{"id":20082345,"url":"https://github.com/hariprasath-v/machinehack_analytics_olympiad_2023","last_synced_at":"2026-04-08T23:33:40.634Z","repository":{"id":204821130,"uuid":"703025171","full_name":"hariprasath-v/Machinehack_analytics_olympiad_2023","owner":"hariprasath-v","description":"Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.","archived":false,"fork":false,"pushed_at":"2023-10-10T14:13:14.000Z","size":7132,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-13T01:44:53.270Z","etag":null,"topics":["binaryclassification","catboost","exploratory-data-analysis","machine-learning","numpy","pandas","python","scikit-learn","shap"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hariprasath-v.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-10-10T13:03:40.000Z","updated_at":"2023-11-01T04:04:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"23be09c5-5f6f-42b7-ac1c-8ec2f839544b","html_url":"https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023","commit_stats":null,"previous_names":["hariprasath-v/machinehack_analytics_olympiad_2023"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hariprasath-v%2FMachinehack_analytics_olympiad_2023","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hariprasath-v%2FMachinehack_analytics_olympiad_2023/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hariprasath-v%2FMachinehack_analytics_olympiad_2023/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hariprasath-v%2FMachinehack_analytics_olympiad_2023/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hariprasath-v","download_url":"https://codeload.github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241515953,"owners_count":19975140,"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":["binaryclassification","catboost","exploratory-data-analysis","machine-learning","numpy","pandas","python","scikit-learn","shap"],"created_at":"2024-11-13T15:43:03.385Z","updated_at":"2026-04-08T23:33:35.614Z","avatar_url":"https://github.com/hariprasath-v.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machinehack_analytics_olympiad_2023\n\n### Competition hosted on \u003ca href=\"https://machinehack.com/hackathons/analytics_olympiad_2023/overview\"\u003eMachinehack\u003c/a\u003e\n\n# About\n\n### Create a machine learning model to determine the likelihood of a customer defaulting on a loan based on credit history, payment behavior, and account details.\n\n### The Final Competition score is 1.0\n\n### Leaderboard Rank is 5/158\n\n### The Evaluation Metric is roc_auc_score.\n\n### File information\n \n * analytics-olympiad-2023-eda.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=\u0026message=Open%20in%20Kaggle\u0026labelColor=grey\u0026color=blue\u0026logo=kaggle)](https://www.kaggle.com/code/hari141v/analytics-olympiad-2023-eda)\n    #### Basic Exploratory Data Analysis\n    #### Packages Used,\n        * seaborn\n        * Pandas\n        * Numpy\n        * Matplotlib\n* machinehack-analytics-olympiad-2022-model.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=\u0026message=Open%20in%20Kaggle\u0026labelColor=grey\u0026color=blue\u0026logo=kaggle)](https://www.kaggle.com/code/hari141v/analytics-olympiad-2023-model)\n    #### Data Pre-processing and model. \n    #### Packages Used,\n        * Sklearn\n        * Pandas\n        * Numpy\n        * Matplotlib\n        * catboost\n        * shap\n     #### The Catboost model was trained separately for both targets, using default parameters.\n     #### The model was evaluated at each iteration using validation data.\n     #### The model's performance was assessed using an accuracy score. \n     #### [For more detailed information about the model.](https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/blob/main/Analytics%20Olympiad%202023.pdf)\n     \n\n### Catboost – SHAP feature importance for primary close flag\n![Alt text](https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/blob/main/EDA_and_Model_Interpretation_Visualization/SHAP_Global_feature_importance_Primary_close_flag.png)\n\n### Catboost – SHAP feature importance for final close flag\n![Alt text](https://github.com/hariprasath-v/Machinehack_analytics_olympiad_2023/blob/main/EDA_and_Model_Interpretation_Visualization/SHAP_Global_feature_importance_Final_close_flag.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhariprasath-v%2Fmachinehack_analytics_olympiad_2023","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhariprasath-v%2Fmachinehack_analytics_olympiad_2023","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhariprasath-v%2Fmachinehack_analytics_olympiad_2023/lists"}