Career prediction using Decision tree algorithm and pass prediction using linear regression for higher secondary school students

Abstract— Career prediction is an essential issue that students face when deciding on their future education and career paths. In this seminar presentation, we will discuss the concept of career prediction using the decision tree algorithm, and also pass percentage prediction using linear regression algorithm, powerful tools for analyzing and predicting different data patterns. We will explain how this method can be used to predict a student's career path based on their academic performance, interests, and skills. In this presentation, we will start by introducing the basics of machine learning and decision tree algorithms, and how they can be applied to career prediction. We will then explore the various factors that are commonly used in career prediction models, such as academic performance, interests, skills, and personality traits. We will also discuss another algorithm for predicting the pass percentage of the students by using the linear regression algorithm. importance of data collection and analysis in building accurate career prediction models. we will provide some successful examples of career prediction models and discuss the limitations and challenges of using machine learning technologies for career prediction.