Handling Missing Values is of the crucial steps in Data Preprocessing.

There are three ways to handle Missing Values:

1. Ignoring the Null Values: Simply Drop the null value rows in the Data.

2. Imputation: Filling the Misiing values from the known part of the Data. 

3. An Extension To Imputation: Making the Rows with missing values unique.

Imputers are of two types:

1.Univariate Imputer : Uses a Single column to fill the missing Data.

 SimpleImputer(Strategy='mean')
 Strategy can be Mean,Median,Most Frequent, Constant (Fill value). 
 Mean,Medain helps when working with numerical data. Most frequent used for Categorical Data.
      
2.Multivariate Imputer: Estimates values to impute for each column with missing values from all the other features.

IterativeImputer: Passes Regressors as estimators for prediction missing values.

KNN Imputer: Uses nearest Samples to fill the missing values of the data.

Extension to Imputation:

Filling missing data, makes missing part data highly valued (or) priortized beacuse mostly we are using Measure's of Central Tendancy
(Or) other data features which uses regressors, nearest_neghibours. We can't rely on that type of data. Solution is the we keep track
of record whether it is a missing value or not.