Association Rule Mining (Apriori Algorithm) Using Python

 

Association Rule Mining (Apriori Algorithm)

Note: Association Rule Mining is an Unsupervised Machine Learning technique. It is mainly used for Market Basket Analysis to discover relationships between items frequently purchased together.

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🟦 Program Aim

Aim:

To implement the Association Rule Mining (Apriori Algorithm) using Python and identify products that are frequently purchased together.

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🟩 Algorithm Used

Apriori Algorithm

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🟨 Problem Statement

A supermarket wants to analyze customer shopping patterns. By examining previous transactions, the store aims to identify products that are frequently purchased together. This information helps improve product placement, cross-selling, and promotional strategies.

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🟪 Step 1: Install Required Library

Install the mlxtend package (only once).

pip install mlxtend

Explanation

• mlxtend stands for Machine Learning Extensions.
• It provides the Apriori algorithm and functions for generating association rules.

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🟦 Step 2: Import Required Libraries

import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

Explanation

• pandas → Used to create and manipulate data.
• TransactionEncoder → Converts transaction data into a True/False matrix.
• apriori() → Finds frequent itemsets.
• association_rules() → Generates association rules from frequent itemsets.

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🟩 Step 3: Create the Transaction Dataset

transactions = [
    ["Milk", "Bread", "Butter"],
    ["Milk", "Bread"],
    ["Milk", "Butter"],
    ["Bread", "Butter"],
    ["Milk", "Bread", "Butter", "Eggs"],
    ["Bread", "Eggs"],
    ["Milk", "Eggs"]
]

Explanation

Each inner list represents one customer's shopping basket.

Customer	Purchased Items
1	Milk, Bread, Butter
2	Milk, Bread
3	Milk, Butter
4	Bread, Butter
5	Milk, Bread, Butter, Eggs
6	Bread, Eggs
7	Milk, Eggs

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🟨 Step 4: Convert Transactions into Binary Format

encoder = TransactionEncoder()

encoded_data = encoder.fit(transactions).transform(transactions)

df = pd.DataFrame(encoded_data, columns=encoder.columns_)

Explanation

The Apriori algorithm requires data in binary (True/False or 1/0) format.

The dataset becomes:

Bread	Butter	Eggs	Milk
True	True	False	True
True	False	False	True
False	True	False	True
True	True	False	False
True	True	True	True
True	False	True	False
False	False	True	True

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🟦 Step 5: Display the Dataset

print(df)

Explanation

Displays the converted transaction matrix used for mining frequent itemsets.

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🟩 Step 6: Find Frequent Itemsets

frequent_items = apriori(df, min_support=0.3, use_colnames=True)

print(frequent_items)

Explanation

• min_support = 0.3 means an itemset must appear in at least 30% of all transactions.
• use_colnames=True displays product names instead of column numbers.

Example Output:

Support	Itemsets
0.71	{Milk}
0.71	{Bread}
0.57	{Butter}
0.43	{Eggs}
0.43	{Milk, Bread}
0.43	{Milk, Butter}

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🟨 Step 7: Generate Association Rules

rules = association_rules(
    frequent_items,
    metric="confidence",
    min_threshold=0.7
)

print(rules)

Explanation

This step generates association rules using:

• Metric = Confidence
• Minimum Confidence = 70%

Example Rule:

Milk  → Bread

Meaning:

Customers buying Milk are likely to buy Bread as well.

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🟥 Step 8: Display Selected Columns

print(rules[['antecedents',
             'consequents',
             'support',
             'confidence',
             'lift']])

Explanation

This displays the most important measures:

Antecedent	Consequent	Support	Confidence	Lift
Milk	Bread	0.43	0.75	1.05
Bread	Butter	0.43	0.60	1.04

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🟪 Complete Python Program

import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

transactions = [
    ["Milk", "Bread", "Butter"],
    ["Milk", "Bread"],
    ["Milk", "Butter"],
    ["Bread", "Butter"],
    ["Milk", "Bread", "Butter", "Eggs"],
    ["Bread", "Eggs"],
    ["Milk", "Eggs"]
]

encoder = TransactionEncoder()

encoded_data = encoder.fit(transactions).transform(transactions)

df = pd.DataFrame(encoded_data, columns=encoder.columns_)

print("Transaction Dataset")
print(df)

frequent_items = apriori(df,
                         min_support=0.3,
                         use_colnames=True)

print("\nFrequent Itemsets")
print(frequent_items)

rules = association_rules(frequent_items,
                          metric="confidence",
                          min_threshold=0.7)

print("\nAssociation Rules")
print(rules[['antecedents',
             'consequents',
             'support',
             'confidence',
             'lift']])

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🟩 Sample Output

Transaction Dataset

   Bread  Butter  Eggs  Milk
0   True    True  False  True
1   True   False  False  True
2  False    True  False  True
3   True    True  False False
4   True    True   True  True
5   True   False   True False
6  False   False   True  True

Frequent Itemsets

support     itemsets

0.71        {Milk}

0.71        {Bread}

0.57        {Butter}

0.43        {Eggs}

0.43        {Milk, Bread}

...

Association Rules

Milk → Bread

Bread → Butter

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🟦 Step-by-Step Working of the Algorithm

Transaction Data
        │
        ▼
Convert into Binary Matrix
        │
        ▼
Apply Apriori Algorithm
        │
        ▼
Find Frequent Itemsets
        │
        ▼
Generate Association Rules
        │
        ▼
Display Support, Confidence & Lift

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🟨 Important Terms

Term	Description
Support	Frequency of an itemset appearing in all transactions.
Confidence	Probability that customers who buy item A also buy item B.
Lift	Measures the strength of the relationship between two items. A lift value greater than 1 indicates a positive association.
Frequent Itemset	A group of items that appears frequently in the dataset.
Association Rule	A rule showing the relationship between two or more items (e.g., Milk → Bread).

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🌍 Real-Life Applications

• 🛒 Market Basket Analysis
• 🛍 Product Recommendation Systems
• 🏪 Store Shelf Arrangement
• 💳 Banking Product Recommendations
• 🎬 Movie Recommendation Systems
• 🌐 E-commerce Websites (Amazon, Flipkart)
• 🍔 Restaurant Combo Offers

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🎯 Viva Questions

1. What is Association Rule Mining?
2. What is the Apriori Algorithm?
3. Define Support, Confidence, and Lift.
4. What is a Frequent Itemset?
5. Why is TransactionEncoder used?
6. What is the purpose of min_support?
7. What is the purpose of min_threshold in association rules?
8. Give two real-life applications of Association Rule Mining.

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⭐ One-Line Revision

Association Rule Mining uses the Apriori algorithm to discover frequently occurring item combinations and generate rules such as "If a customer buys Milk, they are also likely to buy Bread."