{"id":28059748,"url":"https://github.com/n-y-kim/predicttestscoreapp","last_synced_at":"2026-04-29T10:02:51.280Z","repository":{"id":157376567,"uuid":"400160300","full_name":"n-y-kim/PredictTestScoreApp","owner":"n-y-kim","description":"iOS Application for the 'Prediction of test score' with scikit \u0026 coreML","archived":false,"fork":false,"pushed_at":"2021-08-28T09:28:08.000Z","size":4711,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-05-12T08:09:42.501Z","etag":null,"topics":["coreml","data-science","googlecolab","ios","jupyter-notebook","machine-learning","prediction","sklearn","xcode"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data-Science \u0026 Machine Learning(scikit, coreML) to iOS Application\nProject inspired from ['Dongguk Uni X Naver Boostcourse'](https://www.boostcourse.org/study-dongguk-1)\u003cbr\u003e\nPrediction of test score with [dataset](https://www.kaggle.com/kwadwoofosu/predict-test-scores-of-students)\n\n### Application\n\u003cp float=\"left\"\u003e\u003cimg src = \"https://user-images.githubusercontent.com/76769919/131087546-73f9f008-08c2-49d3-9ca3-83577fa627ed.png\" width=\"30%\" height=\"30%\"\u003e\u003cimg src = \"https://user-images.githubusercontent.com/76769919/131087549-b5c5b9ca-23d1-406a-b7cf-e4bbdab9a8d9.png\" width=\"30%\" height=\"30%\"\u003e\u003c/p\u003e\n\n### Data Analysis\n\u003cp float=\"left\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/76769919/131090057-e5190bf8-18a3-41f8-9477-0b8f276cdebd.png\" width=70%\u003e\u003cimg src = \"https://user-images.githubusercontent.com/76769919/131090206-dbfb4da3-3a98-430d-aa9e-4f0a068725ff.png\" width=50%\u003e\u003cimg src =\"https://user-images.githubusercontent.com/76769919/131090420-7ec57819-7d57-489b-9609-5788368c3db1.png\" width=50%\u003e\u003c/p\u003e\n\n\n\u003cbr\u003e\n\nWorking on a application that can predict student's score tests(posttest) by \n  * \u003cb\u003eSchool\u003c/b\u003e\n  * \u003cb\u003eSchool Setting\u003c/b\u003e: Urban / Rural / Suburban\n  * \u003cb\u003eSchool Type\u003c/b\u003e: Public / Non-public\n  * \u003cb\u003eClassroom\u003c/b\u003e\n  * \u003cb\u003eTeaching Method \u003c/b\u003e: Standard / Experimental\n  * \u003cb\u003eNumber of Students\u003c/b\u003e\n  * \u003cb\u003eGender\u003c/b\u003e: Male / Female\n  * \u003cb\u003eLunch\u003c/b\u003e: Qualified / Not qualified\n\n\u003cbr\u003e\n\n## This repository contains\n  * .csv file of the dataset file\n  * .keynote of the project(mostly about the data analysis)\n  * .ipynb file from Google Colab \u0026 Jupyter Notebook\n  * .xcodeproj file of the application\n\u003cbr\u003e\n\n## Beta Version Explanation\n\n👌🏻 Okay and DONE:\n  * Data Analysis of the dataset\n  * used Linear Regression(scikit) to predict posttest scores without pretest in train data. ➡️ 95% score\n  * Prototype of the application(UI)\n  * conversion of model to CoreML(.mlmodel)\n\n❌ NOT OKAY and need UPDATES:\n  * The .ipynb files need to be organized(it may be hard to figure out the purpose of the code. :( This is my bad!!)\n  * The model(scores.mlmodel) show wrong output if the combination of input datas are not from trained data =\u003e how should I fix?\n  * The model inputs are extremely difficult to get their data as the DataFrame went through One-Hot-Encoding\n\u003cbr\u003e\n\n\n## Track Update\nbeta 0.1 version\n\u003cbr\u003e\n\n## Feedbacks \u0026 Contacts\n📮 nyn2265@gmail.com \u003cbr\u003e\n⭐️ issue or discussion\n\u003cbr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fn-y-kim%2Fpredicttestscoreapp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fn-y-kim%2Fpredicttestscoreapp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fn-y-kim%2Fpredicttestscoreapp/lists"}