Here's machine learning model to predict a student's final grade in a particular course.
The dataset consists of 480 student records (number of instances) and 16 features (number of attributes). The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade level, and section. (3) Behavioral features such as raised hand-on class, opening resources, answering surveys by parents, and school satisfaction. The data set also includes the school attendance and parent participation features. The students are classified into three numerical intervals based on their total grade/mark (the “Class” column): • Low-Level (L): interval includes values from 0 to 69 • Middle-Level (M): interval includes values from 70 to 89 • High-Level (H): interval includes values from 90-100