Speaker
Description
The project aims to develop a personal finance desktop application that provides users with a solution for managing expenses and financial assets through a user-friendly interface. The application assists users in keeping track of their expenses by providing a visual depiction of their expenditures and tracking the daily performance of their cryptocurrency assets. In addition to that, Expensier makes use of machine learning technologies, using algorithms such as Linear Regression and K-Nearest Neighbors to estimate future expenses based on historical spending patterns. As a result, consumers may manage their budgets more effectively and make better-educated financial decisions. Expensier is a WPF application developed with .NET and C#. I believe this project contributes to the field of Computer Science by showcasing the application’s full-stack development process, which gives insights into the adoption of current software development practices. The project also provides a practical example of how machine learning techniques may be used in finance management applications.
