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Application of Data Analytics To Suggest Gifts To Retail Customers Based On Their Emotions

Ghosh, Sayan (2023) Application of Data Analytics To Suggest Gifts To Retail Customers Based On Their Emotions. Masters thesis, Dublin, National College of Ireland.

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Companies and brands are now constantly expanding their product offerings. This can occasionally make it difficult for customers to choose a product from a large product basket. This problem specifically occurs while purchasing gifts. In this study, customers are given suggestions for different gift categories depending on their emotions using machine learning algorithms. The suggested model uses Random Forest, Convolutional Neural Network, and Residual Neural Network (ResNet50) to recognize different facial emotions. The dataset FER2013 is used to calculate the seven different emotions namely happy, sad, angry, surprise, disgust, fear, and neutral. The dataset contains 35,887 grayscale facial images. Once the emotions have been identified, they will be fed into the classification model together with the dataset of gift category data. The Classification model will assign different categories of gifts to individuals based on their emotions. In this paper, three classification models—Logistic Regression, Decision Tree, and Random Forest—are put forth, and their outcomes are compared. The performance of ResNet50 was better than the other two models for recognizing the facial expressions from the image dataset. The accuracy of ResNet50 was 63.43%. For the gift dataset, Random Forest performed better than the other two classification models, with an accuracy of 79.82%.

Item Type: Thesis (Masters)
Muntean, Cristina Hava
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
B Philosophy. Psychology. Religion > Psychology > Emotions
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 14:37
Last Modified: 18 May 2023 14:37

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