학생용

Predict Employee Turnover with scikit-learn

4.4
별점
226개의 평가
제공자:
Coursera Project Network
7,012명이 이미 등록했습니다.
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Apply decision trees and random forests with scikit-learn to classification problems

Interpret decision trees and random forest models using feature importances

Tune model hyperparamters to improve classification accuracy

Create interactive, GUI components in Jupyter notebooks using widgets

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

Welcome to this project-based course on Predicting Employee Turnover with Decision Trees and Random Forests using scikit-learn. In this project, you will use Python and scikit-learn to grow decision trees and random forests, and apply them to an important business problem. Additionally, you will learn to interpret decision trees and random forest models using feature importance plots. Leverage Jupyter widgets to build interactive controls, you can change the parameters of the models on the fly with graphical controls, and see the results in real time! This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed.

개발할 기술

Decision TreeMachine LearningRandom ForestclassificationScikit-Learn

단계별 학습

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

  1. Introduction and Importing Libraries

  2. Exploratory Data Analysis

  3. Encode Categorical Features

  4. Visualize Class Imbalance

  5. Create Training and Test Sets

  6. Build a Decision Tree Classifier with Interactive Controls

  7. Build a Decision Tree Classifier with Interactive Controls (Continued)

  8. Build a Random Forest Classifier with Interactive Controls

  9. Feature Importance and Evaluation Metrics

안내형 프로젝트 진행 방식

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

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

PREDICT EMPLOYEE TURNOVER WITH SCIKIT-LEARN의 최상위 리뷰

모든 리뷰 보기

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