CLASSIFICATION OF JOB ADS

Abstract

As we are working to produce a deep learning model which will be using recurrent neural network as it is best when we are using series ofdata which will help us identify that whether a job ad is real or fraudulent We will be working to produce it with so much efficiency with a hope that it will help big job portal websites to classify ease that whether a job ad posted on their website is a fraud This project is basically our final year project which we have chosen in order to produce something helpful for our society as it will be extremely helpful if it is produced correctly it will help people as the job requirement for companies increasing day by day and companies are using big websites to drag good employers to there companies so with the help ofthis classifier employers will detect that whether with or is legitimate. are a job ad posted on a website is either fraud or legitimate. The motivation for choose to work on this project was that as there is a lot increase in unemployment and the candidates are usingjob ads posted on websites to find a suitable job for themselves and there is no way for them to find out whether a job ad posted on these websites is legitimate or not so this model will help them get a better information about a job ad that is it a fraud or a legitjob requirement. us to i Fake job classification and detection can be done with great accuracy and precision. As a result, in order to improve accuracy, machine learning and deep learning algorithms must be applied to cleaned and pre-processed data. Further, deep learning neural networks are used so as to achieve higher accuracy. Finally, all of these classification models are compared to one another in order to determine which classification algorithm has the best accuracy and precision.