Carrier Dome: Prediction of Student Career Interest

Attempting to deepen the understanding of factors that explain student career interest, this tries to identify and characterize profiles of students based on personal details, academic performance, student and family demographic (background details), family educational details, personality traits, activity aspects. Existing systems like paper work process and web-based services for determining career interest. In existing systems, CHAID (CHI-square Adjusted Interaction Detection), ID3 (Iterative Dichotomiser 3) and C4.5 (Classification Algorithm) algorithms were used but are only for specific factor so, to overcome existing system drawbacks and work on more factors at a time ‘Carrier Dome' Android-based Application is developed by using RF (Random Forest) and CART (Classification And Regression Tree) algorithms. These two algorithms are very powerful and more accurate. These techniques are more advantageous, as they run efficiently on large databases, generate accurate predictive models, suitable for high prediction accuracy of new data, supports high speed deployment, estimates which variables are important in classification, it is an effective method for estimating missing data and improve accuracy even if large proportion of data are missing, no need for prior feature selection and data pre-processing, works on large dataset. In this system, student should have to fill only mandatory data and it is cost effective. Carrier Dome aimed to develop a more accurate and powerful application than existing system for prediction of career interest.