Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Career Recommendation System

Author : Shivam Mathwad 1 Rushikesh Pede 2 Aniket Chaudhari 3 Prashant Patil 4 Mr. Nikhil Dhavase 5

Date of Publication :12th August 2021

Abstract: Abstract---- recently, more and more people have begun to re-evaluate their career decisions and change careers at a later stage in life. A study conducted by GTI Media survey found that 18% of students say that they regret their choice of degree and 1,805 respondents cite a lack of initial research as the main cause of their disappointment. Also many people are confused as of which career path to choose. This can be prevented by proper counselling of young teenagers before they begin their graduate studies. In India, there are 350 million students, the biggest student population in the world. So for them to find a suitable career we need at least 1.4 million counsellors to maintain a globally acceptable student-to-school-counsellor ratio. But the number of counsellors is not the only issue there is, more often than not counsellors charge upwards of 2500-4000 for career counselling, which may not be financially viable to everyone. Thus, there is a need to develop a scientific career counselling software to tackle this issue. We aim to create an app for this purpose. Here, in the proposed system, we evaluate the aptitude and personality of a person based on the user's academic level using carefully curated psychometric and aptitude tests. The user will have to give personality test and all aptitude tests in the app to get their career recommendation. The user test scores (personality & aptitude) will be fed into a machine learning algorithm, which will then generate a model to predict career streams based on the user's scores. Thus, the recommendation will be very close to accurate as all significant data related to user ability, skills and personality will be taken into consideration. Users will be given in-depth analysis of their test results and career recommendation.

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