Installing PySpark for Machine Learning
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Questions and Answers

What evaluation metric was used in the K-Means clustering algorithm?

silhouette

At what number of clusters did the plot show an inflection point like an elbow?

four

Which customer segment displayed low recency, frequency, and monetary value?

  • Cluster 0 (correct)
  • Cluster 3
  • Cluster 1
  • Cluster 2

    • Customers in Cluster 2 tend to buy high-value items or make bulk purchases.

    <p>True</p> Signup and view all the answers

    What function should be used in PySpark to read a CSV file?

    <p>spark.read.csv()</p> Signup and view all the answers

    What are the three main variables used in RFM analysis?

    <p>Recency, Frequency, Monetary Value</p> Signup and view all the answers

    Standardizing data in machine learning ensures that all variables are on the same scale.

    <p>True</p> Signup and view all the answers

    The most popular technique to determine the number of clusters in K-Means clustering is the _______ method.

    <p>elbow</p> Signup and view all the answers

    Match the data preprocessing step with its description:

    <p>Calculating Recency = Determining how recently each customer made a purchase Calculating Frequency = Counting how often each customer bought something Calculating Monetary Value = Finding the total amount spent by each customer</p> Signup and view all the answers

    Study Notes

    Installing PySpark

    • Installing PySpark via !pip install pyspark in a Python file in a Jupyter Notebook
    • PySpark is a Python library for Apache Spark, used for data analysis and machine learning

    End-to-End Customer Segmentation Project

    • Using K-Means clustering to perform customer segmentation on an e-commerce dataset
    • Learning concepts:
      • Reading csv files with PySpark
      • Exploratory Data Analysis with PySpark
      • Grouping and sorting data
      • Performing arithmetic operations
      • Aggregating datasets
      • Data Pre-Processing with PySpark
      • Working with datetime values
      • Type conversion
      • Joining two dataframes
      • The rank() function
      • PySpark Machine Learning

    Step 1: Creating a SparkSession

    • Creating a SparkSession using spark = SparkSession.builder.appName("Datacamp Pyspark Tutorial").config("spark.memory.offHeap.enabled","true").config("spark.memory.offHeap.size","10g").getOrCreate()
    • Setting a name for the application and caching data in off-heap memory

    Step 2: Creating the DataFrame

    • Reading the dataset using df = spark.read.csv('datacamp_ecommerce.csv',header=True,escape="\"")
    • Defining an escape character to avoid commas in the csv file

    Step 3: Exploratory Data Analysis

    • Counting the number of rows in the dataframe using df.count()
    • Finding the number of unique customers using df.select('CustomerID').distinct().count()
    • Finding the country with the most purchases using df.groupBy('Country').agg(countDistinct('CustomerID').alias('country_count')).show()
    • Finding the most recent purchase using df.select(max("date")).show()
    • Finding the earliest purchase using df.select(min("date")).show()

    Step 4: Data Pre-processing

    • Creating new features:
      • Recency: how recently a customer made a purchase
      • Frequency: how often a customer makes a purchase
      • Monetary Value: how much a customer spends on average
    • Pre-processing the dataframe to create these features

    Step 5: Building the Machine Learning Model

    • Standardizing the dataframe using VectorAssembler and StandardScaler
    • Building a K-Means clustering model using PySpark's machine learning API
    • Finding the number of clusters using the elbow method
    • Building the K-Means clustering model with 4 clusters
    • Making predictions using the model

    Step 6: Cluster Analysis

    • Analyzing the customer segments using the K-Means clustering model
    • Visualizing the recency, frequency, and monetary value of each customer segment
    • Characteristics of each cluster:
      • Cluster 0: low recency, frequency, and monetary value
      • Cluster 1: high recency, low frequency, and low monetary value
      • Cluster 2: medium recency, frequency, and high monetary value
      • Cluster 3: high recency, frequency, and low monetary value

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    Description

    Learn how to install PySpark in your Jupyter Notebook with a few lines of code. Follow this step-by-step guide to get started with your machine learning project.

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