Employee Turnover Insights: Using Survival Analysis

Project Overview

Conducted a detailed analysis of my former organization's employee turnover for the period 1st Jan, 2022 tp 15th March, 2023, examining historical trends, departmental and band-wise attrition, and tenure-related insights. Utilized predictive analytics from the Employee Engagement Survey 2022, gathered firsthand feedback from former employees, and performed bivariate analyses. Recently incorporated Survival Analysis using Kaplan Meier estimator and COX proportional hazards model to uncover temporal patterns and predictors. The findings informed strategic decisions for improving employee retention.

Analysis Steps

1. Pre-Insights Primer:

   • Analyzing natue of attrition and looked at attrition through the years.

2. Core Observations:

   • Analyzing the attrition with respect to department, band, tenure and reason for turover. Identifying regretable losses.

3. Predictive Analysis:

   • Using the insights from the employee engagement survey to predict the vulnerable groups.

4. Reconnecting with Former Employees:

   • Finding out the reaons of turnover from the employee who left the organization before the start of this analysis.

5. Bivariate Analysis:

   • Identifying the band group and departments at the higest risk of turnover.

Survival Analysis

Utilizing Kaplan-Meier estimator and Cox proportional hazards method.

Statistical Probability of Employees leaving the Organization

Usage

Prerequisites

• Python 3.10.12
• Required Python packages:
  • lifelines 0.27.8

License

This project is licensed under the Raza Mehar License. See the LICENSE.md file for details.

Contact

For any questions or clarifications, please contact Raza Mehar at [[email protected]].