract
Abstract Proposed personal finance score index is an algorithmic model developed based on the some of the functional aspects of personal financing models combined with system approach for predicting the accuracy of the system in more pragmatic conditions at the user level. While there are many apps that are integral to handling the personal finance data as per the given inputs, validation and program models designed for the application, very few systems enable the companies in handling the solutions in the form of predictive analytics-based guidance solutions, wherein the users are able to have track of inputs that are integral to their personal finance conditions. The model enables the users with prediction inputs on the category of the transaction as suitable or ineffective or other classifications. SVM classifier based trained model reflects the potential factors integral to the model and how the solution is pragmatic for decision making. Test results of the model indicate that if the proposed system is integrated into personal finance applications that has scope for integration, it can help the user base in estimating the transaction worthiness.
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