Course: Missing Data Handling: Imputation

in course jupyter-notebook platzi python tutorial missing-values eda

September 10, 2022

Image description of course

By detecting and exploring missing values in datasets, you can enrich them through data imputation. In this way, you can run analyses with better information. This course teaches you how to treat variables and run the appropriate imputation method for your data set.

  • Use model-based imputation methods such as KNN and MICE.
  • Implement donor-based imputation methods.
  • Prepare categorical variables for imputation.
Details
Posted on:
September 10, 2022
Length:
1 minute read, 61 words
Categories:
course jupyter-notebook platzi python tutorial missing-values eda
Tags:
course jupyter-notebook platzi python tutorial missing-values eda
See Also:
Platzi Reviews - GraphQL Server
Course: Missing Data Handling: Detection and Exploration
Course: Exploratory Data Analysis