Missing Value
Explore techniques for handling missing data in machine learning datasets. Learn to use Scikit-Learn's SimpleImputer and KNNImputer to effectively impute missing values and improve your models.
We'll cover the following...
We'll cover the following...
Why missing values are important
Missing values are very common in real datasets. For different reasons, the datasets contain missing values as blank, nan, inf, or other specified values. In some cases, some normal values are also considered to be a missing value, such as 0 or 1. Why do we care about the missing values?
- Some algorithms or some implementations can’t deal with the missing values. They assume the dataset is complete.
- The missing values would impact the performance of our model.
In most cases, the first is the main reason.
In some cases, you may think about just dropping the rows or columns with too many missing values. It’s a good idea if only a small part of the data is dropped. However, when the ...