Taming Big Data Analytics by A Tanveer

Author:A, Tanveer [A, Tanveer]
Language: eng
Format: epub
Published: 2020-12-22T16:00:00+00:00

Output :

#columns identified as features are as below:

#['Cruise_line','Age','Tonnage','passengers','length','cabins','passenger_density']

#to work on the features, spark MLlib expects every value to be in numeric form

#feature 'Cruise_line is string datatype

#using StringIndexer, string type will be typecast to numeric datatype

#import library strinindexer for typecasting

from pyspark.ml.feature import StringIndexer

indexer=StringIndexer(inputCol='Cruise_line',outputCol='cruise_cat')

indexed=indexer.fit(df).transform(df)

#above code will convert string to numeric feature and create a new dataframe

#new dataframe contains a new feature 'cruise_cat' and can be used further

#feature cruise_cat is now vectorized and can be used to fed to model

for item in indexed.head(5):

print(item)

print('
')

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