Employee Attrition Prediction Using Machine Learning

Offered By
Coursera Project Network
In this Free Guided Project, you will:

Understand the theory and intuition behind logistic regression classifier models

Build, train and test a logistic regression classifier model in Scikit-Learn

Perform data cleaning, feature engineering and visualization

Showcase this hands-on experience in an interview

Clock2 hours
BeginnerBeginner-friendly
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this project-based course, we will build, train and test a machine learning model to predict employee attrition using features such as employee job satisfaction, distance from work, compensation and performance. We will explore two machine learning algorithms, namely: (1) logistic regression classifier model and (2) Extreme Gradient Boosted Trees (XG-Boost). This project could be effectively applied in any Human Resources department to predict which employees are more likely to quit based on their features. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Requirements

Basic python programming and Machine Learning Knowledge

Skills you will develop

Machine Learning RegressionData ScienceArtificial Neural NetworkMachine Learningregression

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the Problem Statement and Business Case

  2. Import Libraries and Datasets

  3. Perform Data Visualization

  4. Perform Data Visualization - Continued

  5. Create Training and Testing Datasets

  6. Understand the Intuition Behind Logistic Regression

  7. Train and Evaluate a Logistic Regression Model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.