2 datasets found
  1. Market Basket Analysis

    • kaggle.com
    Updated Dec 9, 2021
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    Aslan Ahmedov (2021). Market Basket Analysis [Dataset]. https://www.kaggle.com/datasets/aslanahmedov/market-basket-analysis
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 9, 2021
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aslan Ahmedov
    Description

    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.

    imagehttps://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

    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.

    imagehttps://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

    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.

    imagehttps://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> imagehttps://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

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

    imagehttps://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

    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 ...

  2. Enhanced Pizza Sales Data (2024–2025)

    • kaggle.com
    Updated May 12, 2025
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    akshay gaikwad (2025). Enhanced Pizza Sales Data (2024–2025) [Dataset]. https://www.kaggle.com/datasets/akshaygaikwad448/pizza-delivery-data-with-enhanced-features
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 12, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    akshay gaikwad
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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.

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

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

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

    πŸ’‘** Why Use This Dataset?** This dataset is ideal for:

    πŸ“ˆ Sales Analysis & Reporting 🧠 Machine Learning Models (demand forecasting, recommendations) πŸ“… Time Series Forecasting πŸ“Š Data Visualization Projects 🍽️ Customer Behavior Analysis πŸ›’ Market Basket Analysis πŸ“¦ 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

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Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Aslan Ahmedov (2021). Market Basket Analysis [Dataset]. https://www.kaggle.com/datasets/aslanahmedov/market-basket-analysis
Organization logo

Market Basket Analysis

Analyzing Consumer Behaviour Using MBA Association Rule Mining

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Dec 9, 2021
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Aslan Ahmedov
Description

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.

imagehttps://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

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.

imagehttps://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

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.

imagehttps://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> imagehttps://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

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

imagehttps://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

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 ...

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