Handling missing values :

Techniques to fill missing values (Data Imputation):

Importing the libraries:

 

Loading the titanic dataset:

 
Printing first five rows:

 

To find number of rows and columns:

 

Overall information about the dataset:

 

Finding missing values:

 

Visualizing the null values using heatmap:

 

Method:1
1) Deleting the rows with missing values

Deleting the body, cabin, home.dest, boat columns:

 

Method:2
2) Filling with mean or median or mode value

Filling NaN with mean value in the age column:

Filling NaN with median value in the age column:

 
Filling NaN with mode value in the age column:

Method:3
3) Replacing with previous(forward fill) or next value(backward fill)

Filling values with previous value (Forward fill):

 
Filling values with next value(Backward fill):

 

Method : 4
4) Filling with a single value

Filling NaN with single value=30:

 

Method : 5
5) Interpolation method

Filling NaN with interpolate method:

 





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