A Novel Python Based Fake Jobs Post Identification And Analysis Methodology Using Elevated Learning Strategy

Online job boards and portals have grown in popularity in recent years, making it easier and more effective for people to look for work. False job ads have proliferated with the advent of digital transformation, which is both an opportunity and a threat. People looking for work may fall victim to these misleading listings, which can lead them astray, waste their time, and expose them to scams. The research presents Elevated Learning based Fake Job Prediction (ELFJP), a claim that utilizes learning based classification algorithms to stop fake job advertisements on the internet. To assess the effectiveness of the suggested method, it is cross-validated using a traditional learning model known as Random Forest Classifier (RFC). We find the best job fraud detection model by comparing the results of different classifiers. Web fraud may be detected and prevented with the help of these classifiers. In a sea of seemingly legitimate job ads, it helps to single out the fraudulent ones. This system is able to identify fake job ads on the web by integrating classifiers with an ensemble learning model and then comparing and evaluating the results. In light of the ever-changing strategies used by con artists, the research stresses the significance of being proactive. To keep up with ever-changing fraud tactics, machine learning models are continuously improved and adjusted. The ultimate aim of this proposed approach is to help make a safer online job market by reducing the likelihood of fraudulent ads and increasing confidence among those looking for work.