Article
STUDENT RESULTS TRACKER AND PREDICTION
The Students Results Tracker and Prediction system is designed to efficiently manage, analyze, and predict students' academic performance using historical academic data. Educational institutions generate a large amount of student information related to marks, attendance, assignments, internal assessments, examinations, subjects, and overall performance. Managing and analyzing this information manually can be timeconsuming and may make it difficult to identify students who require additional academic support. The proposed system provides a centralized platform for recording student results, tracking academic progress, analyzing subject-wise performance, and predicting future results. The collected student data is first preprocessed by handling missing values, duplicate records, inconsistent information, and categorical attributes. Exploratory Data Analysis is performed to identify relationships between attendance, previous marks, study patterns, subject performance, and overall results. Data visualization techniques are used to display student performance through charts, graphs, and dashboards. Machine-learning algorithms such as Linear Regression, Decision Tree, Random Forest, and Gradient Boosting can be applied to predict students' expected marks or performance levels based on historical academic information. The trained models are evaluated using suitable metrics such as MAE, MSE, RMSE, and R² score for marks prediction, or accuracy, precision, recall, and F1-score for performance classification. The system can identify students who may be at academic risk and provide useful insights to teachers and administrators. It also helps students understand their academic progress and areas that may require improvement. Overall, the Students Results Tracker and Prediction system combines result management, data analysis, visualization, and machine learning to support academic monitoring, performance prediction, and data-driven educational decisionmaking.
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