Podcast Beta
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?
Customers in Cluster 2 tend to buy high-value items or make bulk purchases.
Signup and view all the answers
What function should be used in PySpark to read a CSV file?
Signup and view all the answers
What are the three main variables used in RFM analysis?
Signup and view all the answers
Standardizing data in machine learning ensures that all variables are on the same scale.
Signup and view all the answers
The most popular technique to determine the number of clusters in K-Means clustering is the _______ method.
Signup and view all the answers
Match the data preprocessing step with its description:
Signup and view all the answers
Study Notes
Installing PySpark
- Installing PySpark via
!pip install pysparkin 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
VectorAssemblerandStandardScaler - 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
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
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.
