Executive Summary

This project focuses on understanding the factors behind employee churn. In a knowledge-driven economy, human capital is vital. The aim is to provide actionable insights for HR, enabling them to understand and address the key factors influencing employee turnover. Leveraging a Random Forest and Gradient Boosted model, I seek to identify at-risk employees, facilitating timely intervention strategies that could significantly improve retention rates and overall employee satisfaction.

Problem Definition

Employee churn poses a significant challenge for organizations, impacting not only their cost structure but also team dynamics and company culture. This project addresses the need for an accurate and reliable predictive tool to identify the likelihood of employee attrition. By deciphering the underlying patterns and triggers of employee turnover, HR teams can develop targeted strategies to boost employee engagement, address potential dissatisfaction points, and foster a more positive work environment, ultimately leading to reduced turnover rates.

Data Sources

The project utilizes the "IBM HR Employee Attrition" dataset, a comprehensive repository of employee information that includes various attributes such as age, job role, marital status, education, tenure, and performance metrics. This dataset is invaluable as it provides a holistic view of the workforce, encompassing factors often overlooked in traditional turnover analyses. The depth and variety of the data enable a nuanced understanding of the multifaceted nature of employee turnover, facilitating the development of a more comprehensive predictive model.

Methodology

The methodology adopted for this project involves several key steps:

Data Preprocessing

• Cleaning and transforming the data to ensure it is suitable for analysis.
• Handling missing values, encoding categorical variables, and normalizing the data.

Exploratory Data Analysis (EDA)

• Conducting an in-depth analysis to uncover trends, patterns, and correlations within the data.
• This step is vital for hypothesis generation and understanding the factors that most significantly impact employee churn.

Model Development

• Utilizing machine learning algorithms such as random forests and gradient boosting to develop a predictive model.
• The model is trained on a subset of the data and validated using various metrics to ensure its accuracy and reliability.

Feature Importance Analysis

• Identifying the most influential factors contributing to employee turnover.
• This insight is critical for HR teams to understand which areas require more focus and intervention.