{"id":20287535,"url":"https://github.com/steviecurran/credit-risk","last_synced_at":"2025-03-04T04:15:24.726Z","repository":{"id":160673931,"uuid":"635516777","full_name":"steviecurran/credit-risk","owner":"steviecurran","description":"Credit Risk Modelling For Dummies: But With Fewer Dummies","archived":false,"fork":false,"pushed_at":"2023-05-10T22:42:58.000Z","size":4396,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-14T08:27:36.431Z","etag":null,"topics":["binary-classification","credit-risk","decision-trees","finance","logistic-regression","machine-learning","nearest-neighbors","suport-vec"],"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/steviecurran.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-05-02T21:36:58.000Z","updated_at":"2023-05-24T03:33:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"c2a45681-aa8d-451b-98e5-9810e40718ad","html_url":"https://github.com/steviecurran/credit-risk","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/steviecurran%2Fcredit-risk","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/steviecurran%2Fcredit-risk/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/steviecurran%2Fcredit-risk/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/steviecurran%2Fcredit-risk/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/steviecurran","download_url":"https://codeload.github.com/steviecurran/credit-risk/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241780496,"owners_count":20019061,"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":["binary-classification","credit-risk","decision-trees","finance","logistic-regression","machine-learning","nearest-neighbors","suport-vec"],"created_at":"2024-11-14T14:40:23.534Z","updated_at":"2025-03-04T04:15:24.707Z","avatar_url":"https://github.com/steviecurran.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# credit-risk\nCredit Risk Modelling For Dummies: But With Fewer Dummies\n\nStandard credit risk analysis utilises scorecards which are built using only datasets with\ncategorical variables. This requires the continuous numerical features to be fine-classed (grouped\ninto discrete sets) and converted to dummy variables. Although the point of the scorecard is to\npresent the model in a simple way, this practice requires much convoluted pre-processing of the\ndata, which greatly bloats the size of the dataset and makes it more susceptible to containing\nerrors. Most importantly though, I find that, by retaining the numerical features, the predictive\npower of the data is great improved, with a Gini coefficient of 0.95 (cf. 0.40 with fine-classing)\nand Kolmogorov-Smirnov statistic of KS = 0.85 (cf. 0.30).\n\nAlthough all of the processing was done in the python scripts mentioned in report.pdf, a simplified \nrun-through is included as the Jupyter notebooks PP.ipynb and ML.ipynb\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsteviecurran%2Fcredit-risk","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsteviecurran%2Fcredit-risk","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsteviecurran%2Fcredit-risk/lists"}