{"id":21414919,"url":"https://github.com/alfastrek/future-scholar","last_synced_at":"2026-04-13T01:03:02.704Z","repository":{"id":246712706,"uuid":"821941768","full_name":"Alfastrek/Future-Scholar","owner":"Alfastrek","description":"Data Science Project with live dashboard, analytics and showcasing predictive analytics on student exam performance.","archived":false,"fork":false,"pushed_at":"2024-10-15T19:40:11.000Z","size":1440,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-16T18:46:27.235Z","etag":null,"topics":["analytics","azure","cloud","dashboard","data-science","flask","machine-learning","mlops","python","visualization"],"latest_commit_sha":null,"homepage":"https://scorepredictor.aradhya.site","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/Alfastrek.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":"2024-06-29T21:37:24.000Z","updated_at":"2024-11-26T17:57:39.000Z","dependencies_parsed_at":"2025-01-23T05:25:38.534Z","dependency_job_id":"e8adf1d5-75e2-4124-ab12-cc86b41e67ac","html_url":"https://github.com/Alfastrek/Future-Scholar","commit_stats":null,"previous_names":["alfastrek/mlproject","alfastrek/future-scholar"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Alfastrek/Future-Scholar","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alfastrek%2FFuture-Scholar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alfastrek%2FFuture-Scholar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alfastrek%2FFuture-Scholar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alfastrek%2FFuture-Scholar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Alfastrek","download_url":"https://codeload.github.com/Alfastrek/Future-Scholar/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alfastrek%2FFuture-Scholar/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":272915733,"owners_count":25014662,"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":"2025-08-30T02:00:09.474Z","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":["analytics","azure","cloud","dashboard","data-science","flask","machine-learning","mlops","python","visualization"],"created_at":"2024-11-22T18:34:33.233Z","updated_at":"2026-04-13T01:03:02.659Z","avatar_url":"https://github.com/Alfastrek.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Student Score Predictor - Aradhya Shukla 🧑🏻‍🎓🌟\r\n\r\n### Introduction About the Data :\r\n\r\n#### Dataset: Students Performance in Exams - https://www.kaggle.com/code/aryanml007/students-performance-analysis/input\r\n\r\n## Description\r\n\r\nThis dataset contains information about student performance on exams, including:\r\n\r\n- `gender`: Gender of the student (male/female)\r\n- `ethnicity`: Ethnicity of the student\r\n- `parental_level_of_education`: Highest education level of the student's parents\r\n- `lunch`: Type of lunch the student has (standard or free/reduced)\r\n- `test_preparation_course`: Whether the student completed a test preparation course (completed or none)\r\n- `math_score`: Score obtained in the math exam\r\n- `reading_score`: Score obtained in the reading exam\r\n- `writing_score`: Score obtained in the writing exam\r\n\r\n**Size:** The dataset consists of approximately 1000 rows (students) and 8 columns (features).\r\n\r\n\r\n## Approach for the project \r\n\r\n1. Data Ingestion : \r\n    * In Data Ingestion phase the data is first read as csv. \r\n    * Then the data is split into training and testing and saved as csv file.\r\n\r\n2. Data Transformation : \r\n    * In this phase a ColumnTransformer Pipeline is created.\r\n    * for Numeric Variables first SimpleImputer is applied with strategy median , then Standard Scaling is performed on numeric data.\r\n    * for Categorical Variables SimpleImputer is applied with most frequent strategy, then ordinal encoding performed , after this data is scaled with Standard Scaler.\r\n    * This preprocessor is saved as pickle file.\r\n\r\n3. Model Training : \r\n    * In this phase base model is tested . The best model found was catboost regressor.\r\n    * After this hyperparameter tuning is performed on catboost and knn model.\r\n    * A final VotingRegressor is created which will combine prediction of catboost, xgboost and knn models.\r\n    * This model is saved as pickle file.\r\n\r\n4. Prediction Pipeline : \r\n    * This pipeline converts given data into dataframe and has various functions to load pickle files and predict the final results in python.\r\n\r\n5. Flask App creation : \r\n    * Flask app is created with User Interface to predict the gemst\r\n\r\n## Possible Uses\r\n\r\n- Analyze factors influencing student performance\r\n- Identify areas for improvement in educational programs\r\n- Predict student performance based on various factors\r\n- Gender Performance: Compare performance between male and female students.\r\n- Ethnicity Impact: Assess how ethnicity affects academic results.\r\n- Parental Education: Explore the influence of parents' education levels on student scores.\r\n- Lunch Type: Determine if lunch standard impacts student performance.\r\n- Test Preparation:\u003c/strong\u003e Evaluate the effect of test prep courses on exam scores.\r\n\r\n## Target Variable\r\n\r\n- `math_score`: Score obtained in the math exam\r\n\r\n# AZURE Deployment Link :\r\n\r\n(https://scorepredictor.aradhya.site/)\r\n\r\n# Screenshot of UI\r\n![Screenshot (247)](https://github.com/user-attachments/assets/b5d434cc-bb06-4585-9bd2-f522ca371c7d)\r\n\r\n\r\none prices inside a Web Application.\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falfastrek%2Ffuture-scholar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falfastrek%2Ffuture-scholar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falfastrek%2Ffuture-scholar/lists"}