{"id":15414529,"url":"https://github.com/phenomsg/house-prices---advanced-regression-techniques","last_synced_at":"2026-02-21T22:01:04.706Z","repository":{"id":256819160,"uuid":"854624031","full_name":"PhenomSG/House-Prices---Advanced-Regression-Techniques","owner":"PhenomSG","description":"Predict house prices in Ames, Iowa, using 79 features, evaluated by RMSE of log-transformed predicted and actual prices.","archived":false,"fork":false,"pushed_at":"2024-09-12T18:41:52.000Z","size":183,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-21T08:44:54.522Z","etag":null,"topics":["python3","pytorch","pytorch-implmention","regression-analysis"],"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/PhenomSG.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":"2024-09-09T13:56:46.000Z","updated_at":"2024-09-12T18:44:39.000Z","dependencies_parsed_at":"2024-09-13T07:44:18.777Z","dependency_job_id":null,"html_url":"https://github.com/PhenomSG/House-Prices---Advanced-Regression-Techniques","commit_stats":null,"previous_names":["phenomsg/house-prices---advanced-regression-techniques"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/PhenomSG/House-Prices---Advanced-Regression-Techniques","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FHouse-Prices---Advanced-Regression-Techniques","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FHouse-Prices---Advanced-Regression-Techniques/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FHouse-Prices---Advanced-Regression-Techniques/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FHouse-Prices---Advanced-Regression-Techniques/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PhenomSG","download_url":"https://codeload.github.com/PhenomSG/House-Prices---Advanced-Regression-Techniques/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PhenomSG%2FHouse-Prices---Advanced-Regression-Techniques/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29694781,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-21T18:18:25.093Z","status":"ssl_error","status_checked_at":"2026-02-21T18:18:22.435Z","response_time":107,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["python3","pytorch","pytorch-implmention","regression-analysis"],"created_at":"2024-10-01T17:03:55.811Z","updated_at":"2026-02-21T22:01:04.421Z","avatar_url":"https://github.com/PhenomSG.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# House Prices - Advanced Regression Techniques\n\nThis project focuses on predicting house sale prices using a comprehensive dataset of residential homes in Ames, Iowa. The dataset contains 79 explanatory variables that describe various aspects of the homes, such as size, location, quality, and condition.\n\n![Main Image](house.png)\n\n## Project Overview\n\nThe goal of this project is to predict the sales price for each house based on its features. The predictions will be evaluated using Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted and actual sale prices. This approach ensures that errors in predicting both expensive and affordable houses are treated equally.\n\n## Dataset\n\nThe dataset includes various features about each house, including:\n- **Lot size and area**\n- **Number of bedrooms and bathrooms**\n- **Year built and remodeled**\n- **Garage type and size**\n- **Quality of materials**\n- **Neighborhood and location features**\n\nThese features allow us to build a model that can capture the various factors influencing house prices.\n\n## Evaluation Metric\n\nThe model's performance is measured by RMSE between the logarithmic values of the predicted and actual sale prices. This ensures a balanced evaluation for houses of different price ranges.\n\n## Acknowledgments\n\nThe Ames Housing dataset was compiled by Dean De Cock and is widely used in data science education as an alternative to the Boston Housing dataset.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphenomsg%2Fhouse-prices---advanced-regression-techniques","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphenomsg%2Fhouse-prices---advanced-regression-techniques","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphenomsg%2Fhouse-prices---advanced-regression-techniques/lists"}