Employee Attrition Prediction

TL;DR

This paper presents an employee attrition prediction model using k-Nearest Neighbors, achieving high accuracy by leveraging performance and engagement features, and compares it with other machine learning approaches.

Contribution

It introduces a k-NN based approach for employee attrition prediction and evaluates its performance against other models using a real dataset.

Findings

Achieved 94.32% accuracy in predicting employee attrition.

Demonstrated effectiveness of k-NN with selected features.

Compared performance with ANNs, decision trees, and logistic regression.

Abstract

We aim to predict whether an employee of a company will leave or not, using the k-Nearest Neighbors algorithm. We use evaluation of employee performance, average monthly hours at work and number of years spent in the company, among others, as our features. Other approaches to this problem include the use of ANNs, decision trees and logistic regression. The dataset was split, using 70% for training the algorithm and 30% for testing it, achieving an accuracy of 94.32%.