{"id":17953397,"url":"https://github.com/eloyekunle/student_intervention","last_synced_at":"2025-06-11T03:12:37.624Z","repository":{"id":128581925,"uuid":"68619899","full_name":"eloyekunle/student_intervention","owner":"eloyekunle","description":"Machine learning model to analyze students' performance and predict success likelihood.","archived":false,"fork":false,"pushed_at":"2016-09-19T16:04:32.000Z","size":24,"stargazers_count":8,"open_issues_count":0,"forks_count":3,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-19T05:55:01.094Z","etag":null,"topics":["student-intervention","udacity","udacity-machine-learning-nanodegree","udacity-nanodegree"],"latest_commit_sha":null,"homepage":null,"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/eloyekunle.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":"2016-09-19T15:37:49.000Z","updated_at":"2024-03-25T19:40:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"2a6ce8f9-0267-4316-8bee-2bea95895617","html_url":"https://github.com/eloyekunle/student_intervention","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eloyekunle%2Fstudent_intervention","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eloyekunle%2Fstudent_intervention/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eloyekunle%2Fstudent_intervention/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/eloyekunle%2Fstudent_intervention/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/eloyekunle","download_url":"https://codeload.github.com/eloyekunle/student_intervention/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245377920,"owners_count":20605374,"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":["student-intervention","udacity","udacity-machine-learning-nanodegree","udacity-nanodegree"],"created_at":"2024-10-29T10:05:09.691Z","updated_at":"2025-03-25T00:31:48.066Z","avatar_url":"https://github.com/eloyekunle.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Project 2: Supervised Learning\n## Building a Student Intervention System\n\n### Install\n\nThis project requires **Python 2.7** and the following Python libraries installed:\n\n- [NumPy](http://www.numpy.org/)\n- [Pandas](http://pandas.pydata.org)\n- [scikit-learn](http://scikit-learn.org/stable/)\n\nYou will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html)\n\nUdacity recommends our students install [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains all of the necessary libraries and software for this project. \n\n### Code\n\nTemplate code is provided in the notebook `student_intervention.ipynb` notebook file. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project.\n\n### Run\n\nIn a terminal or command window, navigate to the top-level project directory `student_intervention/` (that contains this README) and run one of the following commands:\n\n```ipython notebook student_intervention.ipynb```  \n```jupyter notebook student_intervention.ipynb```\n\nThis will open the iPython Notebook software and project file in your browser.\n\n## Data\n\nThe dataset used in this project is included as `student-data.csv`. This dataset has the following attributes:\n\n- `school` : student's school (binary: \"GP\" or \"MS\")\n- `sex` : student's sex (binary: \"F\" - female or \"M\" - male)\n- `age` : student's age (numeric: from 15 to 22)\n- `address` : student's home address type (binary: \"U\" - urban or \"R\" - rural)\n- `famsize` : family size (binary: \"LE3\" - less or equal to 3 or \"GT3\" - greater than 3)\n- `Pstatus` : parent's cohabitation status (binary: \"T\" - living together or \"A\" - apart)\n- `Medu` : mother's education (numeric: 0 - none,  1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)\n- `Fedu` : father's education (numeric: 0 - none,  1 - primary education (4th grade), 2 - 5th to 9th grade, 3 - secondary education or 4 - higher education)\n- `Mjob` : mother's job (nominal: \"teacher\", \"health\" care related, civil \"services\" (e.g. administrative or police), \"at_home\" or \"other\")\n- `Fjob` : father's job (nominal: \"teacher\", \"health\" care related, civil \"services\" (e.g. administrative or police), \"at_home\" or \"other\")\n- `reason` : reason to choose this school (nominal: close to \"home\", school \"reputation\", \"course\" preference or \"other\")\n- `guardian` : student's guardian (nominal: \"mother\", \"father\" or \"other\")\n- `traveltime` : home to school travel time (numeric: 1 - \u003c15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - \u003e1 hour)\n- `studytime` : weekly study time (numeric: 1 - \u003c2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - \u003e10 hours)\n- `failures` : number of past class failures (numeric: n if 1\u003c=n\u003c3, else 4)\n- `schoolsup` : extra educational support (binary: yes or no)\n- `famsup` : family educational support (binary: yes or no)\n- `paid` : extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)\n- `activities` : extra-curricular activities (binary: yes or no)\n- `nursery` : attended nursery school (binary: yes or no)\n- `higher` : wants to take higher education (binary: yes or no)\n- `internet` : Internet access at home (binary: yes or no)\n- `romantic` : with a romantic relationship (binary: yes or no)\n- `famrel` : quality of family relationships (numeric: from 1 - very bad to 5 - excellent)\n- `freetime` : free time after school (numeric: from 1 - very low to 5 - very high)\n- `goout` : going out with friends (numeric: from 1 - very low to 5 - very high)\n- `Dalc` : workday alcohol consumption (numeric: from 1 - very low to 5 - very high)\n- `Walc` : weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)\n- `health` : current health status (numeric: from 1 - very bad to 5 - very good)\n- `absences` : number of school absences (numeric: from 0 to 93)\n- `passed` : did the student pass the final exam (binary: yes or no)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feloyekunle%2Fstudent_intervention","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Feloyekunle%2Fstudent_intervention","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Feloyekunle%2Fstudent_intervention/lists"}