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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