Pyspark | Linear regression using Apache MLlib

19 Jul, 2019
Problem Statement: Build a predictive Model for the shipping company, to find an estimate of how many Crew members a ship requires.The dataset contains 159 instances with 9 features.
The Description of dataset is as below:Let’s make the Linear Regression Model, predicting Crew members
Attached dataset: cruise_ship_info
import pysparkfrom pyspark.sql import SparkSession#SparkSession is now the entry point of Spark#SparkSession can also be construed as gateway to spark libraries  #create instance of spark classspark=SparkSession.builder.appName('housing_price_model').getOrCreate()  #create spark dataframe of input csv filedf=spark.read.csv('D:\python coding\pyspark_tutorial\Linear regression\cruise_ship_info.csv'                  ,inferSchema=True,header=True)df.show(10)
Output :
+-----------+-----------+---+------------------+----------+------+------+-----------------+----+
|  Ship_name|Cruise_line|Age|           Tonnage|passengers|length|cabins|passenger_density|crew|
+-----------+-----------+---+------------------+----------+------+------+-----------------+----+
|    Journey|    Azamara|  6|30.276999999999997|      6.94|  5.94|  3.55|            42.64|3.55|
|      Quest|    Azamara|  6|30.276999999999997|      6.94|  5.94|  3.55|            42.64|3.55|
|Celebration|   Carnival| 26|            47.262|     14.86|  7.22|  7.43|             31.8| 6.7|
|   Conquest|   Carnival| 11|             110.0|     29.74|  9.53| 14.88|            36.99|19.1|
|    Destiny|   Carnival| 17|           101.353|     26.42|  8.92| 13.21|            38.36|10.0|
|    Ecstasy|   Carnival| 22|            70.367|     20.52|  8.55|  10.2|            34.29| 9.2|
|    Elation|   Carnival| 15|            70.367|     20.52|  8.55|  10.2|            34.29| 9.2|
|    Fantasy|   Carnival| 23|            70.367|     20.56|  8.55| 10.22|            34.23| 9.2|
|Fascination|   Carnival| 19|            70.367|     20.52|  8.55|  10.2|            34.29| 9.2|
|    Freedom|   Carnival|  6|110.23899999999999|      37.0|  9.51| 14.87|            29.79|11.5|
+-----------+-----------+---+------------------+----------+------+------+-----------------+----+

#prints structure of dataframe along with datatypedf.printSchema()
Output :
#In our predictive model, below are the columnsdf.columns
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 StringIndexerindexer=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 modelfor item in indexed.head(5):    print(item)    print('\n')
Output :
Row(Ship_name='Journey', Cruise_line='Azamara', Age=6, 
Tonnage=30.276999999999997, passengers=6.94, length=5.94, 
cabins=3.55, passenger_density=42.64, crew=3.55, cruise_cat=16.0)

Row(Ship_name='Quest', Cruise_line='Azamara', Age=6, 
Tonnage=30.276999999999997, passengers=6.94, length=5.94, 
cabins=3.55, passenger_density=42.64, crew=3.55, cruise_cat=16.0)

Row(Ship_name='Celebration', Cruise_line='Carnival', Age=26, 
Tonnage=47.262, passengers=14.86, length=7.22, 
cabins=7.43, passenger_density=31.8, crew=6.7, cruise_cat=1.0)

Row(Ship_name='Conquest', Cruise_line='Carnival', Age=11, 
Tonnage=110.0, passengers=29.74, length=9.53,
 cabins=14.88, passenger_density=36.99, crew=19.1, cruise_cat=1.0)

Row(Ship_name='Destiny', Cruise_line='Carnival', Age=17, 
Tonnage=101.353, passengers=26.42, length=8.92, 
cabins=13.21, passenger_density=38.36, crew=10.0, cruise_cat=1.0)

from pyspark.ml.linalg import Vectorsfrom pyspark.ml.feature import VectorAssembler#creating vectors from features#Apache MLlib takes input if vector formassembler=VectorAssembler(inputCols=['Age', 'Tonnage', 'passengers', 'length', 'cabins', 'passenger_density', 'cruise_cat'],outputCol='features')output=assembler.transform(indexed)output.select('features','crew').show(5)#output as below
Output :
#final data consist of features and label which is crew.final_data=output.select('features','crew')#splitting data into train and testtrain_data,test_data=final_data.randomSplit([0.7,0.3])train_data.describe().show()
Output :
test_data.describe().show()
Output :
#import LinearRegression libraryfrom pyspark.ml.regression import LinearRegression#creating an object of class LinearRegression#object takes features and label as input argumentsship_lr=LinearRegression(featuresCol='features',labelCol='crew')#pass train_data to train modeltrained_ship_model=ship_lr.fit(train_data)#evaluating model trained for Rsquared errorship_results=trained_ship_model.evaluate(train_data)  print('Rsquared Error :',ship_results.r2)#R2 value shows accuracy of model is 92%#model accuracy is very good and can be use for predictive analysis
Output :
#testing Model on unlabeled data#create unlabeled data from test_data#testing model on unlabeled dataunlabeled_data=test_data.select('features')unlabeled_data.show(5)
Output :
predictions=trained_ship_model.transform(unlabeled_data)predictions.show()#below are the results of output from test data
Output :
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