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Market basket analysis in retail

Authors: Reig Grau, Gerard;

Market basket analysis in retail

Abstract

In this Master Thesis memory will be described a full end-to-end data science project performed in CleverData, a successful start-up specialized in machine learning techniques and analytics tools. Over all its capacities, it offers a huge variety of solutions to nowadays business needs from different domains. This project was performed for one of its client, an important retail company from Spain. It consist of analysing the market basket of customers. Thus, the main goal is to find which items are purchased together in their stores. Through the memory, the reader will see how, step by step, the project grows. Since the first step of defining objectives, until the last one of results delivery. Moreover, the reader will see one of the most promising tools used for machine learning as a service nowadays, BigML. At the end of the project, the reader will have a general idea how data science projects are structured, and how machine learning can be used to solve real problems in today’s companies.

En col·laboració amb la Universitat de Barcelona (UB) i la Universitat Rovira i Virgili (URV)

Country
Spain
Keywords

Random Forests, Artificial intelligence, Market Basket Analysis, Intel·ligència artificial, Retail, Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Hierarchical agglomerative clustering, Association rules, unsupervised learning, supervised learning, Computer algorithms, Cluster Validation Indices, stores, association rules, :Informàtica [Àrees temàtiques de la UPC], data knowledge discovery, Algorismes computacionals, G-means, :Informàtica::Intel·ligència artificial [Àrees temàtiques de la UPC], clusters, Mineria de dades, Àrees temàtiques de la UPC::Informàtica, K-means, Data mining

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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