In graduate studies, many students find it difficult to achieve good grades because they do not get much support in higher education courses compared to the support that students receive in schools. We can use machine learning for the student grades prediction task so that instructors can help students prepare for topics where student grades were predicted low. In this article, I will walk you through the task of student grades prediction with Machine Learning using Python.
Student Grades Prediction
Universities are very prestigious places to access higher education. But the amount of fees universities charge today never equals the support they give to students. Some students need a lot of attention from instructors because if special attention will not be given to those students who are not getting good grades, it could be detrimental to their emotional state and their career in the long run.
By using machine learning algorithms, we can predict how well the students are going to perform so that we can help the students whose grades are predicted low. Student Grades Prediction is based on the problem of regression in machine learning. In the section below, I will take you through the task of Student Grades prediction with machine learning using Python.
Student Grades Prediction using Python
I hope you now have understood why we need to predict the grades of a student. Now let’s see how we can use machine learning for the task of student grades prediction using Python. I will start this task by importing the necessary Python libraries and the dataset:
The dataset that I am using for the task of students grade prediction is based on the achievements of the students of the Portuguese schools. In this dataset the G1 represents the grades of the first period, G2 represents the grades of the second period, and G3 represents the final grades. Now let’s prepare the data and let’s see how we can predict the final grades of the students:
In the above code, I first selected the necessary columns that we need to train a machine learning model for the task of student grades prediction. Then I declared that the G3 column is our target label and then I split the dataset into 20% testing and 80% training. Now let’s see how to train a linear regression model for the task of student grades prediction:
The linear regression model gave an accuracy of about 84% which is not bad in this task. Now let’s have a look at the predictions made by the students’ grade prediction model:
[[16.16395534 14.23423176 14.08532841 5.28096434 14.23423176] [16.16395534 16.16395534 14.08532841 5.28096434 7.97291422] [14.52779998 11.92149651 14.08532841 9.13993948 4.71694746] ... [ 4.71694746 11.92149651 3.9451298 9.13993948 9.13993948] [12.56424351 4.92497623 3.9451298 5.28096434 5.28096434] [11.92149651 9.05247158 3.9451298 5.28096434 16.16395534]] [[[15 16 2 0 2] [15 14 2 0 2] [15 14 3 0 6] [ 7 6 2 0 10] [15 14 2 0 2]]....
So this is how you can train a linear regression model for the task of students grade prediction with machine learning using Python. You can do a lot more with this dataset, you can find the complete information about this dataset from here. I hope you liked this article on Students Grade prediction with machine learning using Python. Feel free to ask your valuable questions in the comments section below.