Appropriate Job Selection Using Machine Learning Techniques

Abstract

Abstract Job recruitment is the process of picking a qualified candidate for a vacant position in an organization. Suitable job selection is a challenging task, especially for freshers. Every year, millions of students complete their graduation and come up with many options for choosing their jobs. This job selection procedure depends on various factors. Considering these issues, this research aims to build a model to predict whether a job is suitable or not suitable for a candidate according to their skills, experiences, and desires for the job. In this situation, Machine Learning approaches can be useful. Data were collected from 120 people currently appointed to a job in various fields. Distinct machine learning techniques are used to predict whether they are satisfied with their current job or not. We also find the other performance matrices and compare them with other algorithms to evaluate the performance of the best model. The results show that the random forest technique is the most effective method for forecasting appropriate job selection, with 92% accuracy and 8% error. Based on the findings, this research will become an effective tool for selecting a suitable job based on people's desires.