How Do You Perform Market Basket Analysis In Python?

Summary:  Market Basket Analysis in Python helps retailers analyse customer purchase patterns using data mining techniques. The Apriori algorithm identifies frequent item sets, generating valuable insights for business optimisation.

Introduction

We live in a data-driven world, and data analytics is critical in analysing consumer behaviour and optimising business strategies. Companies are reaping the benefits by assessing consumers’ interest in particular products. Tools like Market Basket Analysis in Python are pivotal in this.

Unfolding the insights and understanding the factors that motivate the customer to purchase becomes easier. It eventually helps improve cross-selling, product placement, and targeted marketing campaigns.

Market Basket Analysis in Python is a data mining technique that helps retailers increase sales by unravelling the details of customer purchasing patterns.

Market Basket Analysis involves working on large data sets. It includes the buying history, revealing product groupings and products purchased together. Hence, this tool becomes quite significant for retailers. This article will explore how to perform Market Basket Analysis in Python. 

What is Market Basket Analysis in Python? 

Market Basket Analysis, also known as Association Analysis or Affinity Analysis, is a data mining technique that unfurls product relationships based on the consumer’s purchase pattern. 

Python provides various libraries and tools to perform Market Basket Analysis efficiently. 

One such commonly used library is the mlxtend library. It offers a range of functionalities for performing association rule mining, including the Apriori algorithm (discussed later), a popular algorithm for Market Basket Analysis. 

By analysing customer transaction data, MBA or Market Basket Analysis allows businesses to uncover patterns, associations, and dependencies that are not immediately apparent. 

This knowledge can be leveraged to optimise business strategies, improve customer experience, and drive revenue growth. The next segment of this blog discusses some of the critical aspects of Market Basket Analysis and provides examples. 

How do you do Market Basket Analysis in Python?

As we have mentioned, several Python libraries are used for MBA. Apriori Algorithm is one of the most commonly used Python libraries for association data mining. 

What is Association Rule Mining?

Apriori Algorithm is used for association rule mining, which unfolds the pattern of items purchased together and concurrence in the same. For example, one can find a correlation and association between different items that customers purchase and the recurring pattern. 

Let’s say Raghav buys a bottle of cold drinks and adds chips and wafers to his basket. The store manager analyses that many other customers, like Raghav, purchase cold drinks, chips, and wafers. So, the manager starts placing these products together, eventually boosting sales. 

This process of analysing the association between different purchased products is called association rule mining. The Apriori algorithm, one of the most popular and efficient algorithms, implements association rule mining. 

How Does the Apriori Algorithm Work? 

The Apriori algorithm is popular in Market Basket Analysis and association rule mining. Its primary purpose is to identify frequent itemsets within a transactional dataset. Here’s an explanation of how the Apriori algorithm works: 

• Support: The algorithm begins by scanning the dataset to calculate each item’s support. Support refers to an item’s frequency in the dataset and is expressed as a percentage or decimal value. 
• Generating Candidate Itemsets: The algorithm generates candidate item sets based on the support values. Initially, it creates a list of frequent 1-item sets, individual items that meet the minimum support threshold. These frequent 1-itemsets are considered as the seeds for generating larger itemsets. 
• Joining Step: In this step, the algorithm joins the frequent (k-1)-itemsets to generate candidate k-itemsets. It combines itemsets that share the first (k-2) items. For example, if we have frequent 2-itemsets {A, B} and {A, C}, the algorithm joins them to create a candidate 3-itemset {A, B, C}. 
• Pruning Step: The pruning step eliminates candidate itemsets that contain infrequent subsets. If any (k-1)-subset of a candidate k-itemset is infrequent, the candidate k-itemset is pruned since it cannot be a frequent itemset. 
• Scanning the Dataset: After generating candidate item sets, the algorithm scans the dataset again to calculate each item set’s support.
• Repeat: Steps 3 to 5 are repeated iteratively until no more frequent itemsets can be generated. The algorithm continues until it reaches this stage. 
• Association Rule Generation: Once all frequent item sets are discovered, association rules are generated. These rules consist of an antecedent (left-hand side) and a consequent (right-hand side). The antecedent represents the items that imply the presence of the resultant in a transaction. 
• Rule Evaluation: Finally, the generated association rules are evaluated based on metrics such as support, confidence, and lift. Support measures the frequency of the rule, confidence indicates the likelihood of the consequent item being purchased given the antecedent, and lift measures the strength of the association between the antecedent and the resultant. 

Steps of Market Basket Analysis in Python 

Understanding the steps of Market Basket Analysis in Python is essential for uncovering hidden patterns in sales data. Analysing purchase behaviour helps businesses optimise product placement, improve marketing strategies, and enhance customer experience. This knowledge enables data-driven decision-making, boosting profitability and operational efficiency. The steps of Market Basket Analysis in Python are as follows: 

• Preparing the Dataset: 

Gathering and preparing the dataset for Market Basket Analysis is crucial before implementing it. The dataset should consist of transactional records, each representing a unique customer purchase and containing a list of items purchased.

First, we must import Python libraries, such as Pandas and NumPy, for data manipulation and analysis. We can then load the dataset into a Pandas DataFrame and explore its structure using various functions and methods provided by the library.

• Data Preprocessing and Exploration:

Once the dataset is loaded, it is essential to preprocess and explore the data to ensure its quality and understand its characteristics. This step involves handling missing values, removing duplicates, and transforming the data into the appropriate format for analysis.

• Handling Missing Values:

Missing values can hinder the accuracy and reliability of the analysis. Therefore, it is crucial to handle them appropriately. Depending on the dataset, you can remove rows or fill in missing values with appropriate techniques, such as mean imputation or forward/backward filling.

• Removing Duplicates:

Duplicate records can skew the analysis results, leading to inaccurate associations. Removing duplicates ensures that each transaction is considered only once during the analysis, providing more reliable insights.

• Data Transformation:

Most MBA algorithms require the data to be in a binary format, where each column represents a unique item, and the values indicate whether an item is present in a particular transaction. We can use one-hot or binary encoding techniques to transform the dataset into this format.

• Applying Apriori Algorithm:

The Apriori algorithm is a popular and widely used algorithm for Market Basket Analysis. It helps identify frequent itemsets and generate association rules based on them. The algorithm follows the “Apriori property” principle, which states that any subset of a frequent itemset must also be frequent.

To implement the Apriori algorithm in Python, we can utilise the mlxtend library, which efficiently implements various association rule mining algorithms.

• Installing mlxtend:

Before proceeding, we need to install the mlxtend library using pip:

• Applying Apriori:

Once the library is installed, we can import the necessary functions and apply the Apriori algorithm to our preprocessed dataset.

• Analysing the Results:

After applying the Apriori algorithm, we obtain a set of frequent item sets and corresponding association rules. These rules provide valuable insights into product relationships and can be used to optimise business strategies.

• Interpreting Association Rules:

Association rules consist of an antecedent (left-hand side), a consequent (right-hand side), and various metrics such as support, confidence, and lift. Here’s an example of how an association rule can be interpreted:

• Support: Indicates the frequency of occurrence of both products in transactions.
• Confidence: This represents the conditional probability of purchasing Product B given that Product A has already been purchased.
• Lift: Measures the strength of the association between the antecedent and the consequent. A lift greater than 1 indicates a positive association.
• Filtering and Sorting Rules:

Depending on your business goals, you can filter the generated association rules based on specific metrics. For example, you may focus on rules with high support and confidence values to identify strong associations. Additionally, sorting the rules based on the lift can reveal the most influential associations.

• Visualising the Results: 

Data visualisation plays a crucial role in conveying insights effectively. By visualising the association rules, we can better understand the relationships between products and identify patterns that might not be immediately apparent in tabular form.

Python offers various libraries for data visualisation, such as Matplotlib and Seaborn. These libraries enable us to create bar plots, scatter plots, and network graphs to visualise the association rules and their corresponding metrics.

This structured approach outlines the necessary steps and considerations for conducting Market Basket Analysis using Python, from data preparation to visualisation of results.

Market Basket Analysis Example: 

To understand the steps mentioned above, let’s consider an example: 

ABC retail store sells groceries. It collects transactional data, including information about customer purchases. Each transaction has a unique identifier (e.g., receipt number) and a list of items bought together. 

With Market Basket Analysis, the store aims to understand the association and pattern between different customer products. Based on this data, it will optimise its business strategy. 

• Step 1: Data Collection:

The store gathers transactional data over some time. For example, they have a dataset containing information about individual transactions, such as: 

Transaction : Milk, Bread, Eggs, Cheese

Transaction : Bread, Butter, Eggs

Transaction 3: Milk, Bread, Butter, Yogurt

Transaction 4: Bread, Eggs, Cheese, Yogurt 

• Step 2: Data Preprocessing:

Cleansing of data to ensure precise outcomes. Removal of duplicity or irrelevant information. 

• Step 3: Transaction Encoding:

The transaction data is encoded into a suitable format for analysis. One common approach is binary encoding, where each item is represented as a binary variable (0 or 1) indicating its presence or absence in a transaction.

Transaction 1: 1, 1, 1, 1

Transaction 2: 1, 0, 1, 0

Transaction 3: 1, 1, 0, 1

Transaction 4: 1, 0, 1, 1 

• Step 4: Frequent Itemset Mining:

The store identifies frequent item sets using the Apriori algorithm based on a specified support threshold. For this example, let’s assume a support threshold of 50%. 

Frequent 1-itemsets: 

Bread: 4

Eggs: 3

Milk: 2

Butter: 2

Cheese: 2

Yoghurt: 2 

• Step 5: Association Rule Generation:

From the frequent itemsets, the store generates association rules. These rules consist of an antecedent (left-hand side) and a consequent (right-hand side). For example: 

If customers buy Bread and Eggs, they will also buy Milk. (Bread, Eggs) -> (Milk)

If customers buy Butter and Yogurt, they are likely to buy Bread. (Butter, Yogurt) -> (Bread)

• Step 6: Rule Evaluation:

The generated rules are evaluated based on support, confidence, and lift metrics, which provide insights into the strength and significance of the associations.

For example, the rule (Bread, Eggs) -> (Milk) may have a support of 25%, confidence of 66% (indicating that 66% of transactions containing Bread and Eggs also contain Milk), and a lift value greater than 1, indicating a positive association. 

• Step 7: Interpretation and Action:

The store analyses the generated rules to gain insights and take appropriate action. For example, it may strategically place Milk near the Bread and Eggs sections to encourage additional purchases. It might also create promotions or discounts targeting customers who purchase Butter and Yogurt together to increase Bread sales. 

The retail store can optimise product placement by utilising market basket analysis, create targeted marketing campaigns, and enhance customer satisfaction, increasing sales and profitability. 

Benefits of Market Basket Analysis:

Now, you will read about the benefits of Market Basket Analysis. This strategic insight helps businesses tailor marketing strategies and improve overall operational efficiency. Market Basket Analysis offers several benefits for businesses. Here are some critical advantages of utilising Market Basket Analysis: 

• Identifying Product Associations: 

It helps businesses uncover relationships and associations among products frequently purchased together. By identifying these patterns, companies can optimise product placement and cross-selling strategies. For example, suppose customers often buy cereal and milk together. In that case, supermarkets can position these items near each other to encourage additional purchases. 

• Personalised Recommendations: 

Today, consumers are too picky when buying a product, so product customisation and personalisation have become even more significant. With techniques like Market Basket Analysis, retailers can provide personalised recommendations to customers based on their purchase history and the buying behaviour of similar customers. 

By suggesting related or complementary products, businesses can enhance the customer experience, increase customer satisfaction, and drive additional sales. 

• Optimising Inventory Management: 

Understanding product associations helps businesses optimise inventory management. By identifying frequently co-purchased items, companies can ensure sufficient stock levels of related products. This helps avoid stockouts, improves supply chain efficiency, and reduces holding costs for slow-moving items. 

• Effective Pricing Strategies: 

Market Basket Analysis can inform businesses about price bundling opportunities. By identifying items that are frequently purchased together, companies can create attractive bundle offers or pricing incentives to encourage customers to buy the bundled products. This strategy can increase sales and enhance customer perception of value. 

• Targeted Marketing Campaigns: 

Insights derived from Market Basket Analysis enable businesses to design targeted marketing campaigns. Companies can create customised promotions, coupons, or discounts tailored to specific customer segments by understanding which products are commonly bought together. This targeted approach enhances the effectiveness of marketing efforts and improves customer engagement.

• Improving Store Layout and Merchandising:

With this technique, retailers can guide businesses in optimising store layout and merchandising. By placing frequently co-purchased items nearby, companies can enhance customer convenience and encourage impulse purchases. Strategic placement based on product associations can improve the overall shopping experience and drive sales.

• Data-Driven Decision Making:

By leveraging Market Basket Analysis, businesses can make data-driven decisions backed by quantitative insights. Instead of relying solely on intuition or guesswork, companies can base their strategies on actual purchase data and patterns. This enhances decision-making accuracy and increases the likelihood of achieving desired business outcomes. 

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Frequently Asked Questions

What is Market Basket Analysis in Python?

 Market Basket Analysis in Python uncovers product relationships from customer purchase patterns using data mining techniques. Libraries like `mlxtend` and algorithms like Apriori help analyse transactional data to improve sales strategies.

How does the Apriori algorithm work for Market Basket Analysis?

The Apriori algorithm identifies frequent item sets in a dataset. It calculates support, generates candidate itemsets, prunes infrequent subsets, and iterates until no more frequent itemsets exist. It then generates and evaluates association rules.

Why is Market Basket Analysis critical for retailers?

Market Basket Analysis helps retailers optimise product placement, enhance cross-selling, personalise recommendations, manage inventory, and design targeted marketing campaigns, ultimately boosting sales and customer satisfaction.

Conclusion

Market Basket Analysis in Python is vital for retailers to understand customer purchase behaviour. By leveraging techniques like the Apriori algorithm, businesses can uncover hidden patterns in transaction data, optimise product placement, personalise recommendations, and create targeted marketing campaigns. 

Implementing Market Basket Analysis leads to data-driven decisions that enhance sales, improve customer experience, and drive revenue growth, making it indispensable in today’s competitive market.