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 ...

BackgroundPost-stroke hemiparesis strongly affects stroke patients’ activities of daily living and health-related quality of life. Scalp acupuncture (SA) is reportedly beneficial for post-stroke hemiparesis. However, there is still no standard of SA for the treatment of post-stroke hemiparesis. Apriori algorithm-based association rule analysis is a kind of “if-then” rule-based machine learning method suitable for investigating the underlying rules of acupuncture point/location selections. This study aimed to investigate the core SA combinations for the treatment of post-stroke hemiparesis by using a systematic review and Apriori algorithm-based association rule analysis.MethodsWe conducted a systematic review to include relevant randomized controlled trial (RCT) studies investigating the effects of SA treatment in treating patients with post-stroke hemiparesis, assessed by the Fugl-Meyer Assessment (FMA) score. We excluded studies using herbal medicine or manual acupuncture.ResultsWe extracted 33 SA locations from the 35 included RCT studies. The following SA styles were noted: International Standard Scalp Acupuncture (ISSA), WHO Standard Acupuncture Point Locations (SAPL), Zhu’s style SA, Jiao’s style SA, and Lin’s style SA. Sixty-one association rules were investigated based on the integrated SA location data.ConclusionsSAPL_GV20 (Baihui), SAPL_GV24 (Shenting), ISSA_MS6_i (ISSA Anterior Oblique Line of Vertex-Temporal, lesion-ipsilateral), ISSA_MS7_i (ISSA Posterior Oblique Line of Vertex-Temporal, lesion-ipsilateral), ISSA_PR (ISSA Parietal region, comprised of ISSA_MS5, ISSA_MS6, ISSA_MS7, ISSA_MS8, and ISSA_MS9), and SAPL_Ex.HN3 (Yintang) can be considered the core SA location combination for the treatment of post-stroke hemiparesis. We recommend a core SA combination for further animal studies, clinical trials, and treatment strategies.

Accuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.

This dataset contains transactional data collected for market basket analysis. Each row represents a single transaction with items purchased together. It is ideal for implementing association rule mining techniques such as Apriori, FP-Growth, and other machine learning algorithms.

Key Features:

• Transactions: Lists of items purchased together in a single transaction.
• Applications: Perfect for studying customer purchase patterns, building recommendation systems, and identifying frequent item sets.
• Usage: Use this dataset to practice generating actionable insights for retailers and e-commerce platforms.

Format:

• Rows: Each row represents a transaction.
• Columns: Each column corresponds to an item in the transaction.

Examples of Potential Use Cases:

• Find combinations of items frequently purchased together.
• Predict the likelihood of items being bought together.
• Build AI-powered marketing strategies based on association rules.

Credits:

• This dataset is formatted for educational and research purposes. Feel free to use it to explore and enhance your skills in data mining and machine learning!

Socio-demographic characteristics among adolescent girls in Ethiopia, 2016 EDHS.