SimpleImputer is a class in scikit-learn, a popular machine learning library in Python, used for handling missing values in datasets. It provides a simple strategy for imputing missing values, such as filling missing entries with the mean, median, most frequent value, or a constant. Here’s a basic example of how you might use SimpleImputer: This…
hen dealing with missing data of type categorical, several methods can be used to impute the missing values. Here are some common approaches: The choice of imputation method depends on the nature of the data, the underlying patterns, and the goals of the analysis. Always consider the context of the data and the potential impact…
There are many ways to address missing data, each with pros and cons. Let’s take a look at the less complex options: Option 1: Delete data with missing rows. When we have a model that cannot handle missing data, the most prudent thing to do is to remove rows that have information missing. Let’s remove…
Do we have a complete dataset in a real-world scenario? No. We know from history that there is missing information in our data! How can we tell if the data we have available is complete? We could print the entire dataset, but this could involve human error, and it would become impractical with this many…
