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Predicting the hiring possibility for candidate

Abstract: Recruitment follows some essential steps like attracting, selecting, appointing fabulous candidates for that position. There is ever-increasing center of attention of a purposeful recruitment. Every employer make investments lot of cash and time for recruiting humans to fill some unique positions and losing assets for looking viable candidates. The complete funding turns into loss if the chosen candidates do no longer be a part of in the corporation after finishing this complete process. The most important goal of this lookup is to predict becoming a member of environment friendly candidates on earlier than resume resolution and complete technique is to be accomplished in an environment friendly way with minimal fee and minimal time. This prediction based totally on some applicable attributes of quantitative and qualitative elements like age, gender, work experience, cutting-edge revenue and earnings hikes etc., to predict the hiring candidates through making use of a number statistical measures on characteristic choice and the use of a variety of computer gaining knowledge of algorithms to construct the model. These outcomes will assist to predict the candidates who are becoming a member of in the end. So here we'll build multiple models, where we'll compare which model gonna give better accuracy. Compare the properties of the model. And once we'll build, we'll get the best model to take that and we'll build a front end HTML and CSS, JavaScript and back end with Flask framework. We will be providing you the complete code, we'll be helping you to set up the whole environment in your system. And in the end, we will be providing you the content for your report and also you can publish it into IEEE this will be one of the best IEEE project that you're gonna present in your colleges and you can publish it into IEEE and even some other publications. It will be best to finally year engineering project.

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