{"_buckets": {"deposit": "40c15367-5a60-4d85-9d66-2cbb26585a1a"}, "_deposit": {"created_by": 45, "id": "6164", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "6164"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/6164", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Customer Churn Analysis in Banking Sector", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The role of ICT in the banking sector is a crucial part of the development of nations. The development of the banking sector mostly depends on its valuable customers. So, customer churn analysis is needed to determine customers whether they are at risk of leaving or worth retaining. From organizational point of view, gaining new customers is usually more difficult or more expensive than retaining existing customers. So, customer churn prediction has been popular in the banking industry. By reducing customer churn or attrition, the commercial banks gain not only more profits but also enhancing core competitiveness among the competitors. Although many researchers proposed many single prediction models and some hybrid model, but accuracy is still weak and computation time of some algorithms is still increased. In this research, churn prediction model of classifying bank customer is built by using the hybrid model of k-means and Support Vector Machine data mining methods on bank customer churn dataset to overcome the instability and limitations of single prediction model and predict churn trend of high value users."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "churn prediction"}, {"interim": "data mining"}, {"interim": "k-means"}, {"interim": "Support Vector Machine"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-11-13"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Customer Churn Analysis in Banking Sector.pdf", "filesize": [{"value": "501 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 501000.0, "url": {"url": "https://meral.edu.mm/record/6164/files/Customer Churn Analysis in Banking Sector.pdf"}, "version_id": "2bb00054-0be7-4fac-8113-19830715824c"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "1", "subitem_journal_title": "University Journal of Research and Innovation 2019 of University of Computer Studies (Pakokku)", "subitem_pages": "191-195", "subitem_volume": "1"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Saw Thazin Khine"}, {"subitem_authors_fullname": "Win Win Myo"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Journal article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-12-01"}, "item_title": "Customer Churn Analysis in Banking Sector", "item_type_id": "21", "owner": "45", "path": ["1596102427017"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000006164", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-11-13"}, "publish_date": "2020-11-13", "publish_status": "0", "recid": "6164", "relation": {}, "relation_version_is_last": true, "title": ["Customer Churn Analysis in Banking Sector"], "weko_shared_id": -1}