Educational data mining helps the educational institutions to perform effectively and efficiently by exploiting the data related to all its stakeholders. It can help the at-risk students, develop recommendation systems and alert the students at different levels. It is beneficial to the students, educators and authorities as a whole. Deep learning has gained momentum in various domains especially image processing with a large dataset. We devise a regression model for analyzing the academic performance of the students using deep learning. We have applied regression using deep learning and linear regression on the dataset. For such models with smaller datasets, to tackle the issue of overfitting is critical. Hence, the parameters can be tuned to deal with such issues. The deep learning model records a mean absolute score (mae) of 1.61 and loss 4.7 with the value of k = 3. While the linear regression model yields a loss of 6.7 and mae score of 1.97. The deep learning model outperforms the linear regression model. The model may be successfully extended to other programmes to mine and predict the performance of the learners.

教育数据挖掘通过利用与所有利益相关者相关的数据来帮助教育机构有效地执行工作。它可以帮助处于危险中的学生，开发推荐系统并警告不同级别的学生。它对学生，教育者和当局整体都有好处。深度学习在各个领域都得到了发展，特别是在具有大型数据集的图像处理中。我们设计了一种回归模型，用于分析使用深度学习的学生的学业成绩。我们已在数据集上使用深度学习和线性回归应用了回归。对于具有较小数据集的此类模型，解决过度拟合的问题至关重要。因此，可以调整参数以处理此类问题。深度学习模型记录的平均绝对得分（mae）为1.61，损失为4。7，其k =3。线性回归模型的损失为6.7，mae得分为1.97。深度学习模型优于线性回归模型。该模型可以成功扩展到其他程序，以挖掘和预测学习者的表现。