A Comparative Study on Job Recommendation using Classification Algorithms

Recommendation part plays a vital role for both jobseekers and job recruiters to find the most accurate jobs and efficient jobseekers in less time. To implement a job recommendation system for job seeker which will consider various aspects such as skillset., certifications., and interests for recommending the appropriate job. A job seeker can apply only the jobs he is eligible for according to his skillset Jobseeker will be able to rate the jobs and recommend the jobs to their peers. This recommendation will display top rated and recommended jobs so that the jobseeker doesn't have to go through all the available jobs to find the right one. There are many machine learning algorithms in use for the recommendation part such as classification and clustering algorithms. This study has considered a dynamic dataset and tested it with various classification algorithms like: Logistic Regression., Support Vector Machine., Naive Bayes., Decision Tree., and Random Forest to check the eligibility of the jobseeker based on his grades and skillset. Among all these algorithms., the highest accuracy rate is attained with Logistic Regression (LR) algorithm. Henceforth., based on the observations and the results., the proposed recommendation model is implemented using logistic regression to make most accurate recommendations. When compared to the previous existing systems., the proposed algorithm can provide the most efficient recommendations in less time.