PREDICTION OF CANDIDATE JOINING AND RECRUITING PROCESS USING MACHINE LEARNING

Abstract

In the era of advanced technology and digitalization, the use of machine learning makes it possible to increase the efficiency of the recruitment process. This project work aims to identify various variables that directly play a role in recruitment. The work also addresses the HR sphere problems and proposes a solution related to the use of machine learning. The proposed methodology includes an analysis of the characteristics which are dominated by past candidates. The data obtained suggests that machine learning can bring significant benefits to the recruitment process, such as reducing the cost and time for selection, reducing emotional factors, and increasing accuracy. Overall, this proposed work provides valuable insight into the potential of machine learning in recruitment and highlights how it can be used to facilitate the recruitment process. Employee recruitment process is one the complex tasks in the current generation. There are many reasons where candidate may reject the offer given by the company. So it is very difficult to identify which candidate may join and which candidate may reject the offer. In this proposed system we build an automation for prediction whether candidate joins to the company or not. HR datasets collected from the reputed websites like www.kaggle.com, www.dataworld.com etc... In this proposed system we develop automation for companies to know the candidates joining. Company joining prediction is done using Machine Learning algorithms. Efficient ML algorithm used to process the HR data. Proposed system is a browser based application meant for any organization and developed using Microsoft technologies such as Visual Studio, C# and SQL Server.