Improving KNN Model for Direct Marketing Prediction in Smart Cities-Reference-Cited by-同舟云学术

Improving KNN Model for Direct Marketing Prediction in Smart Cities

Published:2021 Issue: Volume: Page:107-118
ISSN:1860-949X
Container-title:Studies in Computational Intelligence
language:
Short-container-title:

Author:

Tékouabou Koumétio Stéphane Cédric^ORCID,Toulni Hamza^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-72065-0_7

Reference32 articles.

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2. Grzonka, D., Suchacka, G., & Borowik, B. (2016). Application of selected supervised classification methods to bank marketing campaign. Information Systems in Management, 5(1), 36–48.

3. Parlar, T., et al. (2017). Using data mining techniques for detecting the important features of the bank direct marketing data. International Journal of Economics and Financial Issues, 7(2), 692.

4. Rahangdale, G., Ahirwar, M., & Motwani, M. (2016). Application of k-nn and naive bayes algorithm in banking and insurance domain. International Journal of Computer Science Issues (IJCSI), 13(5), 69.

5. Ayetiran, E. F., & Adeyemo, A. B. (2012). A data mining-based response model for target selection in direct marketing. IJ Information Technology and Computer Science, 1, 9–18.

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