HR Analytics Project

The Data: This dataset offers insights into HR analytics, exploring employee-related factors impacting organizational dynamics. Features include satisfaction levels, performance evaluations, turnover rates, etc. Variable descriptions are in "data-variables-description.pdf."

Section A (Data Exploration and Pre-processing)

1. Exploring data using tables, visualizations, etc.

   • Overall diversity profile
   • Relationship between supervisor and performance
   • Areas with potential pay inequity

2. Applying different pre-processing methods for subsequent models.

Section B (Dimensionality Reduction)

1. Applying PCA algorithm.
2. Creating a scatter plot color-coded by employment status.
3. Identifying features strongly represented in each PCA component.
   • Most effective features for employee separation
   • Least effective features for separation
4. Creating a biplot using the first two principal components (PC1 and PC2).
5. Using PCA to identify outliers.

Section C (Classification)

Predictin the employment status (Active/Terminated) with a predictive model.

Section D (Regression)

Prediction the time until employee termination from the last performance review.

Section E

• Calculating Employee Retention Rate (2008-2017) for each recruitment source.
• Calculating diversity index for each department based on race, gender, and age.
• Creating a map visualization showing employee distribution by state.