In this chapter, we covered a lot of ground! We started with a discussion about how trends are detected and predicted in the retail vertical. Then we dived into what market basket analysis really means and the core concepts, mathematical formulae underlying the algorithms, and the critical metrics which are used to evaluate the results obtained from the algorithms, notably, support, confidence, and lift. We also discussed the most popular techniques used for analysis, including contingency matrix evaluation, frequent itemset generation, and association rule mining. Next, we talked about how to make data driven decisions using market basket analysis. Finally, we implemented our own algorithms and also used some of the popular libraries in R, such as arules
, to apply these techniques to some real world transactional data for detecting, predicting, and visualizing trends. Do note that these machine learning techniques only talk about product based recommendations purely based on purchase...
Argentina
Australia
Austria
Belgium
Brazil
Bulgaria
Canada
Chile
Colombia
Cyprus
Czechia
Denmark
Ecuador
Egypt
Estonia
Finland
France
Germany
Great Britain
Greece
Hungary
India
Indonesia
Ireland
Italy
Japan
Latvia
Lithuania
Luxembourg
Malaysia
Malta
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Romania
Russia
Singapore
Slovakia
Slovenia
South Africa
South Korea
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
Ukraine
United States