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Customer Relationship Management usi...

University of Toronto (Canada).

Customer Relationship Management using Ensemble Methods.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Customer Relationship Management using Ensemble Methods./
作者:	Cui, Yue.
面頁冊數:	1 online resource (62 pages)
附註:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438185203

Customer Relationship Management using Ensemble Methods.
Cui, Yue.

Customer Relationship Management using Ensemble Methods. - 1 online resource (62 pages)

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.A.S.)--University of Toronto (Canada), 2018.

Includes bibliographical references

This thesis aims to provide a method for customer relationship management prediction to beat the in-house classification method implemented by the data provider company. By reviewing the common machine learning algorithms, we recommend ensemble methods to predict the targets. Three different ensemble methods are implemented in the thesis: random forest, gradient boosting decision trees and ensemble selection. With the results, we conclude that all ensemble methods outperform the benchmark, reduce the positive predictions and increase the true positive rates. Ensemble selection performs the best, followed by the gradient boosting decision trees. In addition, the results indicate that the ensemble methods' running time significantly increase when compared to the benchmark. The results also indicate that careful feature selection can significantly simplify the training and prediction process. We further discuss the potential applications in marketing using the prediction results and the trade-off between accuracy and computational complexity when applying ensemble methods.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438185203Subjects--Topical Terms:

561178
Information science.
Index Terms--Genre/Form:

554714
Electronic books.

Customer Relationship Management using Ensemble Methods.
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245 1 0 $a Customer Relationship Management using Ensemble Methods.
264 0 $c 2018
300 $a 1 online resource (62 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Masters Abstracts International, Volume: 58-01.
500 $a Adviser: Joseph C. Paradi.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2018.
504 $a Includes bibliographical references
520 $a This thesis aims to provide a method for customer relationship management prediction to beat the in-house classification method implemented by the data provider company. By reviewing the common machine learning algorithms, we recommend ensemble methods to predict the targets. Three different ensemble methods are implemented in the thesis: random forest, gradient boosting decision trees and ensemble selection. With the results, we conclude that all ensemble methods outperform the benchmark, reduce the positive predictions and increase the true positive rates. Ensemble selection performs the best, followed by the gradient boosting decision trees. In addition, the results indicate that the ensemble methods' running time significantly increase when compared to the benchmark. The results also indicate that careful feature selection can significantly simplify the training and prediction process. We further discuss the potential applications in marketing using the prediction results and the trade-off between accuracy and computational complexity when applying ensemble methods.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0723
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Toronto (Canada). $b Chemical Engineering & Applied Chemistry. $3 1181092
773 0 $t Masters Abstracts International $g 58-01(E).
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