13 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. A

    ‘Groceries dataset ’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Aug 15, 2015
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2015). ‘Groceries dataset ’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-groceries-dataset-b6be/136ba9af/?iid=001-023&v=presentation
    Explore at:
    Dataset updated
    Aug 15, 2015
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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 ---

  3. dataset Financial_Literacy.xlsx

    • figshare.com
    xlsx
    Updated May 13, 2025
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    Maricris Usita; Virgie Liza Luriban (2025). dataset Financial_Literacy.xlsx [Dataset]. http://doi.org/10.6084/m9.figshare.29046950.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    May 13, 2025
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Maricris Usita; Virgie Liza Luriban
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The research focuses on students’ financial knowledge, behaviors, and attitudes, which can then be analyzed using the APRIORI algorithm to identify significant patterns and associations.

  4. A

    ‘Market Basket Analysis Data’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Market Basket Analysis Data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-market-basket-analysis-data-1c3c/915d4e47/?iid=006-454&v=presentation
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Market Basket Analysis Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ahmtcnbs/datasets-for-appiori on 28 January 2022.

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

    Market Basket Analysis Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. We apply an iterative approach or level-wise search where k-frequent itemsets are used to find k+1 itemsets.

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

  5. f

    The hyperparameters of the apriori algorithm.

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
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    Saeyeon Cheon; Thanin Methiyothin; Insung Ahn (2023). The hyperparameters of the apriori algorithm. [Dataset]. http://doi.org/10.1371/journal.pone.0282119.t002
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Saeyeon Cheon; Thanin Methiyothin; Insung Ahn
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The hyperparameters of the apriori algorithm.

  6. f

    Quantitative data in the database for Example 1.

    • figshare.com
    xls
    Updated Jun 1, 2023
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    Xiaohan Liao; Cunjin Xue; Fenzhen Su (2023). Quantitative data in the database for Example 1. [Dataset]. http://doi.org/10.1371/journal.pone.0177438.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Xiaohan Liao; Cunjin Xue; Fenzhen Su
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Quantitative data in the database for Example 1.

  7. f

    Table_2_Delayed Comparison and Apriori Algorithm (DCAA): A Tool for...

    • frontiersin.figshare.com
    docx
    Updated Jun 13, 2023
    + more versions
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    Lianhong Ding; Shaoshuai Xie; Shucui Zhang; Hangyu Shen; Huaqiang Zhong; Daoyuan Li; Peng Shi; Lianli Chi; Qunye Zhang (2023). Table_2_Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein–Protein Interactions From Time-Series Phosphoproteomic Data.DOCX [Dataset]. http://doi.org/10.3389/fmolb.2020.606570.s002
    Explore at:
    docxAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    Frontiers
    Authors
    Lianhong Ding; Shaoshuai Xie; Shucui Zhang; Hangyu Shen; Huaqiang Zhong; Daoyuan Li; Peng Shi; Lianli Chi; Qunye Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of high-throughput omics data is one of the most important approaches for obtaining information regarding interactions between proteins/genes. Time-series omics data are a series of omics data points indexed in time order and normally contain more abundant information about the interactions between biological macromolecules than static omics data. In addition, phosphorylation is a key posttranslational modification (PTM) that is indicative of possible protein function changes in cellular processes. Analysis of time-series phosphoproteomic data should provide more meaningful information about protein interactions. However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. Moreover, most reported tools ignore the lag between functional alterations and the corresponding changes in protein synthesis/PTM and are highly dependent on previous knowledge, resulting in high false-positive rates and difficulties in finding newly discovered protein–protein interactions (PPIs). Therefore, in the present study, we developed a new method to discover protein–protein interactions with the delayed comparison and Apriori algorithm (DCAA) to address the aforementioned problems. DCAA is based on the idea that there is a lag between functional alterations and the corresponding changes in protein synthesis/PTM. The Apriori algorithm was used to mine association rules from the relationships between items in a dataset and find PPIs based on time-series phosphoproteomic data. The advantage of DCAA is that it does not rely on previous knowledge and the PPI database. The analysis of actual time-series phosphoproteomic data showed that more than 68% of the protein interactions/regulatory relationships predicted by DCAA were accurate. As an analytical tool for PPIs that does not rely on a priori knowledge, DCAA should be useful to predict PPIs from time-series omics data, and this approach is not limited to phosphoproteomic data.

  8. P

    ROSTD Dataset

    • paperswithcode.com
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    Varun Gangal; Abhinav Arora; Arash Einolghozati; Sonal Gupta, ROSTD Dataset [Dataset]. https://paperswithcode.com/dataset/rostd
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    Authors
    Varun Gangal; Abhinav Arora; Arash Einolghozati; Sonal Gupta
    Description

    A dataset of 4K out-of-domain (OOD) examples for the publicly available dataset from (Schuster et al. 2019). In contrast to existing settings which synthesize OOD examples by holding out a subset of classes, the examples were authored by annotators with apriori instructions to be out-of-domain with respect to the sentences in an existing dataset.

  9. f

    The executing time (in Millisecond) comparison among Apriori, TDApriori and...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Iyad Aqra; Tutut Herawan; Norjihan Abdul Ghani; Adnan Akhunzada; Akhtar Ali; Ramdan Bin Razali; Manzoor Ilahi; Kim-Kwang Raymond Choo (2023). The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on mushroom dataset. [Dataset]. http://doi.org/10.1371/journal.pone.0179703.t025
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Iyad Aqra; Tutut Herawan; Norjihan Abdul Ghani; Adnan Akhunzada; Akhtar Ali; Ramdan Bin Razali; Manzoor Ilahi; Kim-Kwang Raymond Choo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on mushroom dataset.

  10. f

    The executing time (in Millisecond) comparison among Apriori, TDApriori and...

    • figshare.com
    xls
    Updated May 31, 2023
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    Iyad Aqra; Tutut Herawan; Norjihan Abdul Ghani; Adnan Akhunzada; Akhtar Ali; Ramdan Bin Razali; Manzoor Ilahi; Kim-Kwang Raymond Choo (2023). The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on chess data. [Dataset]. http://doi.org/10.1371/journal.pone.0179703.t023
    Explore at:
    xlsAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Iyad Aqra; Tutut Herawan; Norjihan Abdul Ghani; Adnan Akhunzada; Akhtar Ali; Ramdan Bin Razali; Manzoor Ilahi; Kim-Kwang Raymond Choo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on chess data.

  11. f

    The executing time (in Millisecond) comparison among Apriori, TDApriori and...

    • figshare.com
    xls
    Updated Jun 4, 2023
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    Iyad Aqra; Tutut Herawan; Norjihan Abdul Ghani; Adnan Akhunzada; Akhtar Ali; Ramdan Bin Razali; Manzoor Ilahi; Kim-Kwang Raymond Choo (2023). The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on apriori data. [Dataset]. http://doi.org/10.1371/journal.pone.0179703.t024
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Iyad Aqra; Tutut Herawan; Norjihan Abdul Ghani; Adnan Akhunzada; Akhtar Ali; Ramdan Bin Razali; Manzoor Ilahi; Kim-Kwang Raymond Choo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The executing time (in Millisecond) comparison among Apriori, TDApriori and ITDApriori on apriori data.

  12. f

    An overview of the avocado content (grams avocado per 100 g food) at food...

    • figshare.com
    xls
    Updated Jun 6, 2023
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    Vivienne X. Guan; Elizabeth P. Neale; Yasmine C. Probst (2023). An overview of the avocado content (grams avocado per 100 g food) at food group levelsb'*' in which avocado-containing products were found. [Dataset]. http://doi.org/10.1371/journal.pone.0279567.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 6, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Vivienne X. Guan; Elizabeth P. Neale; Yasmine C. Probst
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    An overview of the avocado content (grams avocado per 100 g food) at food group levelsb'*' in which avocado-containing products were found.

  13. f

    Food items containing avocados at meal occasions.

    • plos.figshare.com
    xls
    Updated Jun 7, 2023
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    Vivienne X. Guan; Elizabeth P. Neale; Yasmine C. Probst (2023). Food items containing avocados at meal occasions. [Dataset]. http://doi.org/10.1371/journal.pone.0279567.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 7, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Vivienne X. Guan; Elizabeth P. Neale; Yasmine C. Probst
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Food items containing avocados at meal occasions.

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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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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