{"id":23442126,"url":"https://github.com/ssiarhei115/customer-classification","last_synced_at":"2025-04-09T21:48:30.244Z","repository":{"id":204651740,"uuid":"712350394","full_name":"ssiarhei115/Customer-Classification","owner":"ssiarhei115","description":"Developing ML model predicting bank' customer inclination to open a deposit","archived":false,"fork":false,"pushed_at":"2023-10-31T09:57:59.000Z","size":2498,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-09T21:48:21.537Z","etag":null,"topics":["big-data","big-data-analytics","data","data-science","data-visualization","mashine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ssiarhei115.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-10-31T09:48:39.000Z","updated_at":"2024-02-19T14:21:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"d64126cb-6b64-4fbe-93c6-b9ba8f7eac06","html_url":"https://github.com/ssiarhei115/Customer-Classification","commit_stats":null,"previous_names":["ssiarhei115/customer-classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssiarhei115%2FCustomer-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssiarhei115%2FCustomer-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssiarhei115%2FCustomer-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ssiarhei115%2FCustomer-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ssiarhei115","download_url":"https://codeload.github.com/ssiarhei115/Customer-Classification/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248119416,"owners_count":21050754,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["big-data","big-data-analytics","data","data-science","data-visualization","mashine-learning"],"created_at":"2024-12-23T17:28:48.349Z","updated_at":"2025-04-09T21:48:30.219Z","avatar_url":"https://github.com/ssiarhei115.png","language":"Jupyter Notebook","readme":"# Bank customer classification\r\n\r\n## Main goal\r\nDeveloping ML model predicting bank' customer inclination to open a deposit\r\n\r\n### Tasks \r\n1) EDA;\r\n2) Feature preprocessing;\r\n3) Feature selection;\r\n4) Data scaling;\r\n5) Fitting Models (LogisticRegression, DecisionTree, RandomForrest); Hyperparameter optimization; Scores evaluation\r\n\r\n## Data set feature description\r\n### Customer details:\r\n- age;\r\n- job;\r\n- marital (relationship status);\r\n- education (level of education);\r\n- default (has got an expired credit);\r\n- housing (has got a housing loan);\r\n- loan (has got a personal loan);\r\n- balance.\r\n\r\n### Features related to the last contact:\r\n- contact (contact type with a customer);\r\n- month (month of the last contact);\r\n- day (day of the last contact);\r\n- duration (contact duration, seconds).\r\n\r\n### Other features:\r\n- campaign (quantity of contacts with a client durint the current campaign);\r\n- pdays (quantity of days missed since the last marketing campaign till the contact in the current campaign);\r\n- previous (quantity of contacts till the current campaign)\r\n- poutcome (the result of the previous campaign).\r\n\r\n### Target:\r\n- deposit. Defines if a customer agrees to open a credit in a bank.\r\n\r\n\r\n## Summary\r\n    * Following classifiers were tested and compaired during the investigation: LogisticRegression, DecisionTree, RandomForest, GradientBoosting, Stacking\r\n    * DecisionTree, RandomForest, GradientBoosting models provided comparable scores after hyperparameters optimization  \r\n    * The best scores obtained with StackingClassifier after threshold optimizing: f1-score=0.83, accuracy=0.83\r\n\r\n## Tools used\r\npandas==2.0.1\r\nnumpy==1.23.5\r\nmatplotlib==3.6.3\r\noptuna==3.3.0\r\nseaborn==0.12.2\r\nscikit-learn==1.3.0\r\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fssiarhei115%2Fcustomer-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fssiarhei115%2Fcustomer-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fssiarhei115%2Fcustomer-classification/lists"}