Job Candidate Eligibility Prediction using Convolutional Neural Network

The foundation of the applicant selection process is the candidate eligibility criteria. All applications must go through some evaluation process to get selected for a job. Without the use of a machine learning algorithm, the traditional method can require a great deal of time and work from the recruiter and force candidates to wait weeks or months for their results, depending on how many applications there are. It can be completed in a couple of minutes or even seconds with the help of machine learning algorithms. For employees who need this data to decide whether the applicants qualify, this enhancement may prove to be a significant benefit even though there are additional factors to take into account, such as fraud prevention and background checks. Applying convolution neural network to these kinds of challenges has produced quite encouraging outcomes. In this work, we attempt to apply Convolutional Neural Network integration to candidate eligibility cases using a probabilistic technique. The result shows that this technique is suitable for the scenario that requires only two states—approved or rejected-because it could generate binary classified output. Our project aims to implement Convolutional Neural Network and iterate on the approach by adjusting the approach of the network.