Fake Job Detection and Analysis Using Machine Learning and Deep Learning Algorithms


  • C.S. Anita
  • P. Nagarajan
  • G. Aditya Sairam
  • P. Ganesh
  • G. Deepakkumar




With the pandemic situation, there is a strong rise in the number of online jobs posted on the internet in various job portals. But some of the jobs being posted online are actually fake jobs which lead to a theft of personal information and vital information. Thus, these fake jobs can be precisely detected and classified from a pool of job posts of both fake and real jobs by using advanced deep learning as well as machine learning classification algorithms. In this paper, machine learning and deep learning algorithms are used so as to detect fake jobs and to differentiate them from real jobs. The data analysis part and data cleaning part are also proposed in this paper, so that the classification algorithm applied is highly precise and accurate. It has to be noted that the data cleaning step is a very important step in machine learning project because it actually determines the accuracy of the machine learning as well as deep learning algorithms. Hence a great importance is emphasized on data cleaning and pre-processing step in this paper. The classification and detection of fake jobs can be done with high accuracy and high precision. Hence the machine learning and deep learning algorithms have to be applied on cleaned and pre-processed data in order to achieve a better accuracy. Further, deep learning neural networks are used so as to achieve higher accuracy. Finally all these classification models are compared with each other to find the classification algorithm with highest accuracy and precision.