Employee Attrition Using Machine Learning And Depression Analysis

Amongst the significant issues that corporate leaders have to deal with within an organization is the decline in proficient employees. This decline is primarily attributed to extreme work pressure, dissatisfaction at work, and ignored mental health issues such as depression, anxiety, etc. This is known as Employee Attrition or Churn Rate. Given the amount of stress employed people go through, focus on the state of mind has gained much-needed traction. Our model aims to predict the employee attritionrate and the employees' emotional assessment in an organization. A survey containing attrition-related questions helped us gather the required data for analysis. Our model will predict the attrition and give the depression analysis with the help of this data. Algorithms such as Decision Tree Classifier (DTC), Support Vector Machine(SVM) and Random Forest Classifier(RFC) were applied to this dataset after performing preprocessing steps, which helped us achieve an accuracy of 86.0% in predictingattrition rate. The results have been expressed using the primary classification metrics, including F1-score and accuracy.