用scikit-learn预测员工流失率

Predict Employee Turnover with scikit-learn

768 次查看
Rhyme
Coursera
  • 完成时间大约为 2 个小时
  • 中级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

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

课程概况

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.

课程大纲

Project: Predict Employee Turnover with scikit-learn

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!

课程项目

Introduction and Importing Libraries

Exploratory Data Analysis

Encode Categorical Features

Visualize Class Imbalance

Create Training and Test Sets

Build a Decision Tree Classifier with Interactive Controls

Build a Decision Tree Classifier with Interactive Controls (Continued)

Build a Random Forest Classifier with Interactive Controls

Feature Importance and Evaluation Metrics

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