Predictive Model for Student’s Academic Performance Using Machine Learning Techniques

Abeer Ali Saeed Amer
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Abstract

Abstract: This research aims to predict student academic performance using historical data and machine learning algorithms. The dataset includes parental, and academic information about students. The study focuses on three machine learning algorithms: Logistic Regression, Decision Tree, and Support Vector Machine (SVM). To begin, we conducted data analysis to understand the distribution and relationships within the data. Visualizations such as homogeneity analysis of parental education, race, and gender, as well as count plots for gender according to parental education and race, were created to identify patterns and insights. The data was then pre-processed and used to train the three models. Each model's performance was evaluated based on accuracy, precision, recall, and F1 score. Confusion matrices and ROC curves were also generated to provide a comprehensive evaluation of each model's predictive power.
利用机器学习技术预测学生学习成绩的模型
摘要:本研究旨在利用历史数据和机器学习算法预测学生的学习成绩。数据集包括学生家长和学业信息。研究重点是三种机器学习算法:逻辑回归、决策树和支持向量机(SVM)。首先,我们进行了数据分析,以了解数据的分布和关系。我们创建了可视化图表,如父母教育程度、种族和性别的同质性分析,以及根据父母教育程度和种族划分的性别计数图,以确定模式和见解。然后对数据进行预处理,并用于训练三个模型。根据准确度、精确度、召回率和 F1 分数来评估每个模型的性能。此外,还生成了混淆矩阵和 ROC 曲线,以全面评估每个模型的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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