{"id":"W3002583521","doi":"10.1109/besc48373.2019.8963436","title":"A Case Study of Predicting Banking Customers Behaviour by Using Data Mining","year":2019,"lang":"en","type":"article","venue":"","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Data mining; Customer relationship management; Artificial neural network; Data warehouse; Variety (cybernetics); Association rule learning; Data modeling; Data science; Machine learning; Artificial intelligence; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001975327,0.0006160954,0.0005653468,0.001500106,0.001159961,0.001312164,0.001313223,0.002053031,0.002110684],"category_scores_gemma":[0.006321154,0.000316363,0.0006071991,0.002833068,0.0005119755,0.001244872,0.0006370124,0.0009228279,0.0005379735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106686,"about_ca_system_score_gemma":0.0009358659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01883399,"about_ca_topic_score_gemma":0.02055861,"domain_scores_codex":[0.9986456,0.0006120777,0.0001132246,0.0001713053,0.000324882,0.0001328728],"domain_scores_gemma":[0.9947296,0.003818366,0.0002306223,0.0003323715,0.0006442052,0.0002447241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001924696,0.004447132,0.3411618,0.001203847,0.0004615153,0.02870978,0.004942457,0.3354864,0.008431057,0.008323214,0.02094095,0.2439672],"study_design_scores_gemma":[0.0001467911,0.0009852087,0.0703373,0.0001199104,0.000141968,0.003271558,0.004349076,0.893927,0.009331203,0.003662421,0.01363451,0.00009291044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9665326,0.0004855224,0.0226694,0.00208737,0.00006648273,0.0003398157,0.00163041,0.0003811323,0.005807275],"genre_scores_gemma":[0.9669125,0.0002791366,0.02969143,0.0001248503,0.00002649688,0.0001016762,0.0008482501,0.00002598501,0.001989654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01883399,"threshold_uncertainty_score":0.03744876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06840287939409515,"score_gpt":0.2967648518924933,"score_spread":0.2283619724983982,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}