Extensive data exploration for automatic price suggestion using item description: Case study for the Kaggle Mercari challenge

Amine Ait Si Ali, Huseyin Seker, Steven Farnie, John Elliott

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationICAAI 2018 - 2018 the 2nd International Conference on Advances in Artificial Intelligence
PublisherAssociation for Computing Machinery, Inc
Pages41-45
Number of pages5
ISBN (Electronic)9781450365833
DOIs
Publication statusPublished - 6 Oct 2018
Event2nd International Conference on Advances in Artificial Intelligence, ICAAI 2018 - Barcelona, Spain
Duration: 6 Oct 20188 Oct 2018

Conference

Conference2nd International Conference on Advances in Artificial Intelligence, ICAAI 2018
CountrySpain
CityBarcelona
Period6/10/188/10/18

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