The term Market Basket Analysis is self-explanatory. In short, it helps find the probability of the items frequently bought together by mining the large volume of transactions for items that were often purchased together. This helps understand the customer better, place the frequently bought items together, provide product recommendations, etc.

Multiple algorithms are available to perform Market Basket Analysis, but Apriori is the most frequently used algorithm because it is easy to use and technically simple.

Apriori Algorithm

Apriori is an unsupervised learning algorithm that uses the below three metrics. Confidence is the metric used to decide the probability of buying a pair/combination.

Support: Refers to the percentage of transactions that includes a particular product. We can remove products that are very rarely bought to speed up the computation.

Support = Number of transactions having item A / Total number of transactions




WHERE ITEM LIKE ALL ('%light cream%'); -- 117

Support(A) = SELECT 117/7501 = 0.015598


WHERE ITEM LIKE ALL ('%chicken%'); -- 450

Support(B) = SELECT 450/7501 = 0.059992


WHERE ITEM LIKE ALL ('%light cream%', '%chicken%'); -- 34

Support(A->B) = SELECT 34/7501 = 0.004533

Confidence: Refers to how a product helped to buy another product.

Confidence = Number of transactions having items A and B /

Number of transactions having item A


Confidence = SELECT 34/117 = 0.290598

Lift: Helps find the association’s strength.

Lift = (Support for A & B) / (Support A * Support B)


Lift = SELECT 0.004533/(0.015598*0.059992) = 4.844215583833

Now let us get into the tools we are using for implementation.

Apriori Function:

Market Basket Analysis Using Snowpark Package Apriori Function

Here is a description of the parameters used in the Apriori function:

transactions: Input data for this algorithm. It should be a Python Dataframe.

min_support: Specifies the minimum frequency of occurrence. There is no point in including an item that is rarely bought.

min_confidence: Represents the minimum influence of product cause to buy another product (it is like conditional probability: the probability of A given B, i.e., A|B).

min_lift: Represents the minimum likelihood of buying items together. If the lift’s value is zero, there is no motive between the given items. The pairs with high lift value mean those are the items frequently bought together.

min_length: It represents the minimum number of items to participate in the analysis. i.e., P(A, B, C), if min_length =3.

For min_length =2, the conditional probability is P(A,B) = p(A|B) p(B).

Suppose we extend this for three variables, as per the chain rule. In that case, the conditional probability is –

P(A,B,C) = P(A|B,C) P(B,C) = P(A|B,C) P(B|C) P(C)

max_length: It represents the maximum number of items to analyze for the association.

We can find the probability of any pair by changing the values for min_support and min_confidence.

Tools Used For Implementation

  • Snowpark
  • Virtual environment
  • Snowflake
  • Python
  • Jupyter notebook

Start With Snowpark

Using Snowpark, we can easily access and process the data using programming languages like Python, Java, and Scala. From snowflake documentation, “The Snowpark library provides an intuitive for querying and processing data in a data pipeline. Using this library, you can build applications that process data in Snowflake without moving data to the system where your application code runs.

Following are the steps to have Snowpark ready on the system.

Step 1: Install Conda to have the notebook on the machine.

Step 2: Snowpark currently supports Python 3.8 and above. You may have other applications running on different Python versions. So, we need to isolate the libraries needed for different applications. Create a virtual environment and enable it. I have created a virtual environment in this example and named it py38_env.

To do this, open an Anaconda prompt from the Anaconda navigator and submit the below command:

(base) PS C:\Users\farid> $ conda create --name py38_env -c python=3.8 numpy pandas

Step 3: Activate the new virtual environment by submitting the below command:

(base) PS C:\Users\farid> $ conda activate py38_env4

Step 4: Install the Snowpark package.

(py38_env) PS C:\Users\farid $ conda install snowflake-Snowpark-python

Step 5: Open a Jupyter notebook

(py38_env) PS C:\Users\farid> jupyter notebook

Creating A Snowpark Session

First, run the pip install to add Snowpark API. Then, follow the code below to establish a Snowpark session and use.

Market Basket Analysis Using Snowpark Package Create Snowpark Session

Fetching The Data From Snowflake

Market Basket Analysis Using Snowpark Package Fetch Data

Here, Transactions is a table in Snowflake demo_db.demo_schema schema.

Inserting The Data Into The Snowflake

Market Basket Analysis Using Snowpark Package Insert Data

Here, we are pushing the data from the Python Dataframe df to Snowflake table demo_db.demo_schema.mba_Snowpark.

About Source Data Set

The source data file has a comma-separated list of products belonging to transactions. This data file I have loaded this into the Snowflake table DEMO_DB.DEMO_SCHEMA.TRANSACTIONS. Below is the snippet of the data file.

Market Basket Analysis Using Snowpark Package Source Data Set

Use the below code snippet to load the sample file into Snowflake:


Here is a snapshot of the table:

Market Basket Analysis Using Snowpark Package Table Snapshot

The result of the Apriori function produces a tuple output. This tuple needs to be converted to a Python list and then load the result into Snowflake using Snowpark API.

Market Basket Analysis Using Snowpark Package Snowpark API

Let us place all these steps in a sequence to automate or run on an ad hoc basis using a single Python command.


#pip install snowflake-Snowpark-python
from snowflake.Snowpark.functions import col
from snowflake.Snowpark import Session, DataFrameWriter, DataFrame
import os
connection_parameters = {
	"account" : os.environ["SNOWSQL_ACT"],
	"user" : os.environ["SNOWSQL_UID"],
	"password" : os.environ["SNOWSQL_PWD"],
	"role" : os.environ["SNOWSQL_ROLE"],
	"database" : os.environ["SNOWSQL_DB"],
	"schema" : os.environ["SNOWSQL_SCHEMA"]
test_session = Session.builder.configs(connection_parameters).create()
print(test_session.sql("select current_account(), current_user(), current_database(), current_schema()").collect())
b = test_session.table("TRANSACTIONS").collect()
transactions = []
for i in range(0, 7500):
for i in range(0, 6):
from apyori import apriori
rules = apriori(transactions = transactions, min_support = 0.003, min_confidence = 0.2, min_lift = 3, min_length = 2, max_length = 2)
results = list(rules)
from datetime import date
import pandas as pd

def inspect(results):
	lhs = [tuple(result[2][0][0])[0] for result in results]
	rhs = [tuple(result[2][0][1])[0] for result in results]
	supports = [result[1] for result in results]
	confidences = [result[2][0][2] for result in results]
	lifts = [result[2][0][3] for result in results]
	min_support = ['PD3Txn' for result in results]
	min_confidence = [0.2 for result in results]
	min_lift = [3 for result in results]
	process_date   = [ for result in results]
	return list(zip(lhs, rhs, supports, confidences, lifts, min_support, min_confidence, min_lift, process_date))
df = pd.DataFrame(inspect(results), columns = ['LHS', 'RHS', 'SUPPORT', 'CONFIDENCE', 'LIFT', 'MIN_SUPPORT', 'MIN_CONFIDENCE', 'MIN_LIFT','PROCESS_DATE'])
df.sort_values(by=['LIFT'], ascending=False)
ddf = test_session.create_dataframe(df)

Please contact us if you have any questions regarding this blog or need help with Market Basket Analysis or other machine learning services.

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