Guided Project: Creating a Kaggle Workflow

Learn how to create and use a machine learning workflow with Kaggle's 'Titanic' Competition


  • Learn to use Jupyter notebook while working with Kaggle competitions.
  • Learn why workflows are important for machine learning, and create a Kaggle workflow.
  • Learn how to use functions to automate and simplify repetitive machine learning tasks.

Mission Outline

1. Introducing Data Science Workflows
2. Preprocessing the Data
3. Exploring the Data
4. Engineering New Features
5. Selecting the Best-Performing Features
6. Selecting and Tuning Different Algorithms
7. Making a Submission to Kaggle
8. Next Steps

Course Info:

Kaggle Fundamentals


The average completion time for this course is 10-hours.

This course requires a premium subscription, and includes three missions and one guided project.  It is the 27th course in the Data Scientist in Python path.


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