ABSTRACT
Research in the field of educational data mining, which entails using data mining tools and techniques to evaluate data at educational institutions, has been prompted by the need to improve the educational system. Because there is no specialized help provided for students who may require extra attention in certain of the registered courses, students at some higher educational institutions struggle to complete various courses. These issues with a system that analyses and periodically tracks students' course progress and performance lead to low academic performance and underachievement on the part of the students. As a result, this project was created with the intention of creating a model that uses a machine learning technique to predict student grades in their courses at different academic levels. The machine learning technique, which is frequently used for data exploration, was used for this project in order to create a model for forecasting student academic achievement. The constructed model was trained and tested using a dataset with attributes representing student personal information, including their gender, age, special skills, grades, time to study, resources, Attendance, etc. The system development was accomplished using the Python programming language, with Jupyter serving as the IDE for training and testing the model. This project will be very helpful to instructors, students, institutions, and parents in knowing in advance the strengths and limitations of a child, ward, or student.
