Paper Title
APPLYING MACHINE LEARNING TO IMPROVE THE UNDERLYING MODELS USING DATA BALANCING AND OPTIMAL PARAMETER SELECTION

Abstract
Abstract - Employees of any company are the main factor of development and growth of the organization. Keeping control and analyzing the current situation of staff turnover can significantly improve the company's position in the market through timely replacement of an employee or improvement of his working conditions in order for him to continue working in the same place. Employment of new people leads to a loss of time for the introduction of the employee to the company and its preparation for the specialized tasks of the organization. It is experimentally found that people between the age group of 24 to 28 inclusive are the most likely to change jobs. And also, the effect of various attributes from the raw data on resignation is shown and found that most of them are weakly correlated with resignation. Based on the dataset, various machine learning models have been built to predict employee quitting from the organization and the quality of the best one reaches almost 82% accuracy rate. Keywords - Data Analysis, Parameter Balancing, Sub Optimal Parameters, Binary Classification, Models.