At a Glance

Name: Learn Explainable AI: Employee Retention Use Case
Price: Free CAD
Rating: 5.0 (2 reviews)
Author: karan_goswami, faranak_heidari, victoria_nadar, joseph_santarcangelo

Learn Explainable AI (XAI) techniques analyzing employee retention and uncover HR insights. Build interpretable models to predict retention and identify key drivers behind employee decisions. In this project, you’ll learn data preprocessing, feature engineering, and building AI models with IBM’s AI Explainability 360 toolkit (AIX 360). Leverage tools like SHAP for feature importance and generative AI (gen AI) to generate detailed explanations and actionable HR strategies, helping HR teams make smarter, transparent decisions. Perfect for data scientists and AI enthusiasts.

Imagine being able to predict which employees are at risk of leaving your organization and, more importantly, understanding why. Employee turnover can disrupt operations and increase costs, but what if you had an AI-driven tool that not only forecasts attrition but also explains the reasoning behind each prediction? Discover how to apply Explainable AI (XAI) techniques using IBM's AI Explainability 360 (AIX 360) toolkit to predict and understand employee retention, enabling organizations to make data-driven decisions with transparency and trust. This project focuses on building interpretable AI models that identify key factors influencing whether employees stay or leave a company. Using Python, scikit-learn, and advanced XAI tools such as TED_CartesianExplainer, you’ll preprocess data, create actionable explanations, and build models that empower HR teams with meaningful insights.

Why this topic is important

Employee retention is critical for organizational stability and success, and understanding the reasons behind employee turnover can significantly impact business strategies. By completing this project, you’ll learn how to build AI models that not only predict retention but also provide clear, actionable explanations for their decisions. Leveraging IBM's AIX 360 toolkit, this project demonstrates the importance of explainability in AI, enabling data scientists and HR professionals to bridge technical insights with actionable outcomes for improved workforce management.

A look at the project ahead

In this project, you’ll work with a real-world employee dataset to uncover the factors influencing retention through transparent AI models. Learn how to preprocess data, generate domain-specific explanations, and evaluate predictions using techniques like feature engineering, random forest classifier and TED_CartesianExplainer.

By the end of the project, you will:

Understand how to preprocess datasets for AI-driven employee retention analysis, including encoding categorical variables and scaling numerical features.
Build and train interpretable models using random forest classifiers and Explainable AI tools like TED Cartesian Explainer.
Leverage SHAP to analyze feature importance and uncover the role of key factors in employee retention.
Generate and encode explanations for model predictions, providing actionable insights into employee retention trends.
Use Generative AI to provide detailed explanations for predictions and generate actionable HR strategies to address key retention challenges.
Evaluate the accuracy of predictions and explanations to ensure the reliability and transparency of your models.

What you’ll need

To successfully complete this project, you’ll need:

A foundational understanding of Python programming and libraries such as pandas, scikit-learn, and matplotlib.
Basic knowledge of AI and machine learning concepts, especially in classification tasks.
A web browser to access tools and run your code.

Offered By: IBMSkillsNetwork

Learn Explainable AI: Employee Retention Use Case

At a Glance

Why this topic is important

A look at the project ahead

What you’ll need

This Guided Project is part of the following Learning Paths