253 ratings
8,023 already enrolled
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
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.
Decision Tree
Machine Learning
Random Forest
classification
Scikit-Learn
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
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
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
by ED
May 12, 2020Excellent approach to predicting employee turnovers!
by AO
Oct 29, 2020It really worth the time and I was exposed to new approach to codes and algorithm.
by AS
Sep 25, 2020A good place to learn the implementation of Random Forest and Decision Trees and how to interpret the results.
by SJ
Apr 27, 2020Excellent course for those who knowledge on the topics mentioned in the content.
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.
You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.
Guided Projects are not eligible for refunds. See our full refund policy.
Financial aid is not available for Guided Projects.
Auditing is not available for Guided Projects.
At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
More questions? Visit the Learner Help Center.
