Predict Employee Turnover with scikit-learn

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In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Decision TreeMachine LearningRandom ForestclassificationScikit-Learn

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. 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

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

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Frequently Asked Questions

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