Course Content

  • 2_3_Handling_Missing_Data

Course Content


Replacing the Value That Isn't There:

  • Using an Arbitrary Value as a Substitute.
  • Using Mode to Replace.
  • Substituting Median
  • Forward fill (replacing with preceding value).
  • Backward fill by replacing with the following value.
  • Interpolation
  • Assume the Most Common Value.

Missing data causes a slew of issues. To begin with, the lack of data diminishes statistical power, which refers to the likelihood that the test will reject the null hypothesis if it is wrong. Second, missing data can lead to parameter estimation bias. Third, it may reduce the sample's representativeness.

When dealing with missing data, data scientists have two options for resolving the problem: imputation or data removal. For missing data, the imputation process generates credible predictions. When the percentage of missing data is low, it's the most beneficial.

Recommended Courses

Share With Friend

Have a friend to whom you would want to share this course?

Download LearnVern App

App Preview Image
App QR Code Image
Code Scan or Download the app
Google Play Store
Apple App Store
598K+ Downloads
App Download Section Circle 1
4.57 Avg. Ratings
App Download Section Circle 2
15K+ Reviews
App Download Section Circle 3
  • Learn anywhere on the go
  • Get regular updates about your enrolled or new courses
  • Share content with your friends
  • Evaluate your progress through practice tests
  • No internet connection needed
  • Enroll for the webinar and join at the time of the webinar from anywhere