3 Main Types of Missing Data | Do THIS Before Handling Missing Values!

#ai #ml #datascience #data #machinelearning #artificialintelligence 🔥 This video covers the three main types of missing values: missing completely at random, missing at random and missing not at random. Before moving to the missing value handling step, you need to understand where are the values in the dataset? Why they disappeared? You can proceed to the missing value handling after understanding the statistical effect of the missing data points on your analysis. What if you mistakenly delete an important information which can lead to an underestimation? We bring valuable examples to clearly explain main differences among these three categories. Remember, missing completely at random occurs when the missing data is completely random and does not relate with the observed data. Missing at random refers to those missing values that are related to the observed data. While missing not at random is the most problematic. In that case, reasons are tied to the characteristics of the missing data, making it difficult or impossible to directly infer or predict what those missing values might be based solely on the observed data. 🔍 Key points covered: 0:00 - Introduction. 0:28 - In this video... 0:34 - Types of missing data. 0:42 - Missing Completely at Random. 1:26 - Missing at Random. 2:03 - Missing Not at Random. 2:47 - Subscribe to us! 🔔 Don't forget to like, subscribe, and hit the bell icon to stay updated with our latest videos! 🤖 Note that we use synthetic generations, such as AI-generated images and voices, to enhance the appeal and engagement of our content. 🌐 If you have any questions or topics you want us to cover, leave a comment below. Additionally, share with your thoughts about the content, how do you think we can make them better? Thanks for watching!