Career Recommendation using Machine Learning for Secondary Education

With the advancement of modern technology and diverse career options, students at school level are more uncertain and confused to choose their streams of education. It is crucial for students to select those streams and courses which relate to their aptitude and interest. However, this vast amount of information along with other extraneous variables, such as parental decisions and peer pressure, negatively influence the future career prospects of students. This paper describes a machine learning-based solution, which is designed to recommend suitable streams for secondary education namely, arts, commerce, or science, considering the interests, aptitude, and academic performance of the student. The application of the Random Forest Regression has shown the robustness and reliability of the proposed system to offer an effective recommendation tool that allows students to make better decisions about their future in education.