Build a Clustering Model using PyCaret

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

build an end-to-end clustering model using PyCaret

Learn how to interpret a clustering model

Clock2 hours
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project-based course, you will create an end-to-end clustering model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can segment a wholesale customers based on their historical purchases. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering. Here are the main steps you will go through: frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model. This guided project is for seasoned Data Scientists who want to build a accelerate the efficiency in building POC and experiments by using a low-code library. It is also for Citizen data Scientists (professionals working with data) by using the low-code library PyCaret to add machine learning models to the analytics toolkit. To be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning.

Skills you will develop

Python ProgrammingMachine LearningPyCaretclustering

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 setup of the environment

  2. Load and prepare the data

  3. Evaluate Model

  4. Preprocess Data

  5. Build Clustering Model

  6. Evaluate Model

  7. Interpret and Explain Model

  8. Deploy 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

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