Financial aid decision support using decision tree algorithm

This extended abstract presents a study on financial aid decision support using decision tree algorithm. The objectives of this research were to design a model by using decision tree algorithm for classification of financial aid decision, to develop a classification of financial aid decision system, and to evaluate the model by using Evaluation Metrics. A dataset of student profiles and financial aid records was used to accomplish these objectives. There was preprocessing done to the dataset. Then, a decision support system was built using the decision tree method. The decision tree algorithm's performance was measured by dividing the data into training and testing sets. Accuracy was evaluated as a performance metric and used to predict aid eligibility on the test set using the trained model. The results of this study demonstrate the performance of the financial aid decision support decision tree algorithm. The algorithm's capability to create accurate predictions was shown by the accuracy obtained on the test data. To speed up and enhance the efficiency and fairness of the financial aid decision-making process, this study highlights the significance of methods based on data. To improve the accuracy and precision of financial aid projections, future research can concentrate on improving the decision tree model, including extra features, and investigating alternative methods.