This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

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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.

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...

Dataset

This dataset was created by sirishav0919

Released under Database: Open Database, Contents: Database Contents

Contents

• 3M+ Contact Profiles • 5M+ Worldwide eCommerce Brands • Direct Contact Info for Decision Makers • Contact Direct Email and Mobile Number • 15+ eCommerce Platforms • 20+ Data Points • Lifetime Support Until You 100% Satisfied

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NoSQL Software Market Outlook

The global NoSQL software market size was valued at approximately USD 6 billion in 2023 and is projected to reach around USD 20 billion by 2032, growing at a compound annual growth rate (CAGR) of 14% during the forecast period. This market is driven by the escalating need for operational efficiency, flexibility, and scalability in database management systems, particularly in enterprises dealing with vast amounts of unstructured data.

One of the primary growth factors propelling the NoSQL software market is the exponential increase in data volumes generated by various digital platforms, IoT devices, and social media. Traditional relational databases often struggle to handle this surge efficiently, prompting organizations to shift towards NoSQL databases that offer more flexibility and scalability. The ability to store and process large sets of unstructured data without needing a predefined schema makes NoSQL databases an attractive choice for modern businesses seeking agility and speed in data management.

Moreover, the proliferation of cloud computing services has significantly contributed to the growth of the NoSQL software market. Cloud-based NoSQL databases provide cost-effective, scalable, and easily accessible solutions for enterprises of all sizes. The pay-as-you-go pricing model and the capacity to scale resources based on demand have made NoSQL databases a preferred option for startups and large enterprises alike. The seamless integration of NoSQL databases with cloud infrastructure enhances operational efficiencies and reduces the complexities associated with database management.

Another critical driver is the increasing adoption of NoSQL databases in various industry verticals such as retail, BFSI, IT, and healthcare. These industries require robust data management solutions to handle large volumes of diverse data types. NoSQL databases, with their flexible data models and high performance, cater to these requirements efficiently. In the retail sector, for example, NoSQL databases are used to manage customer data, product catalogs, and transaction histories, enabling more personalized and efficient customer services.

Regionally, North America holds a significant share of the NoSQL software market due to the presence of major technology companies and a mature IT infrastructure. The rapid digital transformation across enterprises in the region, alongside substantial investments in big data analytics and cloud computing, further fuels market growth. Additionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the expanding IT sector, increased adoption of cloud services, and significant investments in digital technologies in countries like China and India.

Graph Databases Software has emerged as a crucial component in the landscape of NoSQL databases, particularly for applications that require understanding complex relationships between data entities. Unlike traditional databases that store data in tables, graph databases use nodes, edges, and properties to represent and store data, making them ideal for scenarios where relationships are as important as the data itself. This approach is particularly beneficial in fields such as social networking, where the ability to analyze connections between users can provide deep insights into social dynamics and influence patterns. As businesses increasingly seek to leverage data for competitive advantage, the demand for graph databases is expected to grow, driven by their ability to efficiently model and query interconnected data.

Type Analysis

The NoSQL software market is segmented into various types, including Document-Oriented, Key-Value Store, Column-Oriented, and Graph-Based databases. Document-oriented databases, such as MongoDB, store data in JSON-like documents, offering flexibility in data modeling and ease of use. These databases are widely used for content management systems, e-commerce applications, and real-time analytics. Their ability to handle semi-structured data and scalability features make them a popular choice among developers and enterprises seeking agile database solutions.

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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 ---

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 ...

Comprehensive dataset of 12 Alcohol retail monopolies in California, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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Comprehensive dataset of 1,714 Retail space rental agencies in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This data includes the name, type, and location of active retail tobacco and vapor product vendors operating in New York State. Active retail tobacco and vapor product vendors include only vendors that were categorized as active (i.e. open to the public) on the date the data was downloaded from a DOH database. The vendor type includes, for example, convenience stores or grocery supermarkets. The location of the vendor includes its street address, city, state, zip code, municipality, and county.

Comprehensive dataset of 24 Retail space rental agencies in Colorado, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 17 Retail space rental agencies in Louisiana, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This data includes the name, type, and location of active retail tobacco and vapor product vendors operating in New York State. Active retail tobacco and vapor product vendors include only vendors that were categorized as active (i.e. open to the public) on the date the data was downloaded from a DOH database. The vendor type includes, for example, convenience stores or grocery supermarkets. The location of the vendor includes its street address, city, state, zip code, municipality, and county.

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This dataset shows the location of retailers that are allowed to sell alcoholic beverages in Ontario. It is based on licences issued by the Alcohol and Gaming Commission of Ontario.

Variables included in the dataset include:

• name of store
• address
• city/town
• postal code
• GPS coordinates (latitude and longitude)
• products the store is authorized to sell (for example, beer, wine, cider)