• AYUSH SRIVASTAVA
• ADARSH KUMAR MALL
• ADITYA KISHORE
• AVINASH CHANDRA
• SURAJ KUMAR B.P.

Today, the business sector does not consider the potential employee's skills, but rather their characteristics. People are what shape professional, but also personal lives. So the recruiter needs to refine the employee's attitude. With the number of job seekers on the rise but the number of jobs continuing to decrease, it is very difficult to hand-list the best candidate for a suitable job by looking at their CV. This article attempts to explore different machine learning approaches to accurately predict human behavior through reanalysis, and concepts such as natural language processing (NLP). The Output shows that the Random Forest algorithm have better precision compared to other algorithms such as kNN, Logistic Regression, SVM and Naive Bayes. The template searches for the characters and other details of the candidates, such as skills, experience, etc. Using this system, organizations can filter the applicant pool and reduce the workload of a company's recruitment department.

The Big Five personality test, known as the OCEAN model, analyzes various personality traits based on five dimensions: openness (O), conscientiousness (C), extraversion (E), agreeableness (A), natural reaction (N). Each character represents a different type of person and generating score on the basis of technical skills.

"Predictive Modelling of Personality Traits through Curriculum Vitae Analysis using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.g242-g247, May-2024, Available :http://www.jetir.org/papers/JETIR2405631.pdf

"Predictive Modelling of Personality Traits through Curriculum Vitae Analysis using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppg242-g247, May-2024, Available at : http://www.jetir.org/papers/JETIR2405631.pdf