Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Background: As time evolved, traditional Chinese medicine (TCM) became integrated into the global medical system as complementary treatments. Some essential TCM herbs started to play a limited role in clinical practices because of Western medication development. For example, Fuzi (Aconiti Lateralis Radix Praeparata) is a toxic but indispensable TCM herb. Fuzi was mainly used in poor circulation and life-threatening conditions by history records. However, with various Western medication options for treating critical conditions currently, how is Fuzi used clinically and its indications in modern TCM are unclear. This study aimed to evaluate Fuzi and Fuzi-based formulas in modern clinical practices using artificial intelligence and data mining methods.Methods: This nationwide descriptive study with market basket analysis used a cohort selected from the Taiwan National Health Insurance database that contained one million national representatives between 2003 and 2010 used for our analysis. Descriptive statistics were performed to demonstrate the modern clinical indications of Fuzi. Market basket analysis was calculated by the Apriori algorithm to discover the association rules between Fuzi and other TCM herbs.Results: A total of 104,281 patients using 405,837 prescriptions of Fuzi and Fuzi-based formulas were identified. TCM doctors were found to use Fuzi in pulmonary (21.5%), gastrointestinal (17.3%), and rheumatologic (11.0%) diseases, but not commonly in cardiovascular diseases (7.4%). Long-term users of Fuzi and Fuzi-based formulas often had the following comorbidities diagnosed by Western doctors: osteoarthritis (31.0%), peptic ulcers (29.5%), hypertension (19.9%), and COPD (19.7%). Patients also used concurrent medications such as H2-receptor antagonists, nonsteroidal anti-inflammatory drugs, β-blockers, calcium channel blockers, and aspirin. Through market basket analysis, for the first time, we noticed many practical Fuzi-related herbal pairs such as Fuzi–Hsihsin (Asari Radix et Rhizoma)–Dahuang (Rhei Radix et Rhizoma) for neurologic diseases and headache.Conclusion: For the first time, big data analysis was applied to uncover the modern clinical indications of Fuzi in addition to traditional use. We provided necessary evidence on the scientific use of Fuzi in current TCM practices, and the Fuzi-related herbal pairs discovered in this study are helpful to the development of new botanical drugs.

Analysis of ‘Groceries dataset ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/heeraldedhia/groceries-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Association Rule Mining

Market Basket Analysis is one of the key techniques used by large retailers to uncover associations between items. It works by looking for combinations of items that occur together frequently in transactions. To put it another way, it allows retailers to identify relationships between the items that people buy.

Association Rules are widely used to analyze retail basket or transaction data and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.

Details of the dataset

The dataset has 38765 rows of the purchase orders of people from the grocery stores. These orders can be analysed and association rules can be generated using Market Basket Analysis by algorithms like Apriori Algorithm.

Apriori Algorithm

Apriori is an algorithm for frequent itemset mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent itemsets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis.

An example of Association Rules

Assume there are 100 customers 10 of them bought milk, 8 bought butter and 6 bought both of them. bought milk => bought butter support = P(Milk & Butter) = 6/100 = 0.06 confidence = support/P(Butter) = 0.06/0.08 = 0.75 lift = confidence/P(Milk) = 0.75/0.10 = 7.5

Note: this example is extremely small. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Some important terms:

• Support: This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears.

• Confidence: This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears.

• Lift: This says how likely item Y is purchased when item X is purchased while controlling for how popular item Y is.

--- Original source retains full ownership of the source dataset ---

The study applies Market Basket Analysis and Keymorph Analysis to analyze the articles related to Svalbard published in a sample of Russian mainstream federal and north-western regional media outlets produced between 2010 and 2021. The data for Market Basket Analysis is divided into six target subcorpora: Federal 2010-2013, Regional 2010-2013, Federal 2014-2017, Regional 2014-2017, Federal 2018-2021, and Regional 2018-2021. The data for Keymorph Analysis consists of six target subcorpora: Federal 2010-2013*, Regional 2010-2013*, Federal 2014-2017*, Regional 2014-2017*, Federal 2018-2021*, and Regional 2018-2021*. The data for Keymorph Analysis are the texts containing the keyword 'Spitsbergen' obtained from the data for Market Basket Analysis. Market Basket Analysis is used to retrieve Associative Arrays consisting of various keywords for the keyword meaning 'Spitsbergen'. Keymorph Analysis examines the prominence of the grammatical cases of nouns meaning 'Russia', 'Norway', and 'Spitsbergen'. The dataset includes: 1) the R code for keyword analysis (keywords serve as an input for Market Basket Analysis); 2) lists of keywords obtained from six target subcorpora Federal 2010-2013, Regional 2010-2013, Federal 2014-2017, Regional 2014-2017, Federal 2018-2021, and Regional 2018-2021; 3) the R code for Market Basket Analysis; 4) examples with the nouns meaning 'Russia', 'Norway', and 'Spitsbergen' extracted from six target subcorpora Federal 2010-2013*, Regional 2010-2013*, Federal 2014-2017*, Regional 2014-2017*, Federal 2018-2021*, and Regional 2018-2021* and annotated according to the grammatical cases of these nouns as well as the semantic meanings of the cases; 5) the calculated difference index (DIN*) values for the grammatical cases of the nouns meaning 'Russia', 'Norway', and 'Spitsbergen'. The DIN* was used in Keymorph Analysis as the effect size metric; 6) the R code for creation of the bar chart with DIN* values for the grammatical cases of the nouns meaning 'Russia', 'Norway', and 'Spitsbergen'.