Employee Attrition Prediction Using Machine Learning

제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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

Clock2 hours
Beginner초급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

  • Machine Learning Regression
  • Data Science
  • Artificial Neural Network
  • Machine Learning
  • regression

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.