Analysis of CWU foundation data: pre-processing and data mining
Document Type
Oral Presentation
Campus where you would like to present
SURC 137B
Start Date
17-5-2012
End Date
17-5-2012
Abstract
The CWU foundation is an organization which works with donors, alumni, and friends to raise private funds to support CWU students, faculty, and programs. It has accumulated over 20 year's data up to year 2000 about their members and donations. The CWU foundation hopes to find predictive patterns from those data and to use them as guidelines to raise more funds. In this project, various data preparation methods and mining algorithms were used to accomplish the task. Based on the independent quality of the data, naïve Bayes classifier was chosen because of its simplicity and performance. Confusion matrix was used to evaluate the performance of the classifier. Half of the data was used to build a model of probability distribution; the other half was used to test the model. The results show that two types of members are more likely to donate to CWU. One is married members with double income; the other is members who work in educational areas. The future step is to gather more data after year 2000 and find more behavioral patterns of donors with time series analysis.
Recommended Citation
Wang, Ying, "Analysis of CWU foundation data: pre-processing and data mining" (2012). Symposium Of University Research and Creative Expression (SOURCE). 27.
https://digitalcommons.cwu.edu/source/2012/oralpresentations/27
Additional Mentoring Department
Computer Science
Analysis of CWU foundation data: pre-processing and data mining
SURC 137B
The CWU foundation is an organization which works with donors, alumni, and friends to raise private funds to support CWU students, faculty, and programs. It has accumulated over 20 year's data up to year 2000 about their members and donations. The CWU foundation hopes to find predictive patterns from those data and to use them as guidelines to raise more funds. In this project, various data preparation methods and mining algorithms were used to accomplish the task. Based on the independent quality of the data, naïve Bayes classifier was chosen because of its simplicity and performance. Confusion matrix was used to evaluate the performance of the classifier. Half of the data was used to build a model of probability distribution; the other half was used to test the model. The results show that two types of members are more likely to donate to CWU. One is married members with double income; the other is members who work in educational areas. The future step is to gather more data after year 2000 and find more behavioral patterns of donors with time series analysis.
Faculty Mentor(s)
Boris Kovalerchuk