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# Installing PySpark for Machine Learning

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@ImaginativeNewton

silhouette

four

Cluster 0

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

<p>True</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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