Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{154173,
        author = {Thivyadharsine and Dennisa Molly and Vandhana and Aiswarya and Sujitha and Varsha and M.S.Sassirekha},
        title = {HR Attrition Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {51-54},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154173},
        abstract = {The  HR Attrition Case Study is a fictional dataset which aims to identify important factors that might be influential in determining which employee might leave the firm and who may not. In this, we analyzed the dataset  Employee Attrition to find the main reasons why employees choose to resign. Firstly, we utilized the correlation matrix to see some features that were not significantly correlated with other attributes and removed them from our dataset. Secondly, we selected important features by exploiting Random Forest, finding monthly income, age, and the number of companies that significantly impacted employee attrition.},
        keywords = {Attrition - Attrition is a state when there's a reduction in the workforce, and the company does not take any action regarding this loss immediately. Attrition is a major problem in many organizations. Employees leave for personal reasons or move to more promising jobs.
KNN -  K-Nearest Neighbours algorithm stores all the available data and classifies a new data point based on the similarity. This means when new data appears then it can be easily classified into a well suited category by using K- NN algorithm.
Random Forest - Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.

Prediction - Prediction refers to the output of an algorithm after it has been trained on a historical dataset and applied to new test data to forecast a particular outcome.

Bagging -  It follows a parallel technique(Independent models/Predictions). This gives equal weights },
        month = {},
        }