MACHINE LEARNING POWERED EMPLOYEE ATTRITION PREDICTION
Author(s):
Ms. J. VENKATESWARAMMA, Vaddadhi V C H S S Sai Charan, Donepudi Pavan Praveen, Jonnalagadda Yaswanth Ram, R.Yashwanth vijay sai
Keywords:
Logistic Regression, Random Forest, Recursive Feature Elimination (RFE), Confusion Matrix, Employee Attrition.
Abstract
The project aims to find the Main Factors or Reasons for Employee Attrition to decrease the employee attrition rate by using HR data collected from an organization or a company, for this a dataset has been gathered containing various employee attributes such as ID, Attrition, Years with current manager, over18, Job level, Job Role etc., By analyzing these Features finding the Reasons or factors for Employee Attrition is used to taking steps to control the attrition on a specific Factor. Here we using the Machine Learning Algorithms for finding these Attrition factors, the algorithms using are logistic Regression and Random Forest, before using algorithms few analyses is done on historical data for finding left and stay data and count of Attrition percentage. The ML Models trained with those algorithms and both models working on split data called train and test data, these algorithms are used to predict the top attrition factors from all gathered data, it helps to make solutions for decrease Attrition rate. The Key Components of Project include data collection, data preprocessing, data analysis, data visualization, ML Models training, Model metrics, Top Factors finding, data collected from various sources like historical data and statistical archives and Transformation methods are used to create accurate information for the training model.
Article Details
Unique Paper ID: 164242

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 489 - 494
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