Attrition datasets are usually imbalanced (more people stay than leave). Use techniques like SMOTE (Synthetic Minority Over-sampling Technique) to balance the classes. Algorithm Selection: Logistic Regression: Good for baseline interpretability.
Years at Company, Years in Current Role, Performance Rating, Training Times Last Year.
Environment Satisfaction, Job Satisfaction, Relationship Satisfaction, Work-Life Balance. 3. Exploratory Data Analysis (EDA) Attrition_cb01_gold_HD_2018
To analyze attrition effectively, focus on these common data categories: Age, Gender, Marital Status.
If you are building a machine learning model, follow this workflow: Attrition datasets are usually imbalanced (more people stay
Targeted at high-performing employees in "Gold" roles with low stock options.
Better for capturing complex interactions between satisfaction and pay. Years at Company, Years in Current Role, Performance
Addressing attrition in employees who have been in the same role for more than 3 years. To help you with a more specific guide,