Personality prediction using Random Forest algorithm / Wan Abdul Qayyum Abdul Wahab

The research effort attempted to create a personality prediction system based on the Random Forest algorithm. The issue statement emphasized the need for an objective and dependable approach to evaluate an individual's personality for recruitment and position appropriateness. Previous research has underlined the importance of personality prediction in recruiting, as well as the Random Forest algorithm's ability to provide vital insights into applicant suitability for certain tasks, reduce bias, and predict long-term employee retention and satisfaction. The research objectives included developing and executing a data gathering strategy, analyzing the data, and assessing the model's performance. The study methodology included a systematic approach to problem solving, such as developing data collecting tools, selecting research approaches, and implementing data analysis procedures. The output of this project included the project's conceptual framework, system architecture, user interface, and performance assessment, such as the confusion matrix and accuracy computation. The conclusion emphasized the findings' relevance in improving knowledge of personality prediction and classification systems, as well as future project upgrade ideas.