• Pranav R. Vitankar
• Sanjivni R. Kale
• Sahil S. Hadke
• Advait A. Manohar

The technology has been updated to one level up, and the idea of hiring the employees the business companies, through online procedure is carried out. This makes the companies to get the employees of required post more immediate and in a faster way. It will be cost-effective as well. By exploring the internet, one can get the job easily of their qualifications and the field they wish to work in it. The posted jobs may be fake or legitimate, which are unaware by the people. To get rid of these kind of problems we come up with a new software which is designed to predict the job posts, as a result producing whether it is fake or legit one. We are designing a system as Fake job Post prediction using the concept of machine learning, in that we are using Random Forest classifier that produces accurate results in an efficient manner. The designed algorithm achieves the result of 98% as compared to the previously used algorithms. The students or users who search for a job may find difficulties in identifying the job posts that are fake and apply for the jobs, entering all the personal information without knowing about it. In some case they may get into the scams like paying money in the form of application fees in the need of job or the assurance of getting job after paying the money. The framework helps us to detect the posted jobs are fake not.

Fraud Classification, Fraud Detection Techniques, Machine learning, Decision tree, Random forest Logistic regression, Fraud detection and prediction.

"FAKE JOB DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.e157-e160, June-2024, Available :http://www.jetir.org/papers/JETIR2406423.pdf

"FAKE JOB DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppe157-e160, June-2024, Available at : http://www.jetir.org/papers/JETIR2406423.pdf