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Comparison of Machine Learning Algorithms in Predicting the Age Distribution Parameters of H&M Product Customers

Ismayil, Leyla LU (2022) DABN01 20221
Department of Statistics
Department of Economics
Abstract
Over the past decade, the fashion industry has shifted towards dynamic product assortments with shorter life cycles. As a result, the role of the analysis of product sales has increased and become crucial for fashion retailers. Using H&M Group’s dataset on their product sales, I analyze and compare the performance of two machine learning algorithms in predicting the standard deviation and average age of product customers. These algorithms are Random Forest and Artificial Neural Networks. Since both dependent variables are estimated with noise, I fitted the models to the dataset with products having only a high number of observations. The paper describes in detail the performance of each of the algorithms and compares the accuracy. Random... (More)
Over the past decade, the fashion industry has shifted towards dynamic product assortments with shorter life cycles. As a result, the role of the analysis of product sales has increased and become crucial for fashion retailers. Using H&M Group’s dataset on their product sales, I analyze and compare the performance of two machine learning algorithms in predicting the standard deviation and average age of product customers. These algorithms are Random Forest and Artificial Neural Networks. Since both dependent variables are estimated with noise, I fitted the models to the dataset with products having only a high number of observations. The paper describes in detail the performance of each of the algorithms and compares the accuracy. Random Forest works better in predicting the standard deviation of the age of customers per product, while ANN shows slightly better performance in predicting the average age of product customers. Both models perform better on a restricted sample and the performance of the models increases significantly while predicting the standard deviation of age. (Less)
Please use this url to cite or link to this publication:
author
Ismayil, Leyla LU
supervisor
organization
course
DABN01 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
Fashion, Age Distribution, Machine Learning, Random Forest, Artificial Neural Networks
language
English
id
9083685
date added to LUP
2022-06-08 12:50:53
date last changed
2022-10-10 16:01:01
@misc{9083685,
  abstract     = {{Over the past decade, the fashion industry has shifted towards dynamic product assortments with shorter life cycles. As a result, the role of the analysis of product sales has increased and become crucial for fashion retailers. Using H&M Group’s dataset on their product sales, I analyze and compare the performance of two machine learning algorithms in predicting the standard deviation and average age of product customers. These algorithms are Random Forest and Artificial Neural Networks. Since both dependent variables are estimated with noise, I fitted the models to the dataset with products having only a high number of observations. The paper describes in detail the performance of each of the algorithms and compares the accuracy. Random Forest works better in predicting the standard deviation of the age of customers per product, while ANN shows slightly better performance in predicting the average age of product customers. Both models perform better on a restricted sample and the performance of the models increases significantly while predicting the standard deviation of age.}},
  author       = {{Ismayil, Leyla}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Comparison of Machine Learning Algorithms in Predicting the Age Distribution Parameters of H&M Product Customers}},
  year         = {{2022}},
}