Fraudulent Job Post Recognition

Abstract: Modern technology has advanced to the point that employing staff through an online process is now possible for businesses. This enables businesses to hire workers for necessary positions more quickly and immediately. It will also be reasonably priced. One can quickly find a job that fits their skills and field of interest by searching the internet. People may not be aware that the jobs that are posted are false or authentic. We developed new technologies to forecast job posts and determine if they are legitimate or fraudulent in order to solve these kinds of issues. We are creating a fake job system. Utilising the idea of machine learning for post-prediction, we are employing the Random Forest classifier, which generates precise outcomes quickly. Comparing the designed algorithm to the previously utilised algorithms, the result is 98%. When students or users look for work, they can have trouble spotting phoney job postings and applying, inadvertently providing all of their personal information. In certain instances, people could fall victim to scams that include paying money in the form of application fees in order to obtain employment or receiving a guarantee of employment upon payment. The framework assists us in determining if the jobs listed are fraudulent or not.

Keywords: Fraudulent Job Post, support vector machine, Random Forest Classifier, Machine Learning, NLTK, Hyperparameter Tuning.