{"id":27697884,"url":"https://github.com/busradeveci/student-performance-prediction","last_synced_at":"2025-04-25T16:21:43.066Z","repository":{"id":289573982,"uuid":"971707518","full_name":"Busradeveci/student-performance-prediction","owner":"Busradeveci","description":"A machine learning project to predict student exam performance based on academic, social, and personal features. Built with Python and scikit-learn.","archived":false,"fork":false,"pushed_at":"2025-04-24T00:08:43.000Z","size":9,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-24T01:20:36.757Z","etag":null,"topics":["data-analysis","kaggle","linear-regression","machine-learning","predictive-modeling","python","scikit-learn","student-performance"],"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/Busradeveci.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,"zenodo":null}},"created_at":"2025-04-24T00:04:28.000Z","updated_at":"2025-04-24T00:10:42.000Z","dependencies_parsed_at":"2025-04-24T01:33:05.814Z","dependency_job_id":null,"html_url":"https://github.com/Busradeveci/student-performance-prediction","commit_stats":null,"previous_names":["busradeveci/student-performance-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Busradeveci%2Fstudent-performance-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Busradeveci%2Fstudent-performance-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Busradeveci%2Fstudent-performance-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Busradeveci%2Fstudent-performance-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Busradeveci","download_url":"https://codeload.github.com/Busradeveci/student-performance-prediction/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250850393,"owners_count":21497499,"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","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-analysis","kaggle","linear-regression","machine-learning","predictive-modeling","python","scikit-learn","student-performance"],"created_at":"2025-04-25T16:21:42.421Z","updated_at":"2025-04-25T16:21:43.056Z","avatar_url":"https://github.com/Busradeveci.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Student Exam Score Prediction 📚\n\nThis project aims to predict students' exam scores based on various academic, social, and personal features using a machine learning regression model.\n\n## 📌 Dataset\nThe dataset includes features such as:\n- Hours Studied\n- Attendance\n- Sleep Hours\n- Previous Scores\n- Family Income\n- Internet Access\n- Extracurricular Activities\n- Motivation Level\n- and more...\n\n## 🧠 Model\nA **Linear Regression** model was used for prediction.  \nModel was trained on encoded and normalized data.\n\n## ⚙️ Tools \u0026 Libraries\n- Python 🐍\n- Pandas\n- NumPy\n- Scikit-learn\n- Matplotlib / Seaborn (optional visualization)\n\n## 📈 Results\nPerformance Metrics:\n- **Mean Absolute Error (MAE):** 0.45\n- **Mean Squared Error (MSE):** 3.25\n- **R² Score:** 0.77\n\nThe model explains approximately 77% of the variance in the exam scores.\n\n## 📁 File Structure\n- `student-exam-score-prediction-model.ipynb` → Main notebook with all ML steps\n- `README.md` → Project description\n\n## ✨ Future Work\n- Try other models: RandomForest, XGBoost\n- Feature importance analysis\n- Hyperparameter tuning\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusradeveci%2Fstudent-performance-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbusradeveci%2Fstudent-performance-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusradeveci%2Fstudent-performance-prediction/lists"}