Student performance analysis and prediction using datasets have become essential to modern education systems. With the increasing availability of data on student demographics, academic history, and other relevant factors, schools and universities are using advanced analytics and machine learning algorithms to gain insights into student performance and predict future outcomes. This approach helps educators identify areas of improvement, personalize learning experiences, and provide targeted support to struggling students. Furthermore, student performance analysis and prediction can also aid in decision-making processes for school administrators and policymakers, helping them allocate resources more effectively. In this article, we will explore the benefits of using datasets for student performance analysis and prediction and discuss some methods and tools used in this field.
- Gender: sex of students -> (Male/female)
- Race/ethnicity: ethnicity of students -> (Group A, B, C, D, E)
- Parental level of education: parents’ final education ->(bachelor’s degree, some college, master’s degree, associate’s degree)
- Lunch: having lunch before test (standard or free/reduced)
- Test preparation course: complete or not complete before the test
- Math score
- Reading score
- Writing score
- Check Missing values
- Check Duplicates
- Check the data type
- Check the number of unique values in each column
- Check the statistics of the data set
- Check various categories present in the different categorical column