A Prototype for profit maximization using Apriori data mining algorithm: case of Kula Kona restaurant
Date
2021
Authors
Okeyo, Seth Ouma
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Most businesses use different promotional and pricing methods to improve profits, revenues, and sales volumes. For example, a restaurant manager may change prices to encourage sales of food items. Also, he or she may in a special way advertise or present advertise the items to increase customers’ awareness and demand. This has become cumbersome and has made the management of such businesses difficult and has informed the decisions to develop systems with aims of coming up with a solution to this, most of the systems are complicated in nature and difficult for the users to apply, most of them are also rigid to platforms/system requirements since they were developed in old and probably un-scalable platforms. The aim of this research is to formulate a prototype for profit maximization using Apriori data mining algorithm. This is achieved by applying the algorithm on existing sales knowledge bases with other given parameters, some kept constant and others varying, and the algorithm is able to determine the sales patterns using different internal and external parameters. The prototype then automatically analyzes the patterns and come up with reports and summaries which can aid in decision making and consequently profit maximization with the optimal prices of the goods, which is advantageous to both the restaurant owners and clients. The research site is Kula Kona restaurant located in the Hurlingham area within Nairobi. The research design is used to conduct the scientific study and descriptive approach to demonstrate the effects of adjustments of different variables which help understand the behavior and effects on other variables in relation to sales at a given time. The Data-driven modelling methodology was used in this model development. The methodology was ideal since it relied on retrospective data and it performed at the accuracy level of 93.71% and a mean square error of 0.039. The results were great and showed that Apriori algorithm is the best fit for this type of machine learning prototype.
Description
A Thesis Submitted to the School of Computing and Engineering Sciences in Partial Fulfilment for the Requirement of the Degree of Master of Science in Information Technology of Strathmore University
Keywords
Artificial intelligence, Apriori algorithm, Data mining, Development platforms, Knowledge base