{"id":18330115,"url":"https://github.com/dmarks84/coursework_project_ml-model-eval-refine","last_synced_at":"2026-04-09T18:17:22.458Z","repository":{"id":217712649,"uuid":"744623183","full_name":"dmarks84/Coursework_Project_ML-Model-Eval-Refine","owner":"dmarks84","description":"Project for IBM Data Science course on ML Models \u0026 Analysis -- Read in large dataset of home sales and utilized polynomial linear regression analysis to make predictions of future home sales prices","archived":false,"fork":false,"pushed_at":"2024-01-17T23:22:16.000Z","size":151,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T10:32:08.068Z","etag":null,"topics":["classification","communication","data-modeling","dataframes","machine-learning","matplotlib","numpy","pandas","programming","python","regression","scikit-learn","scipy","seaborn","supervised-ml","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dmarks84.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}},"created_at":"2024-01-17T17:16:37.000Z","updated_at":"2024-03-05T21:14:02.000Z","dependencies_parsed_at":"2024-01-18T01:18:15.855Z","dependency_job_id":"929ce373-b404-41ea-beda-03ab8cb86e1d","html_url":"https://github.com/dmarks84/Coursework_Project_ML-Model-Eval-Refine","commit_stats":null,"previous_names":["dmarks84/project_ml-model-eval-refine","dmarks84/coursework_project_ml-model-eval-refine"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FCoursework_Project_ML-Model-Eval-Refine","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FCoursework_Project_ML-Model-Eval-Refine/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FCoursework_Project_ML-Model-Eval-Refine/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dmarks84%2FCoursework_Project_ML-Model-Eval-Refine/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dmarks84","download_url":"https://codeload.github.com/dmarks84/Coursework_Project_ML-Model-Eval-Refine/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248078971,"owners_count":21044208,"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":["classification","communication","data-modeling","dataframes","machine-learning","matplotlib","numpy","pandas","programming","python","regression","scikit-learn","scipy","seaborn","supervised-ml","visualization"],"created_at":"2024-11-05T19:20:32.061Z","updated_at":"2026-04-09T18:17:22.394Z","avatar_url":"https://github.com/dmarks84.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Project(Project_ML-Model-Eval-Refine)\n### Part of the Coursera series: IBM Data Science\n    \n## Summary\nIn this project, I took in data related to home sales in order to develop a model to predict future home sales.  We had to wrnagle the data and transform it, perform EDA and look at various correlations between features in order to set up a machine learning (polynomial linear regression) model on which to train and then create predictions.  We performed feature engneering and scaling in the process.\n\n## Skills (Developed \u0026 Applied)\nProgramming, Python, Databases, Statistics, Probability, SciPy, Numpy, Pandas, Seaborn, Matplotlib, Scikit-learn, Data Modeling, EDA, Data Visualization, Data Summarization, Data Reporting, Regression, Supervised ML, Communication\n    ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmarks84%2Fcoursework_project_ml-model-eval-refine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmarks84%2Fcoursework_project_ml-model-eval-refine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmarks84%2Fcoursework_project_ml-model-eval-refine/lists"}