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With increase in advancement of technology and digital communication, it brings an ease for organizations torecruit job seekers online. Furthermore, Corona virus(COVID19) outbreak has made it almost impossible for anysector to not operate on a digital platform. Increase inunemployment due to Covid19, people are in desperate need of job, so recruiters tend to post online recruitment forapplicants to access it quickly and timely. However, scammersalso started posting fake jobs to collect hard earned moneyand personal information of job applicants, sometimesadvertisements for such job posting is written against areputable firm to damage its reputation. To avoid suchfraudulent and to minimize such behaviour by using datavisualization to predict fake job postings, so the applicant willbe alert and if required can report such job offers. The projectwill use website for users to perform search for job predictionand filters with the help of tableau software to analyze patternof too good to be true job types. To achieve project objective inreal world experiment is carried out on Employment ScamAegean Dataset (EMSCAD) consists of 18000 samples fromKaggle website. Then sample is updated to make it morerealistic and communicative. [ABSTRACT FROM AUTHOR]

In the present day, deep learning applications are widely used in every field of development. In this paper, such a type of application is discussed in real-time. Mura dataset is the largest abnormal dataset given by the University of Stanford. In this research paper, we will understand the pre-processing of the image dataset. Kera's and TensorFlow libraries are used to build this model. We are classifying images using different ML models where accuracy is compared to get the best model build. This paper gives us a clear understanding of building deep learning models, understanding pre-processing techniques, and working on heavy data. This research's main agenda is to create a model which can classify bone fracture images and also give a small path for further development in the deep learning area. In this project we are using Resnet50, InceptionV3, Mobile Net and DenseNet with optimizer Adam and comparing their accuracies graphically. [ABSTRACT FROM AUTHOR]