The Role of Data Mining Technology in Electronic Transaction Expansion at Dashen Bank S.C
| dc.contributor.advisor | Teferi, Dereje(PhD) | |
| dc.contributor.author | Berhe, Luel | |
| dc.date.accessioned | 2018-11-27T07:41:24Z | |
| dc.date.accessioned | 2023-11-18T12:44:03Z | |
| dc.date.available | 2018-11-27T07:41:24Z | |
| dc.date.available | 2023-11-18T12:44:03Z | |
| dc.date.issued | 2011-07 | |
| dc.description.abstract | In this study the application of decision tree J48, ANN classification algorithms, and Kmeans clustering algorithm of data mining on CRM the case of EFT of POS service of the Dashen Bank S.C. have been discovered within the framework of CRISP-DM model. The card holder customers data along with customer book information have been collected, cleansed, integrated and transformed for testing using the clustering and classification models. The final dataset consists of 11000 records in which different clustering models at k values of 6, 5, and 4 with different seed values have been traced and evaluated against their performances. The cluster model at k value of 6 with default seed value has shown a better performance. Hence, the output of the best clustering model (i.e. at k=6) has been used as an input for the decision tree and Artificial Neural Network (ANN) classification models. Different classifications with the J48 decision tree algorithm are tested with 10-fold cross validation, and splitting the dataset into 70% for training and 30% for testing, techniques by setting the cluster index formed by the cluster model as dependent variable and the remaining variables as independent variables. Different decision tree classification models with minNumObj =default, 5, 10, 15, 20, and 25 have been experimented. From these decision tree parameters, a model with default parameter values showed the maximum overall classification accuracy (i.e. 99.55%). Likewise, different classification models of Multilayerperceptron ANN have been tested by changing the hidden layer and learning rate parameter’s value. As a result, a model with a classification accuracy of 99.97%, which is with default parameter value, was chosen. Lastly, a comparison of the decision tree and ANN models in terms of the overall classification accuracy , accuracy in classifying high level customers, and accuracy in classifying low level/value customers have been undertaken. Therefore, the ANN model has been the best in these evaluation parameters, and thus selected as a better classifier in EFT of POS service customers. The result obtained in this study was encouraging as it has very high classification accuracy. This helps and strengthens the possible application of data mining to the xi banking industry in general, and in the EFT of POS service expansion marketing strategy at the Dashen Bank S.C. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/14543 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Data Mining | en_US |
| dc.title | The Role of Data Mining Technology in Electronic Transaction Expansion at Dashen Bank S.C | en_US |
| dc.type | Thesis | en_US |