Attrition_cb01_gold_hd_2018 〈2025-2026〉

Job Role, Department, Job Level, Business Travel frequency.

Convert categorical variables (like Department ) into numerical values using One-Hot Encoding.

This guide outlines the core components of the dataset and how to use it for predictive modeling. 1. Dataset Overview Attrition_cb01_gold_HD_2018

To analyze attrition effectively, focus on these common data categories: Age, Gender, Marital Status.

Focus on Recall (finding all employees likely to leave) rather than just Accuracy. 5. Strategic Recommendations Job Role, Department, Job Level, Business Travel frequency

Targeted at high-performing employees in "Gold" roles with low stock options.

Identify trends by looking for correlations between these key factors: Attrition_cb01_gold_HD_2018

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.