To understand why and when employees are most likely to leave an oroganisation. This can help structure the actions to improve employee retention as well as possibly planning new hiring in advance. We will attempt to address the problem statement using the below questions:
- What is the likelihood of an active employee leaving the company?
- What are the key indicators of an employee leaving the company?
- What policies or strategies can be adopted based on the results to improve employee retention? Given that we have data on former employees, this is a standard supervised classification problem where the label is a binary variable, 0 (active employee), 1 (former employee). In this study, our target variable Y is the probability of an employee leaving the company.
- Data Exploration
- Data Preprocessing
- Exploratory Data Analysis (EDA) - Python, SQL
- Data Modification
- Data Visualization - Python, Power BI/Tableau
- Data Interpretation - Questionnaire
- Feature Selection
- Model Selection and Training
- Model Evaluation using ML Algorithms for Attrition Prediction
- Classification Report and Accuracy Explanation
- Conclusion and Sources.
In this project, we explored the booming HR Analytics domain, developed a ml model that could predict which employees are more likely to quit. We explored the data, cleaned, modified, visualized and then created a random forest model to predict how likely the employee quit the job.