# Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry
## 2 Related Work
#### Data Mining Techniques for Predicting Employee Turnover  
The research mentioned in [8] aims to avoid this negative impact; therefore, their study builds the prediction model to predict future churners. For the comparison and evaluation of algorithms, the research implements the three algorithms and compares them. Employee churn is a severe problem in organizations. If the organization needs economic benefit, then there is a need to minimize attrition. This related research recognizes employees' related attributes that affect the churn rate. They take a dataset from the higher institutions of Nigeria, and the dataset consists of 309 records of employees. They have applied two methodologies; one is the decision tree, and the other is rule sets for creating the employee churn model [9].  
To identify the most impactful features that affect the employee churn, the researchers in [10] have used the EDA technique, which is recursive feature elimination, and they have used the HR dataset with 34 features that are openly available on the Kaggle website. This feature selection technique recursively selects the features and evaluates the model accuracy; therefore, this EDA technique takes time to select the most impactful features. After using the EDA technique, their work implements the six classification algorithms with best-selected features from the HR dataset and compares the algorithms' results. Their objective was to analyze the algorithm results with and without selected features. Researchers in [11] have used a visual-based EDA technique that shows the statistics about HR data taken from the Kaggle website to find an essential factor. They performed EDA using python libraries and generated a correlation matrix and histogram, which is used to show employee feature relations with output features. After analyzing the employee features, they implemented five classification algorithms and compared the ML algorithms' results for predicting future churners. In another related study [12], the authors only developed an employee prediction model using five classification algorithms, which were SVM, random decision forest, logistic regression, naive Bayes, and decision tree.  
Several types of research have been conducted to identify the most influential features that affect employee churn [13]. Some of the statistical techniques used are not computationally efficient because these techniques are based on model accuracy [14]. In contrast, some statistical techniques are visually based that show the graphical representations of employee input features with output features, and that type of technique requires time for performing analysis [15]. For this reason, it is necessary to apply those techniques that will not rely on model accuracy and should not take a long time for factor analysis [16]. To achieve this, we have used a filter-based method that does not affect model accuracy computationally, and through this, we can quickly analyze factors without taking a long time. Our motivation is to select top-ranked features for the identification of the essential churn features. It is observed broadly that the EDA-based machine learning model is beneficial for the software industry.