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 ...

Number of association rules generated using the Apriori rule mining approach on the HMP (full) dataset at various values of support count and confidence thresholds. Table also depicts variations in number of rules due to adoption of various strategies that define the minimum abundance threshold for individual taxa to be considered for rule mining.

Number of association rules generated using the Apriori rule mining approach on the prebiotics dataset at various values of support count and confidence thresholds. Table also depicts variations in number of rules due to adoption of various strategies that define the minimum abundance threshold for individual taxa to be considered for rule mining.

ObjectiveTraditional Chinese medicine (TCM) has been used for the treatment of chronic liver diseases for a long time, with proven safety and efficacy in clinical settings. Previous studies suggest that the therapeutic mechanism of TCM for hepatitis B cirrhosis may involve the gut microbiota. Nevertheless, the causal relationship between the gut microbiota, which is closely linked to TCM, and cirrhosis remains unknown. This study aims to utilize two-sample Mendelian randomization (MR) to investigate the potential causal relationship between gut microbes and cirrhosis, as well as to elucidate the synergistic mechanisms between botanical drugs and microbiota in treating cirrhosis.MethodsEight databases were systematically searched through May 2022 to identify clinical studies on TCM for hepatitis B cirrhosis. We analyzed the frequency, properties, flavors, and meridians of Chinese medicinals based on TCM theories and utilized the Apriori algorithm to identify the core botanical drugs for cirrhosis treatment. Cross-database comparison elucidated gut microbes sharing therapeutic targets with these core botanical drugs. MR analysis assessed consistency between gut microbiota causally implicated in cirrhosis and microbiota sharing therapeutic targets with key botanicals.ResultsOur findings revealed differences between the Chinese medicinals used for compensated and decompensated cirrhosis, with distinct frequency, dosage, properties, flavors, and meridian based on TCM theory. Angelicae Sinensis Radix, Salviae Miltiorrhizae Radix Et Rhizoma, Poria, Paeoniae Radix Alba, Astragali Radix, Atrctylodis Macrocephalae Rhizoma were the main botanicals. Botanical drugs and gut microbiota target MAPK1, VEGFA, STAT3, AKT1, RELA, JUN, and ESR1 in the treatment of hepatitis B cirrhosis, and their combined use has shown promise for cirrhosis treatment. MR analysis demonstrated a positive correlation between increased ClostridialesvadinBB60 and Ruminococcustorques abundance and heightened cirrhosis risk. In contrast, Eubacteriumruminantium, Lachnospiraceae, Eubacteriumnodatum, RuminococcaceaeNK4A214, Veillonella, and RuminococcaceaeUCG002 associated with reduced cirrhosis risk. Notably, Lachnospiraceae shares key therapeutic targets with core botanicals, which can treat cirrhosis at a causal level.ConclusionWe identified 6 core botanical drugs for managing compensated and decompensated hepatitis B cirrhosis, despite slight prescription differences. The core botanical drugs affected cirrhosis through multiple targets and pathways. The shared biological effects between botanicals and protective gut microbiota offer a potential explanation for the therapeutic benefits of these key herbal components in treating cirrhosis. Elucidating these mechanisms provides crucial insights to inform new drug development and optimize clinical therapy for hepatitis B cirrhosis.