Missing value inputting

This approach is useful when you have categorical data. The intuition behind this approach is that missing values may correlate with other variables, and removing them will result in a loss of information that can affect the model significantly.
For example, if we have a binary variable with two possible values, -1 and 1, we can add another value (0) to indicate a missing value. You can use the following code to replace the null values of the Cabin feature with U0:

# replacing the missing value in cabin variable "U0"
df_titanic_data['Cabin'][df_titanic_data.Cabin.isnull()] = 'U0'