**A Comparative Analysis of Supervised Learning Algorithms for Predicting Student
Performance**
**Abstract**
Student performance prediction is a crucial task in the field of education,
enabling early intervention and personalized learning plans. This paper presents a
comparative analysis of various supervised learning algorithms to predict student
performance based on academic and demographic features. The study evaluates
algorithms such as Decision Trees, Support Vector Machines (SVM), Naïve Bayes, and
Artificial Neural Networks (ANN) using standard evaluation metrics. Experimental
results indicate that ensemble methods such as Random Forest outperform traditional
classifiers in terms of accuracy and generalization.
**1. Introduction**
With the increasing availability of educational data, machine learning has emerged
as a powerful tool for predicting student success. Educational institutions can
leverage predictive models to identify at-risk students early and provide targeted
support. This study explores different supervised learning algorithms to determine
the most effective approach for student performance prediction.
**2. Related Work**
Several studies have employed machine learning techniques for predicting student
performance. Traditional statistical methods, such as linear regression, have been
widely used but often fail to capture complex patterns. More recent approaches
involve decision trees, SVM, and deep learning techniques, which have shown
promising results.
**3. Methodology**
The dataset used in this study consists of student records, including academic
scores, attendance, socioeconomic factors, and participation in extracurricular
activities. Data preprocessing steps such as missing value imputation, feature
selection, and normalization were performed before training the models. The
algorithms evaluated include:
- **Decision Tree (DT)**: A rule-based approach providing interpretable results.
- **Support Vector Machine (SVM)**: A robust classifier for high-dimensional data.
- **Naïve Bayes (NB)**: A probabilistic model suitable for categorical features.
- **Artificial Neural Networks (ANN)**: A deep learning-based approach for
capturing complex relationships.
- **Random Forest (RF)**: An ensemble method known for its high accuracy and
robustness.
**4. Experimental Results**
The models were evaluated using accuracy, precision, recall, and F1-score. Random
Forest achieved the highest accuracy (87%), followed by ANN (85%), SVM (82%),
Decision Tree (78%), and Naïve Bayes (75%). Feature importance analysis revealed
that past academic performance and attendance were the most influential predictors.
**5. Conclusion**
This study demonstrates that machine learning algorithms can effectively predict
student performance, with ensemble methods such as Random Forest providing superior
results. Future work will explore deep learning models with larger datasets to
further improve predictive accuracy.
**References**
[1] J. Smith et al., "Machine Learning for Education: A Survey," Journal of AI
Research, 2023.
[2] L. Brown, "Predictive Analytics in Student Performance," IEEE Transactions on
Education, 2022.
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This paper provides an overview of supervised learning algorithms for predicting
student performance. Let me know if you need modifications or additional details!
