# Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry
## 1 Introduction  
In software industries, the employee churn rate is approximately 12-15% [1]. This percentile shows that the churn rate is prominent in IT industries and cannot be considered good for the industry. When an employee leaves a recognized job, there will be a problem for the organizations as they will have to discover a new valuable employee as a potential replacement. Companies will face various problems when an employee leaves the job, such as salaries, training costs of the new employee, and environment, etc. [1]. Employees are valuable assets to an organization. When a software company wants to lower the churn rate, it needs to analyze the churn factors and understand the reasons. Some organizations provide researchers with facilities to examine the turnover pattern from their HR data [2]. The researchers present their conclusion that the sustainability of employees is a challenging job for the companies, because nowadays, more worthy employees are tempted globally by other organizations [3]. Software companies are growing fast, but on the same side, various researchers have conducted online surveys to examine the churn rate, which comes towards 20.78%, which is relatively high. Attrition means when an employee leaves the job for personal reasons and immediately moves from one organization to another. There are two types of employee turnover, the first type is involuntary, where employees retire or are fired by the companies, and the second type is voluntary, where employees leave their job due to their own personal matters [4]. Most organizations are focused on voluntary turnover where an employee left the job deliberately. It could be expensive for organizations for the reason that finding a new worthy employee is time-consuming. Different features affect employee churn, such as workload, working conditions, salary, job experience, and job satisfaction level [5]. In order to maintain the churn rate in the organization, the company owners need to build trust and emotional safety with each employee. They are more inclined to perform and behave responsibly and to be eager to share their knowledge with coworkers if they have confidence in their leaders and fellow employees. The importance of emotions must be recognized by business leaders. Leaders need to have a certain level of emotional intelligence to notice and comprehend their employees' feelings in order to recognize this. Employees in the company must have emotional intelligence, particularly when working together on activities that call for it. The development of emotional intelligence helps to make interpersonal relationships better [6]. The global COVID-19 pandemic [7], which has been going on for two years, has had a significant impact on the current situation. The economics of several nations are being affected globally by this pandemic. Even at the start of the pandemic, the forecasts were unduly pessimistic; some even anticipated a worse economic disaster than in 2009. Since the pandemic's start, many people have lost their employment, particularly in industrial areas. The world was governed by a coronavirus pandemic in 2020, making it a unique year for the EU economy. The study presented in this paper aims to find the most important features that affect employee churn, and after the churn analysis, we have built a prediction model that predicts the organizations' churners. At the end of this research, a risk model is also developed for further classification of those employees who have not left. With all this, we achieved our goal of performing statistical analysis by using four EDA techniques and then implementing five ML algorithms. Moreover, a comparison is made between our predictive models with the recursive feature elimination (RFE)-based model to present the effectiveness of this research. The main contributions of the proposed approach include:  
* A novel IoT-enabled technique to predict employee churn in the software industry. * An effective risk model development to identify the potential risk of churners.