{"id":27169010,"url":"https://github.com/doguhannilt/planets-demo","last_synced_at":"2026-05-14T23:32:18.263Z","repository":{"id":202779998,"uuid":"708072010","full_name":"Doguhannilt/Planets-demo","owner":"Doguhannilt","description":null,"archived":false,"fork":false,"pushed_at":"2023-10-24T03:16:13.000Z","size":1707,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-09T06:25:55.258Z","etag":null,"topics":["natural-language-processing","planets","streamlit"],"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/Doguhannilt.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":"2023-10-21T12:48:10.000Z","updated_at":"2025-01-11T09:39:38.000Z","dependencies_parsed_at":"2023-10-24T04:28:10.364Z","dependency_job_id":null,"html_url":"https://github.com/Doguhannilt/Planets-demo","commit_stats":null,"previous_names":["doguhannilt/planets-demo"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Doguhannilt/Planets-demo","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Doguhannilt%2FPlanets-demo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Doguhannilt%2FPlanets-demo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Doguhannilt%2FPlanets-demo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Doguhannilt%2FPlanets-demo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Doguhannilt","download_url":"https://codeload.github.com/Doguhannilt/Planets-demo/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Doguhannilt%2FPlanets-demo/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273346734,"owners_count":25089449,"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","status":"online","status_checked_at":"2025-09-02T02:00:09.530Z","response_time":77,"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":["natural-language-processing","planets","streamlit"],"created_at":"2025-04-09T06:22:20.456Z","updated_at":"2026-05-14T23:32:13.242Z","avatar_url":"https://github.com/Doguhannilt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/Doguhannilt/Planets-demo/assets/77373443/35232c88-ee67-4897-992e-cc71d4ef6ca8\"\u003e\n\u003c/div\u003e\n\n\n\u003ch1\u003eOverview\u003c/h1\u003e\n\n\u003cp\u003eThis system employs machine learning to detect planets by analyzing specific parameters in observed astronomical data. Through meticulous parameter selection and analysis, the model uncovers potential signals indicative of exoplanetary presence. The system's success contributes to the expansion of our knowledge of celestial bodies beyond our solar system\u003c/p\u003e\n\n\u003ch1\u003eDisclaimer\u003c/h1\u003e\n\n\u003cp\u003e\nThis application has been designed for demonstration and preview purposes. While the results generated by the model may not fully align with real-world scenarios, this service is intended to assess the model's fundamental capabilities and potential.\nIn the coming phases, precision will be exercised in refining the model, and adjustments will be made based on feedback received from the demonstration.\u003c/p\u003e\n\n\u003ch1\u003eInterface Guide\u003c/h1\u003e\n\n\u003cp\u003e\u003cstrong\u003eLeft Bar: \u003c/strong\u003eIn the left bar, you can read the parameter's explanation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSliders: \u003c/strong\u003eSliders are the adjustable features\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpotify Playlist: \u003c/strong\u003eHave you ever pondered the auditory essence of a planet? If so, then welcome to this playlist!\u003c/p\u003e\n\n\u003ch1\u003eDemo Adjustable Parameters Explanation\u003c/h1\u003e\n\n\u003cp\u003eIn our demo project, we made a clear documentation to understand the parameters of the project more properly\u003c/p\u003e\n\n\u003ca href=\"https://drive.google.com/drive/folders/1EFylLOMbCPvLoc3x_g6RIbe3AMs3isUV\"\u003eGoogle Drive Link\u003c/a\u003e\u003cbr\u003e\n\n\u003cp\u003e\u003cstrong\u003eThank you\u003c/strong\u003e to my friend for preparing this unique documentation for the project! Here is the \u003ca href =\"https://github.com/Alivan-1502\"\u003eGithub\u003c/a\u003e of my friend!\u003c/p\u003e\n\n\u003ch1\u003eModels and Their Explanations\u003c/h1\u003e\n\u003clu\u003e\n\u003cli\u003eAs a basic demo model, we've been using a Random Forest Classifier and at least 1000 rows and 9 columns which are called \u003cstrong\u003edemo model.joblib\u003c/strong\u003e. This model is just only to display the project in Streamlit -you may see that in above- so that's why we ignored get valuable accuracy in the model.\u003c/li\u003e\n\u003cli\u003eThere is also expanded demo model that has more capacity to estimate planet based on the parameters given by the user. This expanded demo model has the ability to give you more accuracy (between %50 to %56) and we used almost 9000 rows and 9 columns to make it more complex. The expanded model name is \u003cstrong\u003emodel.joblib\u003c/strong\u003e\u003c/li\u003e\n\u003c/lu\u003e\n\u003ch4\u003eModel's Driver\u003c/h4\u003e\n\u003cli\u003eYou can access the models by using the \u003ca href = \"https://drive.google.com/drive/folders/1EFylLOMbCPvLoc3x_g6RIbe3AMs3isUV?usp=sharing\"\u003elink\u003c/a\u003e\u003c/li\u003e\n\n\u003ch3\u003eWhy Random Forest Classifier?\u003c/h3\u003e\n\n\u003ch4\u003eMachine Learning Algorithms\u003c/h4\u003e\n\u003ctable border=\"1\"\u003e\n  \u003ctr\u003e\n    \u003cth\u003eAlgorithm\u003c/th\u003e\n    \u003cth\u003eAccuracy\u003c/th\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDecision Tree Classifier\u003c/td\u003e\n    \u003ctd\u003e38%\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDecision Tree Classifier (RandomizedSearchCV)\u003c/td\u003e\n    \u003ctd\u003e22%\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eLogistic Regression\u003c/td\u003e\n    \u003ctd\u003e20%\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eSVC\u003c/td\u003e\n    \u003ctd\u003e19%\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eKNeighborClassifier\u003c/td\u003e\n    \u003ctd\u003e16%\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eKNeighborClassifier (With Best Params)\u003c/td\u003e\n    \u003ctd\u003e19%\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n\n\u003ch4\u003eDeep Learning Algorithm\u003c/h4\u003e\n\u003ctable\u003e\n  \u003ctr\u003e\n\u003ctd\u003eKeras Sequential with PCA\u003c/td\u003e\n\u003ctd\u003e20%\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/table\u003e\n\n\u003ch3\u003eWhat is the negative impact for the models?\u003c/h3\u003e\n\u003cp\u003eIn demo process, we detected that the dataset is not enough to explore complex relationships among the features so that's why the accuracy of each model are not fitting with our project's request. First of all, I will collect more proper data but the major thing is \u003cstrong\u003eFeature Selection results\u003c/strong\u003e, I'll collect the data\nbased on the feature selection results.\u003c/p\u003e\n\u003cp\u003eThe another issue is using resources. In the end of the day, more data will want more resources, the plan is about using \u003cstrong\u003ePrincipal Component Analysis\u003c/strong\u003e\u003c/p\u003e\n\n\u003ch1\u003eResource Using\u003c/h1\u003e\n\u003cp\u003eIn this demo project, we used one of the main concept of Joblib, that's \u003cstrong\u003eMemory Cache\u003c/strong\u003e. It is cache memory, also called cache, supplementary memory system that temporarily stores frequently used instructions and data for quicker processing by the central processing unit (CPU) of a computer. The cache augments, and is an extension of, a computer's main memory.\u003c/p\u003e\n\n\n\u003ch1\u003eDemo Delights: Showcasing My Top Features and Functionalities\u003c/h1\u003e\n\u003clu\u003e\n\u003cli\u003eOOP schema\u003c/li\u003e\n\u003cli\u003eCosine Similarity\u003c/li\u003e\n\u003cli\u003eML\u0026DL algorithms\u003c/li\u003e\n\u003cli\u003eStreamlit\u003c/li\u003e\n\u003cli\u003ePython\u003c/li\u003e\n\u003c/lu\u003e\n\n\u003ch1\u003eDataset\u003c/h1\u003e\n\u003cp\u003eDataset is provided by NASA Exoplanet Archive which is completely free to use. Here is the \u003ca href=\"https://exoplanetarchive.ipac.caltech.edu\"\u003elink\u003c/a\u003e\u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/Doguhannilt/Planets-demo/assets/77373443/75156d8d-ba12-45d5-b741-a16124bd41c0\"\u003e\u003c/img\u003e\n\u003c/div\u003e\n\n\u003ch1\u003eHow to use?\u003c/h1\u003e\n\n\u003clu\u003e\n\u003cli\u003eWrite \u003ccode\u003epip install requirements.txt\u003c/code\u003e to CMD(or relevant)\u003c/li\u003e\n\u003cli\u003eWrite \u003ccode\u003estreamlit run app.py\u003c/code\u003e when you are in \u003ci\u003esrc/Planets/components\u003c/i\u003e to CMD(or relevant)\u003c/li\u003e\n\u003c/lu\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdoguhannilt%2Fplanets-demo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdoguhannilt%2Fplanets-demo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdoguhannilt%2Fplanets-demo/lists"}