Towards Accurate Predictions of Customer Purchasing Patterns
| Autor: | Rafael Valero-Fernandez, David J. Collins, K.P. Lam, Colin Rigby, James Bailey |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Decision tree 02 engineering and technology Machine learning computer.software_genre Logistic regression symbols.namesake Lasso (statistics) 020204 information systems Linear regression 0202 electrical engineering electronic engineering information engineering Database marketing Gaussian process Artificial neural network business.industry Quadratic classifier Perceptron Random forest Support vector machine ComputingMethodologies_PATTERNRECOGNITION symbols 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer |
| Zdroj: | CIT |
| DOI: | 10.1109/cit.2017.58 |
| Popis: | A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%. |
| Databáze: | OpenAIRE |
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