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.
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