Market Basket Analysis
of SAP HANA table (invoices) using R script .
Objective:
If you have SAP HANA
data base which stores all the enterprise transaction data and want
to apply predictive/machine learning algorithms on the HANA data base tables or
views using R. This blog gives you the steps to connect SAP HANA data base from
R and retrieve tables/views/procedures data and apply R statistical
algorithms or machine learning techniques to get the insights of data.
Back
Ground and use case:
R is a data science
scripting language for teams that unites data prep, machine learning, text
mining and predictive model deployment. It is used for business and commercial applications as well as for
research, education, training, rapid prototyping, and application development
and supports all steps of the machine learning process including data
preparation, results visualization, model validation and optimization.
SAP HANA is
an in-memory, column-oriented, relational database management
system developed and marketed by SAP SE. Its primary function as
a database server is to store and retrieve data as requested by the
applications.
With the SAP HANA Data
Platform of structured and unstructured data I wanted to do the proof of
concept on Text analysis, Market basket analysis, and Customer segmentation
data science use cases using R.
The below step by step
procedure helps to connect HANA DB tables/views/procedures from R.
DS Examples using R This
article gives you step by step procedure to connect to SAP HANA DB and do the
analytics
1.Install HANA Client
tools, R studio
2.Request for READ
access to HANA Data Base
3.Setup the Data source
using HDBODBC driver (with the SAP HANA DB Server details)
4.Download the RODBC
package in your R Studio
5.Establish the database
connection using R commands
6.Execute SQL Query
function using SQL commands on R.
7.Feed the data to
the required algorithms to analyze the data/results
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library('RODBC')
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library(arules)
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library("arulesViz")
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library(Matrix)
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library(xlsx)
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library("plotrix")
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library("RColorBrewer")
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ch<-odbcconnect o:p="" pwd="password" uid="REDDYJ">
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res
<-sqlquery ch="" from="" invoice="" o:p="" orders="" product="" reddyj.cbc_mba="">
summary(res)
list(res)
This example shows how
to fetch product wise Invoice to explore the Market Basket analysis
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. Market Basket
Analysis (MBA) which uses Association Rule Mining on the given
transaction data.
An example of
Association Rules Assume there are 100 customers�
- 10 of them bought milk, 8
bought butter and 6 bought both of them *Who 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
Use
cases / Benefits: Store layout Marketing , promotions Website content placement
Recommended engine Cross selling
You
can find code here
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