{"id":19493931,"url":"https://github.com/geo-y20/coursera-managment-system","last_synced_at":"2026-06-19T10:32:04.789Z","repository":{"id":240229312,"uuid":"802041960","full_name":"Geo-y20/Coursera-Managment-System","owner":"Geo-y20","description":"ML and Data Science-based recommendation system","archived":false,"fork":false,"pushed_at":"2024-05-17T12:14:01.000Z","size":2673,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-25T20:28:55.609Z","etag":null,"topics":["course","coursera","data","data-science","data-visualization","datacleaning","machine-learning","mean-square-error","recommendation-system"],"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/Geo-y20.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":"2024-05-17T12:06:46.000Z","updated_at":"2024-05-17T12:38:47.000Z","dependencies_parsed_at":"2024-05-17T13:41:15.686Z","dependency_job_id":null,"html_url":"https://github.com/Geo-y20/Coursera-Managment-System","commit_stats":null,"previous_names":["geo-y20/coursera-managment-system"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Geo-y20/Coursera-Managment-System","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Geo-y20%2FCoursera-Managment-System","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Geo-y20%2FCoursera-Managment-System/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Geo-y20%2FCoursera-Managment-System/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Geo-y20%2FCoursera-Managment-System/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Geo-y20","download_url":"https://codeload.github.com/Geo-y20/Coursera-Managment-System/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Geo-y20%2FCoursera-Managment-System/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34528134,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-19T02:00:06.005Z","response_time":61,"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":["course","coursera","data","data-science","data-visualization","datacleaning","machine-learning","mean-square-error","recommendation-system"],"created_at":"2024-11-10T21:27:54.936Z","updated_at":"2026-06-19T10:32:04.762Z","avatar_url":"https://github.com/Geo-y20.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Coursera Course Dataset\n\nThis repository contains the Coursera Course Dataset and the accompanying analysis and recommendation system developed as part of a Data Science project.\n## Link Dataset:\nhttps://www.kaggle.com/datasets/septa97/100k-courseras-course-reviews-dataset \n\n## Project Overview\n\nThis project aims to create an intelligent course recommendation system for learners seeking the right course on Coursera. By analyzing the dataset scrapped from the Coursera website, we can provide personalized course recommendations based on various factors such as course ratings, difficulty levels, and enrollment numbers.\n\n## Dataset Description\n\nThe dataset contains 890 courses with the following columns:\n\n- **course_title**: Title of the course.\n- **course_organization**: The organization offering the course.\n- **certificate_type**: Types of certification available for the course.\n- **course_rating**: Ratings associated with each course.\n- **course_difficulty**: Difficulty level of the course.\n- **course_students_enrolled**: Number of students enrolled in the course.\n\n## Data Cleaning and Preprocessing\n\nThe initial steps in our data analysis include:\n\n1. Deleting the first unnamed column.\n2. Renaming the column \"course_Certificate_type\" to \"certificate_type\".\n3. Removing duplicate values.\n4. Handling missing data (NaN and None values).\n5. Extracting numerical values from the \"course_students_enrolled\" column.\n6. Modifying the \"course_difficulty\" column for further use.\n\n## Exploratory Data Analysis (EDA)\n\nOur EDA process involves:\n\n- Calculating the sum, mean, median, min, and max of the \"course_rating\" column.\n- Plotting a histogram of \"course_rating\".\n- Creating a scatter plot of \"course_rating\" vs \"overall_rating\".\n- Generating a heatmap to visualize correlations between different variables.\n\n## Feature Selection\n\nFor building the recommendation model, we selected the following features:\n\n- **course_rating**\n- **course_students_enrolled_modified**\n- **course_difficulty_modified**\n\n## Model Building and Evaluation\n\nWe developed a linear regression model using the selected features and evaluated its performance using the Mean Absolute Error (MAE) metric.\n\n### Key Metrics\n\n- **Mean Squared Error**: 4.415\n- **Mean Absolute Error**: 4.017\n- **R-Squared**: 1.0\n\n## Recommendations\n\nBased on the model's performance, we recommend the following improvements:\n\n1. **Use more diverse features**: Include additional features such as course duration, language, and topic to improve the model's accuracy.\n2. **Improve data quality**: Ensure the dataset is accurate and up-to-date, with no missing or incorrect values.\n\nWe also recommend courses with ratings over 8.5 as highly beneficial for learners.\n\n## Conclusion\n\nBy implementing these recommendations, we can enhance the performance of the course recommendation system, providing more accurate and personalized suggestions to learners.\n\n## Team Members\n\n- George Youhana\n- Pavli Bahaa\n\n## Supervisors\n\n- Dr. Essam Seddik\n- Eng. Aya Mohamed\n\n## Acknowledgments\n\nWe would like to thank our supervisors and the Arab Academy for Science Technology \u0026 Maritime Transport College of Artificial Intelligence for their support and guidance throughout this project.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeo-y20%2Fcoursera-managment-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeo-y20%2Fcoursera-managment-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeo-y20%2Fcoursera-managment-system/lists"}