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|Title: ||A machine learning approach to customer prediction|
|Authors: ||Lai, Ying Kit|
|Department: ||Department of Computer Science|
|Issue Date: ||2003|
|Supervisor: ||Dr. Poon, C. K. First Reader: Dr. Ku, Cleve K. W. Second Reader: Dr. Chun, Andy H. W.|
|Abstract: ||Direct promotion like mailings or phone calls to a company’s potential customers
is an effective way to market a product or a service. Being able to make accurate potential
customer predictions, and to promote the product or service to them timely is certainly
valuable to companies. Recently, machine learning approaches has been widely
employed in the subject of customer prediction.
In this project, machine learning techniques are used to solve a real-world
problem in predicting potential customers of a Dutch insurance company. Two of the
machine learning models, namely ANN (Artificial Neural Network) and Bayesian
Learning are the focus of study. Prototypes from both machine learning paradigms are
built and their performance evaluated. In addition, comparisons are made on the
prediction capabilities of the two approaches.
Since the design parameters (e.g. learning rates, m-estimates) have great impacts
on the performance (like training time, prediction capability, etc.) of the learning models,
the available design choices are discussed and their values of selection are justified.
The effects of some of the common practical issues like overfitting and feature
selection are also examined. From the result of the experiment, one can tell the
importance of such issues which are often ignored by machine learning practitioners.|
|Appears in Collections:||Computer Science - Undergraduate Final Year Projects|
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