An Effective Opinion Mining-Based K-Nearest Neighbors Algorithm for Predicting Human Resource Demand in Business

Abstract

The process of estimating and preparing for an association’s future supply and management of human resources is known as human resource planning (HRP). It is a significant section of directorial development. This study suggested a novel technique for HRP demand forecasting using the Modified K-nearest neighbor (MKNN) algorithm. MKNN is machine-learning algorithms that locate the k major comparable data sample to a new information sample and uses those data sample to forecast the value of the new sample. We generate an HR order estimate pointer organization consisting of variables that pressure HR order such as market share, sales volume, and economic conditions. We use MKNN to find the k most similar data samples to each data point in the display organization and use those data samples to predict HR command. We assessed our MKNN algorithm using data from an actual association and found that it can correctly forecast HR command with a fault speed of less than 5%. MKNN is a talented advance to HRP command forecasting as it is easy to use, does not necessitate a lot of data, and can exactly predict HR command in the attendance of non-linearity and haziness.

Received: 28 December 2023 | Revised: 9 July 2024 | Accepted: 15 July 2024

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Shashi Kant Gupta: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration.