Article
FEE PAYMENT TRACKER ANALYSIS AND PREDICTION
The Fee Payment Tracker Analysis and Prediction system is designed to efficiently manage, analyze, and predict student fee payment information using historical payment data. Educational institutions handle a large number of fee records related to tuition fees, examination fees, hostel fees, transportation fees, scholarships, installments, and pending payments. Managing these records manually can be timeconsuming and may lead to errors in payment tracking, record maintenance, and identification of outstanding fees. The proposed system provides a centralized platform for recording student fee details, tracking payment status, monitoring pending amounts, and analyzing payment patterns. The collected data is first preprocessed to handle missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory Data Analysis is performed to identify payment trends based on courses, academic years, fee categories, payment methods, and student information. Data visualization techniques are used to display total collections, paid and pending amounts, monthly payment trends, and fee-category distributions through charts and dashboards. Machine-learning algorithms such as Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting can be used to predict payment status or identify students who may have a higher possibility of delayed payment. Regression techniques can also be applied when the objective is to predict future fee amounts or expected collections. The trained models are evaluated using suitable metrics such as accuracy, precision, recall, F1-score, ROCAUC, MAE, RMSE, and R², depending on the prediction task. The system provides useful reports and dashboards that help administrators monitor fee collections and outstanding payments efficiently. It can also assist institutions in planning future collections and taking timely action regarding pending payments. Overall, the Fee Payment Tracker Analysis and Prediction system combines fee management, data analysis, visualization, and machine learning to improve payment tracking, identify payment patterns, and support effective financial planning and decision-making in educational institutions.
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