PySpark Linear Regression Get Coefficients

In this tutorial series, we are going to cover Linear Regression using Pyspark. Linear Regression is a machine learning algorithm that is used to perform regression methods. Linear Regression is a supervised machine learning algorithm where we know inputs as well as outputs.

Loading Dataframe :

We will be using the data for “E-commerce Customer Data for a company’s website and mobile app”. The task is to predict the customer’s yearly spending on the company’s product.

Dataset link: [https://www.kaggle.com/datasets/pawankumargunjan/ecommercecustomers]

Step 1: Starting the Pyspark Server:

Python3

# Starting the Spark Session
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('LinearRegression').getOrCreate()
spark

Output:

SparkSession - in-memory
SparkContext

Spark UI
Version
v3.3.1
Master
local[*]
AppName
LinearRegression

Step 2: Load the dataset:

Python3

# Reading the data
df = spark.read.csv('Ecommerce_Customers.csv',inferSchema=True, header=True)

# Showing the data
df.show(5)

Output:

+--------------------+--------------------+----------------+------------------+------------------+------------------+--------------------+-------------------+
|               Email|             Address|          Avatar|Avg Session Length|       Time on App|   Time on Website|Length of Membership|Yearly Amount Spent|
+--------------------+--------------------+----------------+------------------+------------------+------------------+--------------------+-------------------+
|mstephenson@ferna...|835 Frank TunnelW...|          Violet| 34.49726772511229| 12.65565114916675| 39.57766801952616|  4.0826206329529615|  587.9510539684005|
|   hduke@hotmail.com|4547 Archer Commo...|       DarkGreen| 31.92627202636016|11.109460728682564|37.268958868297744|    2.66403418213262|  392.2049334443264|
|    pallen@yahoo.com|24645 Valerie Uni...|          Bisque|33.000914755642675|11.330278057777512|37.110597442120856|   4.104543202376424| 487.54750486747207|
|riverarebecca@gma...|1414 David Throug...|     SaddleBrown| 34.30555662975554|13.717513665142507| 36.72128267790313|   3.120178782748092|  581.8523440352177|
|mstephens@davidso...|14023 Rodriguez P...|MediumAquaMarine| 33.33067252364639|12.795188551078114| 37.53665330059473|   4.446308318351434|  599.4060920457634|
+--------------------+--------------------+----------------+------------------+------------------+------------------+--------------------+-------------------+
only showing top 5 rows

Step 3: Check the columns name

Python3

#Shows the columns of the data
df.columns

Output:

 ['Email',
 'Address',
 'Avatar',
 'Avg Session Length',
 'Time on App',
 'Time on Website',
 'Length of Membership',
 'Yearly Amount Spent']

Step 4: The next task is to assemble the data in form of vectors which will be the “features”.

Python3

from pyspark.ml.feature import VectorAssembler

assembler = VectorAssembler(
    inputCols=['Avg Session Length', "Time on App", "Time on Website", 'Length of Membership'],
    outputCol="features")

output = assembler.transform(df)
output.select("features").show(5)

Output:

+--------------------+
|            features|
+--------------------+
|[34.4972677251122...|
|[31.9262720263601...|
|[33.0009147556426...|
|[34.3055566297555...|
|[33.3306725236463...|
+--------------------+
only showing top 5 rows

Step 5: Split the whole data into train data and test data which will be used for training and testing respectively.

Python3

final_data = output.select("features",'Yearly Amount Spent')
train_data,test_data = final_data.randomSplit([0.7,0.3])

Let’s describe the train data and test data.

Python3

train_data.describe().show()

test_data.describe().show()

Output:

+-------+-------------------+
|summary|Yearly Amount Spent|
+-------+-------------------+
|  count|                357|
|   mean|  496.7071530755217|
| stddev|  80.03111843524778|
|    min| 256.67058229005585|
|    max|  765.5184619388373|
+-------+-------------------+

+-------+-------------------+
|summary|Yearly Amount Spent|
+-------+-------------------+
|  count|                143|
|   mean| 505.82213623310577|
| stddev|  77.39011604239676|
|    min|  275.9184206503857|
|    max|  744.2218671047146|
+-------+-------------------+

Step 6:  create a model for Linear Regression and fit it on training data.

Python3

from pyspark.ml.regression import LinearRegression

# Create a Linear Regression Model object
lr = LinearRegression(labelCol='Yearly Amount Spent')

# Fit the model to the data and call this model lrModel
lrModel = lr.fit(train_data)
lrModel

Output:

LinearRegressionModel: uid=LinearRegression_74214a54e364, numFeatures=4

Step 7: Print the coefficient and Intercept of the model

Python3

# Print the coefficients and intercept for linear regression
print("Coefficients: {}".format(lrModel.coefficients))
print('Intercept: {}'.format(lrModel.intercept))

Output:

Coefficients: [25.964105285025216,38.93669968512164,0.2862951403317341,61.42916517189798]
Intercept: -1055.4964671721655

Step 8: Evaluation of model on test data:

Python3

test_results = lrModel.evaluate(test_data)

#Printing Residuals which is the difference between the actua
#l value and the value predicted by the model (y-ŷ) for any given point
test_results.residuals.show(5)

Output:

+-------------------+
|          residuals|
+-------------------+
| 11.275316471318774|
| 0.6070843579793177|
|  6.966802347383464|
| -6.151576882623033|
|-7.3822955579703375|
+-------------------+
only showing top 5 rows

Step 9: Prediction on new dataset

Python3

unlabeled_data = test_data.select('features')

predictions = lrModel.transform(unlabeled_data)
predictions.show(5)

Output:

+--------------------+------------------+
|            features|        prediction|
+--------------------+------------------+
|[29.5324289670579...| 397.3650346013087|
|[30.5743636841713...|441.45732940008634|
|[30.9716756438877...|487.67180740950926|
|[31.0613251567161...|493.70703494052464|
|[31.1280900496166...|  564.634982305025|
+--------------------+------------------+
only showing top 5 rows

Step 10: Calculating Root Mean Squared Error and Mean Squared Error for checking the efficiency of our model:

Python3

print("RMSE: {}".format(test_results.rootMeanSquaredError))
print("MSE: {}".format(test_results.meanSquaredError))

Output:

RMSE: 9.965510046039142
MSE: 99.31139047770706

Step 11: Stop the session

Python3

spark.stop()