{"id":21097133,"url":"https://github.com/umutkavakli/integrated-machine-learning","last_synced_at":"2026-05-15T13:35:18.979Z","repository":{"id":157346177,"uuid":"577456875","full_name":"umutkavakli/integrated-machine-learning","owner":"umutkavakli","description":"An machine learning application that is integrated both desktop and web.","archived":false,"fork":false,"pushed_at":"2023-02-10T19:02:03.000Z","size":435,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-22T05:40:55.635Z","etag":null,"topics":["flask","machine-learning","pyqt5"],"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/umutkavakli.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":"2022-12-12T19:29:38.000Z","updated_at":"2023-03-29T21:38:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"7f6dc61a-257a-4ff3-b626-9060079e07e3","html_url":"https://github.com/umutkavakli/integrated-machine-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/umutkavakli/integrated-machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umutkavakli%2Fintegrated-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umutkavakli%2Fintegrated-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umutkavakli%2Fintegrated-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umutkavakli%2Fintegrated-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/umutkavakli","download_url":"https://codeload.github.com/umutkavakli/integrated-machine-learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umutkavakli%2Fintegrated-machine-learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33068688,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-15T11:35:32.926Z","status":"ssl_error","status_checked_at":"2026-05-15T11:35:31.362Z","response_time":103,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":["flask","machine-learning","pyqt5"],"created_at":"2024-11-19T22:46:24.992Z","updated_at":"2026-05-15T13:35:18.962Z","avatar_url":"https://github.com/umutkavakli.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Integrated Machine Learning\n\nAn machine learning application that is integrated both desktop and web. Therefore, you can use it prompting with two files. The application calculates cost according to selected ML model with taken inputs.\n\n\u003cb\u003eInputs:\u003c/b\u003e\n\n- Sheet Thickness (mm)\n- Yield Strength (mpa)\n- Tensile Strength (mpa)\n- Mold Weight (Tons)\n\n\u003cb\u003eOutput:\u003c/b\u003e\n- Cost (Euro per Ton)\n\n\n\u003chr\u003e\n\n##### Models\n\n- Linear Regression\n- Decision Tree Regressor\n- Random Forest Regressor\n- XGBoost Regressor\n- SVM\n- K-NN\n\u003chr\u003e\n\n## Usage\n\nTo run app, start cloning repo into your local:\n```\ngit clone https://github.com/umutkavakli/integrated-machine-learning.git\n```\nMove to project directory\n\n```\ncd integrated-machine-learning/\n```\n\n#### GUI\n\nFor desktop app:\n\n```\npython3 gui.py\n```\n\u003cbr\u003e\n\n![](.gifs/gui.gif)\n\n\u003cbr\u003e\n\n#### Web Server\n\nFor web server app:\n\n```\npython3 web.py\n```\n\n![](.gifs/web.gif)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fumutkavakli%2Fintegrated-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fumutkavakli%2Fintegrated-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fumutkavakli%2Fintegrated-machine-learning/lists"}