{"id":24539781,"url":"https://github.com/walidalsafadi/divorce-predictors-dataset","last_synced_at":"2026-05-11T16:38:07.537Z","repository":{"id":160248276,"uuid":"604244631","full_name":"WalidAlsafadi/Divorce-Predictors-Dataset","owner":"WalidAlsafadi","description":"A total of 170 couples are included in this dataset, along with the Divorce Predictor Scale variables (DPS), a questionnaire of 54 questions, A five-point scale was used to measure all responses.","archived":false,"fork":false,"pushed_at":"2023-02-20T16:33:41.000Z","size":261,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T04:42:34.227Z","etag":null,"topics":["cross-validation","data-visualization","divorce-prediction","divorce-predictors","jupyter","logistic-regression","machine-learning","machine-learning-algorithms","pca","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/WalidAlsafadi.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-02-20T16:33:38.000Z","updated_at":"2025-01-05T13:05:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"10bb603c-a1db-4cfa-9b46-6e07f9ff58bb","html_url":"https://github.com/WalidAlsafadi/Divorce-Predictors-Dataset","commit_stats":null,"previous_names":["walidalsafadi/divorce-predictors-dataset","walidkw/divorce-predictors-dataset"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/WalidAlsafadi/Divorce-Predictors-Dataset","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WalidAlsafadi%2FDivorce-Predictors-Dataset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WalidAlsafadi%2FDivorce-Predictors-Dataset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WalidAlsafadi%2FDivorce-Predictors-Dataset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WalidAlsafadi%2FDivorce-Predictors-Dataset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/WalidAlsafadi","download_url":"https://codeload.github.com/WalidAlsafadi/Divorce-Predictors-Dataset/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/WalidAlsafadi%2FDivorce-Predictors-Dataset/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32903835,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-10T13:40:02.631Z","status":"online","status_checked_at":"2026-05-11T02:00:05.975Z","response_time":120,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["cross-validation","data-visualization","divorce-prediction","divorce-predictors","jupyter","logistic-regression","machine-learning","machine-learning-algorithms","pca","python"],"created_at":"2025-01-22T17:16:17.984Z","updated_at":"2026-05-11T16:38:07.504Z","avatar_url":"https://github.com/WalidAlsafadi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Divorce Predictors Dataset\n\nWhen we talk about relationships\nbetween men and women, we usually refer to\nmarriage. However, how can we identify a good\nrelationship? Predicting divorce has been an area\nof interest for researchers and practitioners for\nmany years, as it has significant social and\neconomic implications. One of the main reasons\nfor predicting divorce is to identify couples who\nmay be at risk of divorce so that interventions can\nbe put in place to help them address any issues and improve their relationship. There are\nseveral potential benefits to using machine learning to predict divorce. For example,\nmachine learning algorithms can identify risk factors for divorce that may not be\nimmediately obvious to human analysts.\n\n The Divorce Predictors Dataset is a publicly available UCI Machine Learning Repository dataset. A total of 170 couples are included in this dataset, along with the Divorce Predictor Scale variables (DPS), a questionnaire of 54 questions, based on the Gottman couples therapy. Records were collected from face-to-face interviews with couples who were already divorced (49%) or happily married (51%) in various regions of Turkey. A five-point scale was used to measure all responses (0=Never, 1=Seldom, 2=Average, 3=Frequently, 4=Always).\n\n# Requirements:\n\n1. Formulate the machine learning problem by identifying the task (classification or\nregression), data, and challenges.\n\n2. Perform Exploratory Data Analysis. This includes:\n\n* Plot relationship between variables\n* Identify correlated features or the ones that highly correlated with the\nlabel/outcome, if any\n* Perform data imputation for missing variables\n* Encode your outcome to a one-hot vector, if needed\n* Remove redundant variables by dimensionality reduction techniques\n\n3. Consider splitting the data into Training, Validation, and Testing sets. The suggested\nsplit is 70%, 10%, and 20% held-out testing set, respectively. (same split for all tasks)\n\n4. Build and develop the following models (tasks):\n* Task#: Logistic/Linear Regression according to your formulation in step 1\n\n5. For each task, run the model with and without processing the data, e.g., without\nnormalization or dimensionality reduction, and compare the model’s performance\nafter you normalize and/or reduce the dimensionality of the data.\n\n6. For each task, show how you performed the model selection. For example,\ndemonstrate the performance of variants of your model with different\nhyper-parameters, e.g., number of clusters and initialization when it comes to\nclustering methods.\n\n7. For each task, perform a 5-fold Cross Validation.\n\n8. For each task, run the corresponding evaluation metrics on each fold to demonstrate\nthe performance of your model on the held-out testing set","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwalidalsafadi%2Fdivorce-predictors-dataset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwalidalsafadi%2Fdivorce-predictors-dataset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwalidalsafadi%2Fdivorce-predictors-dataset/lists"}