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.

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.

Utilizing Kaplan-Meier estimator and Cox proportional hazards method.

• Python 3.10.12
• Required Python packages:
  • lifelines 0.27.8

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

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