Campus Recruitment Prediction
Anupama P R1, Nithin Sebastian2
1Anupama P R, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
2Nithin Sebastian, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
Manuscript received on 23 April 2024 | Revised Manuscript received on 01 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 31-33 | Volume-4 Issue-1 May 2024 | Retrieval Number: 100.1/ijdm.A163704010524 | DOI: 10.54105/ijdm.A1637.04010524
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: For businesses and students alike, campus recruitment is an important occasion. While businesses aim to draw in the best employees, students eagerly anticipate beginning their professional careers. Salary prediction is a crucial component of college recruitment, when employers ascertain the wage ranges, they would offer prospective employees. Many criteria, including the candidate’s qualifications, experience, and education, as well as the company’s budget and industry norms, play a role in predicting the salary for campus recruitment. In this project, we’ll apply machine learning approaches to forecast college recruitment salaries based on candidate historical data and salaries that match to those positions. In this project, we develop a predictive model for college recruitment by analysing the dataset that has been provided. Data processing and exploratory data analysis (EDA) are our initial steps. After that, we build a Flask web application that uses the trained predictive model to be deployed and lets users anticipate things based on input.
Keywords: Campus Recruitment Prediction, Ridge Regression, Model Selection.
Article of the Scope: Data Science