Extensive Data Exploration for Automatic Price Suggestion Using Item Description: Case Study for the Kaggle Mercari Challenge

Ait Si Ali, Amine, Seker, Huseyin, Farnie, Steven and Elliott, John (2018) Extensive Data Exploration for Automatic Price Suggestion Using Item Description: Case Study for the Kaggle Mercari Challenge. In: Proceedings of the 2nd International Conference on Advances in Artificial Intelligence - ICAAI 2018. IEEE, pp. 41-45. ISBN 978-1-4503-6583-3

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Abstract

This paper is related to a Kaggle competition. The competition is organised by Mercari and it is about building a model that can automatically and accurately suggests a selling price for a given item based on the information that the seller is providing. The provided information could be the description of the item, the category, the brand name, the item condition or the delivery option as well as other things. This is a regression problem and Natural Language Processing (NLP) techniques are used as well. An extensive data exploration is performed to help solving the problem. Logarithmic transformation is applied to skewed data and categorical features are combined with numerical ones. The developed modal produced promising results.

Item Type:	Book Section
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	10 Jun 2019 13:45
Last Modified:	10 Oct 2019 18:17
URI:	http://nrl.northumbria.ac.uk/id/eprint/39610

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