Multivariate K-Nearest Neighbors

Improve your predictions by using more features.


  • Learn how to use multiple variables in machine learning models.
  • Learn how to prepare columns by normalizing and handling missing values.

Mission Outline

1. Recap
2. Removing features
3. Handling missing values
4. Normalize columns
5. Euclidean distance for multivariate case
6. Introduction to scikit-learn
7. Fitting a model and making predictions
8. Calculating MSE using Scikit-Learn
9. Using more features
10. Using all features
11. Next steps
12. Takeaways

Course Info:

Machine Learning Fundamentals


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

This course requires a premium subscription. This course includes five paid missions and one guided project.  It is the 17th course in the Data Scientist in Python path.


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