# 3 ways to handle missing values in Machine Learning

## Steps to handle missing values:

1. **Drop the columns that has missing values**
    
    * Here, we drop the columns that have missing values.
        
    * With this approach, there is a chance that we may lose access to lot of useful information.
        
    * You should use this approach only if most of the values in a column are missing and not otherwise.
        

2. **Imputation**
    
    * Here, we replace all the missing values in a column with the average value of that column. For example, if column has 3, 2, 1 as values then we take average of this column that is 2 and replace it with missing values.
        
    * It’s a standard approach.
        
    * The imputed value is not always right, however, it results in good accuracy as compared to the previous way.
        

3. **Extension to Imputation**
    
    * Here, we follow the process of imputation and then for each column with missing value we also create a new column.
        
    * The location at which the value is imputed we mark an entry in the newly created column to specify that this location has imputed value.
        
    * This may increase the accuracy of prediction, but in some cases it can also make no difference.