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Full Project – Automated market basket analysis system

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CHAPTER ONE

1.0 INTRODUCTION

Market Basket Analysis (MBA) was coined from a supermarket scene where a customer enters and picks a basket to start shopping. The goal of a MBA system is to predict the customers buying pattern and possibly the goods in the superstore within the categories that the customer will like to pick and useful information to support retail marketing decisions (Chena, 2005).

Market Basket Analysis ultimately results in a better understanding of the customers and their purchasing behavior, allowing retailers to explore associations, predict the likelihood of a customer response based on associations to maximize profit for the retailers by providing better services to the consumers and ultimately optimizing marketing and sales operations for results.MBA in the end effects a better understanding of your clients and their buying behavior, allowing you to explore institutions, predict the probability of a purchaser reaction based on associations to maximize earnings for the shops by means of supplying better offerings to purchasers and ultimately optimize your marketing and income operations for better profit making. (Trupti & Santosh, 2014).

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.

Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, ”Which clients are most likely to respond to my next promotional mailing, and why?”

Data mining (DM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, etc.. Data mining is a complex topic and has links with multiple core fields such as computer science and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning and pattern recognition.

Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:

Massive data collection.
Powerful multiprocessor computers.
Data mining algorithms.

Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19percent of respondents are beyond the 50 gigabyte level, while 59per-cent expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost effective manner with parallel multiprocessor computer technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods.

With the explosive growth of information sources available on the World Wide Web, it has become increasingly necessary for users to utilize automated tools in find the desired information resources, and to track and analyze their usage patterns. These factors give rise to the necessity of creating server side and client side intelligent systems that can effectively mine for knowledge. Web mining can be broadly defined as the discovery and analysis of useful information from the World Wide Web.

This study focuses on building an automated system that will predict customers buying pattern. Also, the system should be able to make recommendations the next time the customer comes online to make purchases

1.2 Statement of the Problem

Through in depth research and observations carried on supermarket we have discovered that retailers are willing to know what product is purchased with the other or if particular products are purchased together as a group of items. Which can help in their decision making with respect to placement of product, determining the timing and extent of promotions on product and also have a better understanding of customer purchasing habits by grouping customers with their transactions.
This project is aimed at designing and implementing a well-structured market basket analysis software tool to solve the problem stated above.

1.3 Objectives of the Study

The aim of the study is to maximize profit for the retailers by providing better services to the consumers. The following are other objectives of this study:

Cross-Market Analysis – Data Mining performs Association/correlations between product sales.
Identifying Customer Requirements – helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers.
Customer Profiling – helps to determine what kind of people buy what kind of products.

1.4 Significance of the Study

This study serves as a contribution towards improving customer purchases. It would also help the management understand their clients and their buying behavior, give them the opportunity to explore options, predict the probability of a purchaser reaction based on associations to maximize earnings for the shops by means of supplying better offerings to purchasers.

1.5 Scope of the Study

This scope of the study focuses on (—–case study here–) supermarket and the scope of this project includes:

We aim to develop our very own market basket analysis software, which will be used in ——- supermarket
The software will exhibit a colorful GUI (graphical user interface).
The software will be based on Apriori.
We intend to conduct a research into the various branches of science that this software will be based on, such as artificial intelligence.
Develop software that will eventually stand out among other data mining software.

1.6 Limitation of the Study

The limitations of this software will include:

- Data restrictions: this is a major factor that stands in the way of the execution of this project. Since there is no data on households and individual consumers, it will neglect such purchases.
- Time constraints: this is also a major factor due to the fact that it can’t work on a small amount of raw data because it tends to mislead the retailer in a nut shell this software will work on large volumes of data.

1.7 Definition of terms

Apriori: is an algorithm for frequent item set mining and association rule learning over transactional databases.
Online: An online is a condition of connected to a network of computers of other devices. The term is frequently used to describe someone who is currently connected to the internet.
Internet: This is a global connection of computer network co-operating with each other to exchange data using common software standard protocols
Data Mining: is a process of identifying patterns and establishing relationships. These are raw fact that to proposed into meaningful information
Shopping: It is the processing of browsing and purchasing items online

Users: An agent either human agent, or software agent who uses a computer or network services
Security: Its objective is to establish rules and measures to against attacks over the internet.
Shopping basket/cart: A chart supplied by a shop, especially supermarket, to use by customers inside the shop for transport of merchandise to the check- out counter during shopping.

Accessibility:

A general term used to describe the degree to which a product, device, or environment is accessible by as many people as possible.

Database: A systematized collection of data that can be access immediately and manipulated by a data processing system for a specific purpose

E-commerce: Electronic commerce refers to the process of marketing, buying and selling of product and services online.
Market Basket Analysis: a system to predict the customers buying pattern

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