{"id":18351963,"url":"https://github.com/nickenshidqia/predict_gpa_from_sat_score_using_linear_regression","last_synced_at":"2026-04-26T22:31:42.226Z","repository":{"id":213104121,"uuid":"733034322","full_name":"nickenshidqia/Predict_GPA_From_SAT_Score_Using_Linear_Regression","owner":"nickenshidqia","description":"Build a machine learning model that can predict GPA from SAT Scores to evaluate the potential academic success of applicants.","archived":false,"fork":false,"pushed_at":"2023-12-18T14:14:20.000Z","size":1289,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-10T00:46:29.775Z","etag":null,"topics":["data-science","gpa","linear-regression","logistic-regression","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nickenshidqia.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2023-12-18T12:16:23.000Z","updated_at":"2023-12-18T14:17:01.000Z","dependencies_parsed_at":"2024-11-05T21:38:36.910Z","dependency_job_id":null,"html_url":"https://github.com/nickenshidqia/Predict_GPA_From_SAT_Score_Using_Linear_Regression","commit_stats":null,"previous_names":["nickenshidqia/predict_gpa_from_sat_score_using_linear_regression"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nickenshidqia/Predict_GPA_From_SAT_Score_Using_Linear_Regression","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickenshidqia%2FPredict_GPA_From_SAT_Score_Using_Linear_Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickenshidqia%2FPredict_GPA_From_SAT_Score_Using_Linear_Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickenshidqia%2FPredict_GPA_From_SAT_Score_Using_Linear_Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickenshidqia%2FPredict_GPA_From_SAT_Score_Using_Linear_Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nickenshidqia","download_url":"https://codeload.github.com/nickenshidqia/Predict_GPA_From_SAT_Score_Using_Linear_Regression/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nickenshidqia%2FPredict_GPA_From_SAT_Score_Using_Linear_Regression/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32315711,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T21:09:39.134Z","status":"ssl_error","status_checked_at":"2026-04-26T21:09:21.240Z","response_time":129,"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":["data-science","gpa","linear-regression","logistic-regression","machine-learning","python"],"created_at":"2024-11-05T21:33:50.437Z","updated_at":"2026-04-26T22:31:42.199Z","avatar_url":"https://github.com/nickenshidqia.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Machine Learning Project Using Linear \u0026 Logistic Regression to Predict GPA from SAT Scores\r\n\r\n## Project Description\r\n\r\n**Problem :**  \r\nUnderstanding the relationship between standardized test scores and academic performance is essential for educational institutions to make informed admission decisions. By leveraging historical data, the goal is to create a tool that assists admission offices in evaluating the potential academic success of applicants and provides valuable insights into the predictive power of SAT scores.\r\n\r\n**Challenges :**  \r\nBuild a machine learning model that can predict GPA from SAT Scores\r\n\r\n## Project Goal\r\n\r\nThis project aims to develop a predictive model that can estimate a student's GPA based on their SAT scores.\r\n\r\n## Tools \u0026 Library Used\r\n\r\n[\u003cimg src=\"./image/python-logo-2.png\" alt=\"python-logo\" width=\"50\"/\u003e](https://www.python.org/) \u0026nbsp;\r\n[\u003cimg src=\"./image/jupyter-logo.png\" alt=\"jupyter-logo\" width=\"50\"/\u003e](https://jupyter.org/) \u0026nbsp;\r\n\r\n## Project Result\r\n\r\n[Click here to get full code](https://github.com/nickenshidqia/Predict_GPA_From_SAT_Score_Using_Linear_Regression/blob/74f423e3073470eee6465db06c9d6559230dd5b1/GPA%20%26%20SAT.ipynb)\r\n\r\n### Dataset\r\n\r\n\u003cimg src=\"./image/data_gpa2.png\" alt=\"\" width = \"500\"/\u003e\r\n\r\n- There are 84 students who have studied in college\r\n- SAT Score = Critical reading + Mathematics + Writing\r\n- GPA = Grade Point Average (at graduation from university)\r\n\r\n### Linear Regression\r\n\r\n#### GPA based on SAT Score\r\n\r\n\u003cimg src=\"./image/scatter1.jpg\" alt=\"\" width = \"350\"/\u003e  \r\n  \r\n- That is the best fitting line, or the line which is closest to all observation simultaneously\r\n- Example if there is student who has SAT score 1700, then he will got GPA 3.165\r\n- There is strong relationship between SAT and GPA\r\n- The higher the SAT of a student, the higher their GPA\r\n\r\n#### GPA based on SAT \u0026 Attendance\r\n\r\n\u003cimg src=\"./image/scatter2.jpg\" alt=\"\" width = \"350\"/\u003e\r\n\r\n- From this dataset, we found that average of students attendance more than 75% of lectures is only 46.42% have attended. Mean \u003c 0.5 shows that there are more 0s than 1s.\r\n- On average the GPA of those who attendeded is higher than the one didn't attend the class.\r\n\r\n#### Making Predictions\r\n\r\n**Prediction 1**  \r\nCreate prediction of 2 students, whose the one that get higher GPA :\r\n\r\n- Budi, who got 1700 on SAT and did not attend \u003e75% of lecturers\r\n- Ani, who got 1670 on SAT and attended \u003e75% of lecturers\r\n\r\n\u003cimg src=\"./image/data_scatter.jpg\" alt=\"\" width = \"300\"/\u003e\r\n\r\n- The predicted GPA at graduation for Budi is 3.02\r\n- The predicted GPA at graduation for Ani is 3.20\r\n- Ani scored lower on SAT, but she attended \u003e 75% of lectures, and she is predicted to graduate with a significantly higher GPA than Budi.\r\n\r\n**Prediction 2**  \r\nCreate prediction of GPA for SAT score 1740 and 1760 :  \r\n\u003cimg src=\"./image/data_Scatter2.jpg\" alt=\"\" width = \"200\"/\u003e\r\n\r\n- The predicted GPA for SAT score 1740 = 3.155938\r\n- The predicted GPA for SAT score 1760 = 3.189051\r\n- The higher SAT score, the higher GPA score\r\n\r\n### Logistic Regression\r\n\r\n#### Predicting whether student will be admitted or not\r\n\r\n\u003cimg src=\"./image/scatter3.jpg\" alt=\"\" width = \"400\"/\u003e\r\n\r\n- This function shows the probability of admission given an SAT score\r\n- When SAT score is relatively low, the probability of getting admitted is 0%\r\n- When SAT score is relatively high, the probability of getting admitted is 100%\r\n- Score between 1,600 and 1,750 is uncertain\r\n- SAT score 1,650, the students roughly 50% chance of getting in\r\n- SAT score 1,700, the students got 80% chance of getting in\r\n\r\n#### Predicting which gender will be the most admitted\r\n\r\n\u003cimg src=\"./image/data_gpa3.jpg\" alt=\"\" width = \"400\"/\u003e  \r\n\u003cimg src=\"./image/data_gpa4.jpg\" alt=\"\" width = \"300\"/\u003e\r\n\r\n- odds of female to get admitted are 6.99 times odds of male\r\n- given the same SAT score, a female has 7 times higher odds to get admitted than the male\r\n- in this particular university (degree), it is much easier for females to enter\r\n- example communications, most of them are female, while STEM predominantly male\r\n\r\n### Accuracy\r\n\r\n\u003cimg src=\"./image/data_gpa5.jpg\" alt=\"\" width = \"400\"/\u003e\r\n\r\n- The accuracy of our model is 94.64%. Our model seems good at classifying\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnickenshidqia%2Fpredict_gpa_from_sat_score_using_linear_regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnickenshidqia%2Fpredict_gpa_from_sat_score_using_linear_regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnickenshidqia%2Fpredict_gpa_from_sat_score_using_linear_regression/lists"}