Build a Classification Model using PyCaret

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build an end-to-end classification model using PyCaret

Learn how to interpret a classification model

Clock2 hours
Comment Dots英语(English)

In this 1-hour long project-based course, you will create an end-to-end classification model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can accurately predict whether a teacher's project proposal was accepted, based on the data they provided in their application. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for classification. 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 In order to be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.


  • Python Programming
  • Machine Learning
  • classification
  • PyCaret



  1. Introduction and setup of the environment

  2. Load and prepare the data

  3. Prepare text data

  4. Build Classification Model

  5. Evaluate Model

  6. Interpret the final Model

  7. Deploy Model






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