PyCaret: Anatomy of Regression

Offered By
Coursera Project Network
In this Guided Project, you will:

How to create a regression environment and compare model performance

Create best performing regression models

Using hyper parameter to tune models

Clock2 hours 15 mins
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 2 hour and 15 mins long project-based course, you will learn how to ow to set up PyCaret Environment and become familiar with the variety of data preparing tasks done during setup, be able to create, see and compare the performance of several models, learn how to tune your model without doing an exhaustive search, create impressive visuals of models, interpret models with the wrapper around SHAP Library and much more & all this with just a few lines of code. Note: This project works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

PyCaretMachine LearningPython ProgrammingregressionAuto ML

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. Task 1: Import Data, Initial dataset check and setup Pycaret environment

  2. Task 2: Create regression environment and compare model performance

  3. Task 3: Create best performing regression models

  4. Task 4: Hyper Parameter tuning the models

  5. Task 5: Stacking & Ensemble

  6. Task 6: Visualize and interpret the machine learning model

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

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.