Customer Behaviour Prediction Using Recommender Systems

Onyeka, Ifeoma Delphine (2022) Customer Behaviour Prediction Using Recommender Systems. Masters thesis, Dublin, National College of Ireland.

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Abstract

It is possible to improve business decisions by gaining insight into consumer behavior through predictive research.The goal is to predict customer purchasing habits and recommend items based on their behavioral data. It is possible to improve business decisions by gaining insight into consumer behavior through predictive research. This study compares the statistical approach to data mining in predicting customer behavior with Logistic regression, Random Forest, Support Vector Machine and K-Nearest Neighbour. As a result, Logistic regression achieved 80% accuracy, An accuracy of 82% was gotten from Random Forest while K-Nearest Neighbour gave 81% and finally For Support Vector Machine, 81% accuracy was obtained. Random Forest gave a higher accuracy.

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Marketing > Consumer Behaviour
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	27 Feb 2023 15:52
Last Modified:	27 Feb 2023 15:52
URI:	https://norma.ncirl.ie/id/eprint/6245

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