Students' academic achievements often evaluated mostly through Grade point average (GPA) is very essential in determining their future course of study. They in particular form a critical part of college entry examinations, scholarships and job opportunities and since GPAs form one of the bulk weightings, it is important to understand the factors that determine GPA. Some of these factors include study habits, attendance, parental guidance and control, participation in extra-curricular activities among other things are indeed linked to the good performance in academics. Nonetheless, it is difficult to evaluate the contribution of each other factor in the final GPA and this presents an enormous problem for educators and students making rational decisions based on solid information. Many a times, such uncertainties breed approaches of general nature that again do not address the specific needs of particular students or at the most facilitate very little of the expected change in behavior.
To close this in, our study proposes applying machine learning methods to predict student grades on the basis of certain features and find out how important each is for the
outcome. By looking at age, behavior, and support acquisition, our strategy looks at the bigger picture of impact on educational performance, showing the intervention areas. The model we are planning to create and apply machine learning will be able to accurately determine student's GPA and also rate-in factors that attribute to deception of the estimate. Such a perspective helps in the advancement of intervention strategies development, in order to avail resources and efforts where they are most needed. The importance of this project is rooted in the fact that its outcomes aim at changing how education planning and academic counselling services are conducted.