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 provides simulated retail transaction data, offering valuable insights into customer purchasing behaviour and store operations. It is designed to facilitate market basket analysis, customer segmentation, and a variety of other retail analytics tasks. Each row captures detailed transaction information, including a unique identifier, the date and time of purchase, customer details, a list of purchased products, total items, total cost, payment method, and location details such as city and store type. Furthermore, it includes indicators for discounts and promotions applied, along with a customer category based on background or age group, and the season of purchase. This dataset is entirely synthetic, generated using the Python Faker library, making it a safe and versatile resource for researchers, data scientists, and analysts to develop and test algorithms, models, and analytical tools without using real customer data.

Columns

• Transaction_ID: A unique 10-digit identifier for each individual transaction, ensuring each purchase can be uniquely identified.
• Date: The precise date and time when each transaction occurred, providing a timestamp for every purchase.
• Customer_Name: The name of the customer who completed the purchase, offering a means to identify individual customers.
• Product: A detailed list of all products included in a specific transaction.
• Total_Items: The total quantity of items purchased within a single transaction.
• Total_Cost: The overall financial value of the transaction, denominated in currency.
• Payment_Method: The chosen payment method for the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The geographical location (city) where the transaction took place.
• Store_Type: The classification of the store where the purchase was made, e.g., supermarket, convenience store, department store.
• Discount_Applied: A boolean indicator (True/False) showing whether a discount was applied to the transaction.
• Customer_Category: A categorisation of the customer based on their background or age group.
• Season: The season (e.g., spring, summer, autumn, winter) in which the purchase was made.
• Promotion: The specific type of promotion applied to the transaction, if any (e.g., "None", "BOGO", "Discount on Selected Items").

Distribution

This dataset is typically provided in a CSV file format. It contains approximately 1 million individual transaction records. The data spans a time range from 2020-01-01 to 2024-05-19. There are 329,738 unique customer names and 571,947 unique product entries. Payment methods are distributed with 25% Cash, 25% Debit Card, and 50% Other. Transaction locations include Boston (10%), Dallas (10%), and other cities (80%). Store types are categorised as Supermarket (17%), Pharmacy (17%), and other types (67%). Discounts were applied to approximately 50% of the transactions.

Usage

This dataset is ideally suited for: * Market Basket Analysis: Uncovering associations between products and identifying common buying patterns. * Customer Segmentation: Grouping customers based on their purchasing behaviour to target specific offers. * Pricing Optimisation: Developing strategies to optimise pricing and identify opportunities for discounts and promotions. * Retail Analytics: Analysing overall store performance and emerging customer trends. * Algorithmic Development: Testing and refining machine learning models for retail forecasting or recommendation systems.

Coverage

The dataset's geographic coverage includes transactions from various cities, such as Boston and Dallas, representing a broad, though simulated, global scope. The time range of the transactions extends from 1st January 2020 to 19th May 2024. Demographic insights are provided through the Customer_Category column, which classifies customers based on background or age group, allowing for demographic-based analyses. As a synthetic dataset, specific real-world demographic notes are not applicable.

License

CC0

Who Can Use It

This dataset is beneficial for a wide range of users, including: * Researchers: For academic studies on consumer behaviour and retail economics. * Data Scientists: To develop and validate predictive models, such as recommender systems or churn prediction models. * Analysts: For performing in-depth retail analytics, market basket analysis, and customer segmentation to inform business decisions. * Students: As a practical, realistic dataset for learning and applying data analysis techniques in a retail context.

Dataset Name Suggestions

• Retail Transactions Dataset
• Customer Purchasing Behaviour Data
• Market Basket Analysis Data
• Synthetic Retail Transactions
• E-commerce Transaction Log

Attributes

Original Dat

Snapshot img

Retail Analytics Market Size 2025-2029

The retail analytics market size is forecast to increase by USD 28.47 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing volume and complexity of data generated by retail businesses. This data deluge offers valuable insights for retailers, enabling them to optimize operations, enhance customer experience, and make data-driven decisions. However, this trend also presents challenges. One of the most pressing issues is the increasing adoption of Artificial Intelligence (AI) in the retail sector. While AI brings numerous benefits, such as personalized marketing and improved supply chain management, it also raises privacy and security concerns among customers.
Retailers must address these concerns through transparent data handling practices and robust security measures to maintain customer trust and loyalty. Navigating these challenges requires a strategic approach, with a focus on data security, customer privacy, and effective implementation of AI technologies. Companies that successfully harness the power of retail analytics while addressing these challenges will gain a competitive edge in the market.

What will be the Size of the Retail Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant need for businesses to gain insights from their data and adapt to shifting consumer behaviors. Entities such as text analytics, data quality, price optimization, customer journey mapping, mobile analytics, time series analysis, regression analysis, social media analytics, data mining, historical data analysis, and data cleansing are integral components of this dynamic landscape. Text analytics uncovers hidden patterns and trends in unstructured data, while data quality ensures the accuracy and consistency of information. Price optimization leverages historical data to determine optimal pricing strategies, and customer journey mapping provides insights into the customer experience.

Mobile analytics caters to the growing number of mobile shoppers, and time series analysis identifies trends and patterns over time. Regression analysis uncovers relationships between variables, social media analytics monitors brand sentiment, and data mining uncovers hidden patterns and correlations. Historical data analysis informs strategic decision-making, and data cleansing prepares data for analysis. Customer feedback analysis provides valuable insights into customer satisfaction, and association rule mining uncovers relationships between customer behaviors and purchases. Predictive analytics anticipates future trends, real-time analytics delivers insights in real-time, and market basket analysis uncovers relationships between products. Data security safeguards sensitive information, machine learning (ML) and artificial intelligence (AI) enhance data analysis capabilities, and cloud-based analytics offers flexibility and scalability.

Business intelligence (BI) and open-source analytics provide comprehensive data analysis solutions, while inventory management and supply chain optimization streamline operations. Data governance ensures data is used ethically and effectively, and loyalty programs and A/B testing optimize customer engagement and retention. Seasonality analysis accounts for seasonal trends, and trend analysis identifies emerging trends. Data integration connects disparate data sources, and clickstream analysis tracks user behavior on websites. In the ever-changing retail landscape, these entities are seamlessly integrated into retail analytics solutions, enabling businesses to stay competitive and adapt to evolving market dynamics.

How is this Retail Analytics Industry segmented?

The retail analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  In-store operation
  Customer management
  Supply chain management
  Marketing and merchandizing
  Others


Component

  Software
  Services


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period. In the realm of retail, the in-store operation segment of the market plays a pivotal role in optimizing brick-and-mortar retail operations. This segment encompasses various data analytics applications with

Using multiple antimicrobials in food animals may incubate genetically-linked multidrug-resistance (MDR) in enteric bacteria, which can contaminate meat at slaughter. The U.S. National Antimicrobial Resistance Monitoring System tested 14,418 chicken-associated Escherichia coli between 2004 and 2012 for resistance to 15 antimicrobials, resulting in >32,000 possible MDR patterns. We analyzed MDR patterns in this dataset with association rule mining, also called market-basket analysis. The association rules were pruned with four quality measures resulting in a

Online Retail E-Commerce Data Hey everyone! 👋

This dataset contains real e-commerce transaction data from 2009 to 2011. It comes from a UK-based online store that sells a variety of products. The data includes details like invoices, product codes, descriptions, prices, and even customer IDs.

What’s Inside? Each row represents a transaction, and the dataset has the following key columns: 🛒 Invoice – Unique order ID 📦 StockCode – Product code 📝 Description – Name of the product 📊 Quantity – Number of units sold ⏳ InvoiceDate – When the purchase happened 💰 Price – Unit price of the product 👤 Customer ID – Unique identifier for each customer 🌍 Country – Where the customer is from

Why is this dataset useful? This dataset is great for exploring: Customer Segmentation (Find high-value customers) Customer Lifetime Value (LTV) Analysis Sales & Revenue Trends Market Basket Analysis (Which products are bought together?) Predicting Churn & Retention Strategies

How Can You Use It? If you're into data science, machine learning, or business analytics, this dataset is perfect for hands-on projects. You can analyze customer behavior, predict sales, or even build recommendation systems.

Hope this dataset helps with your projects! Let me know if you find something interesting.

Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

This is a realistic and structured pizza sales dataset covering the time span from **2024 to 2025. ** Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.

📁 What’s Inside? The dataset contains rich details from a pizza business including:

✅ Order Dates & Times ✅ Pizza Names & Categories (Veg, Non-Veg, Classic, Gourmet, etc.) ✅ Sizes (Small, Medium, Large, XL) ✅ Prices ✅ Order Quantities ✅ Customer Preferences & Trends

It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.

💡** Why Use This Dataset?** This dataset is ideal for:

📈 Sales Analysis & Reporting 🧠 Machine Learning Models (demand forecasting, recommendations) 📅 Time Series Forecasting 📊 Data Visualization Projects 🍽️ Customer Behavior Analysis 🛒 Market Basket Analysis 📦 Inventory Management Simulations

🧠 Perfect For: Data Science Beginners & Learners BI Developers & Dashboard Designers MBA Students (Marketing, Retail, Operations) Hackathons & Case Study Competitions

pizza, sales data, excel dataset, retail analysis, data visualization, business intelligence, forecasting, time series, customer insights, machine learning, pandas, beginner friendly

Background

This dataset contains the compiled mineralogical records of 458 tourmaline-bearing sites in the United States. Environmental parameters associated with each locality such as surface temperature, precipitation, and geothermal gradient have also been recorded. Geological processes were identified for each locality using site and mineral descriptions from mineral databases and they were also interpreted using machine learning algorithms. Market basket analysis algorithms were utilized to identify patterns within the mineralogical data and these patterns were attributed to the geological processes listed in the dataset.

Data Sources

Mineral occurrence data were retrieved from Mindat.org for the tourmaline minerals schorl, dravite, elbaite, uvite, and foitite. Mineral descriptions listed in the Handbook of Mineralogy (Anthony et al., 2004) web database aided in the identification of different geological processes. Geothermal gradient data are interpolated from values listed in geothermal temperature studies conducted by Batir et al. (2013), Kron and Stix (1982), and Nathenson and Guffanti (1987). Elevation data were retrieved from the USGS Elevation Point Query Service and the climatic data from the WorldClim dataset. Köppen-Geiger climatic zones were assigned to each locality using data published by Kottek et al. (2006) and made available at http://koeppen-geiger.vu-wien.ac.at/

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Weis Moneymaker (WMK): A Grocery Gem or a Basket Case?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Snapshot img

Consumer Shopping Cart Market Size 2025-2029

The consumer shopping cart market size is forecast to increase by USD 132.2 million at a CAGR of 2.7% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the expansion of the retail sector worldwide. This trend is particularly evident in emerging economies, where increasing disposable income and urbanization are leading to an increase in retail sales. Another key driver is the emergence of smart shopping carts, which offer advanced features such as self-checkout, product recommendations, and real-time inventory management. These innovations are enhancing the shopping experience for consumers and providing retailers with valuable data to optimize their operations. However, the market is not without challenges. The market is experiencing significant growth, driven by the increasing trend towards e-commerce and the resulting demand for efficient and sustainable solutions. Fluctuations in raw material prices, particularly for metals and plastics, can significantly impact the cost of producing shopping carts. Additionally, consumer preferences are shifting towards more eco-friendly options, creating a strong demand for sustainable materials and recyclable packaging solutions.
Additionally, the increasing popularity of e-commerce and contactless shopping solutions may limit the growth of the traditional shopping cart market. To capitalize on market opportunities and navigate these challenges effectively, companies must stay abreast of industry trends and invest in research and development to offer innovative and cost-effective solutions. By doing so, they can differentiate themselves from competitors and maintain a competitive edge in the evolving retail landscape.

What will be the Size of the Consumer Shopping Cart Market during the forecast period?

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The market encompasses a range of solutions designed to facilitate and enhance the online shopping experience. These offerings include cart management tools, training, hosting, and consulting services. Key trends in this market include a focus on usability, cart flow optimization, checkout experience optimization, and personalized shopping experiences. Cart features such as multi-platform integration, security, and analytics are also crucial. Additionally, cart testing, design, and support are essential for ensuring a seamless customer journey. Key market drivers include the growing demand for plastic-based packaging, particularly in sectors such as food and beverage, pharmaceuticals, and industrial chemicals.
Cart abandonment analysis and reduction techniques are also vital for improving conversion rates. Overall, the market is a dynamic and growing sector, with ongoing innovation in functionality, accessibility, and integration.

How is this Consumer Shopping Cart Industry segmented?

The consumer shopping cart industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Steel carts
  Plastic carts
  Others


Distribution Channel

  Direct sales
  Distributors


Type

  Traditional shopping carts
  Smart shopping carts


Product Type

  Roller basket
  Child cart
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    Japan
    South Korea


  Middle East and Africa



  South America

By Product Insights

The steel carts segment is estimated to witness significant growth during the forecast period. The market is a dynamic and evolving industry, driven by various factors that enhance the online shopping experience. Cart recovery and abandoned cart recovery are crucial elements of conversion optimization, ensuring that businesses maximize sales opportunities. Website optimization, customer service, and user interface design are essential components of the customer journey, which can significantly impact conversion rates. Subscription services, machine learning, and targeted marketing are key trends, leveraging big data to personalize the shopping experience. Inventory management, order fulfillment, and payment processing are essential operational functions, requiring efficient and secure solutions. Mobile commerce, social commerce, voice commerce, and augmented reality are emerging channels, expanding the reach of online shopping. This market is driven by the growing demand for packaged products in various industries, including food and beverage, cosmetics, and e-commerce.

Get a glance at the market report of share of various segments Request Free Sample

The Steel carts segment was valued at USD 464.20 million in 2019 and showed a gradual increase during the forecast period.

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