{"id":28023793,"url":"https://github.com/akash-srm/user-engagement-analysis","last_synced_at":"2026-04-16T19:43:41.541Z","repository":{"id":291062986,"uuid":"976459992","full_name":"AKASH-SRM/User-Engagement-Analysis","owner":"AKASH-SRM","description":"Analyzed user engagement and feedback data to derive actionable insights for an online learning platform.","archived":false,"fork":false,"pushed_at":"2025-05-02T06:31:17.000Z","size":231,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-11T01:33:43.867Z","etag":null,"topics":["analytics-projects","data-analysis","data-cleaning","eda","jupyter-notebook","pandas","python","seaborn","student-engagement"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/AKASH-SRM.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":"2025-05-02T06:27:23.000Z","updated_at":"2025-05-02T06:51:55.000Z","dependencies_parsed_at":"2025-05-02T07:41:41.389Z","dependency_job_id":"0768966d-af13-43c1-ba00-d31503502a45","html_url":"https://github.com/AKASH-SRM/User-Engagement-Analysis","commit_stats":null,"previous_names":["akash-srm/user-engagement-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AKASH-SRM/User-Engagement-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AKASH-SRM%2FUser-Engagement-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AKASH-SRM%2FUser-Engagement-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AKASH-SRM%2FUser-Engagement-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AKASH-SRM%2FUser-Engagement-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AKASH-SRM","download_url":"https://codeload.github.com/AKASH-SRM/User-Engagement-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AKASH-SRM%2FUser-Engagement-Analysis/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":262667270,"owners_count":23345526,"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":["analytics-projects","data-analysis","data-cleaning","eda","jupyter-notebook","pandas","python","seaborn","student-engagement"],"created_at":"2025-05-11T01:27:47.572Z","updated_at":"2026-04-16T19:43:41.487Z","avatar_url":"https://github.com/AKASH-SRM.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# User Engagement Analysis on an Online Learning Platform\n\nThis project focuses on analyzing student behavior, engagement levels, and feedback data to uncover insights and suggest improvements for an online learning platform.\n\n## 📁 Dataset Overview\nThe analysis is based on three CSV files:\n- **students.csv**: Contains student demographics and enrolment details.\n- **course_activity.csv**: Logs time spent and completion rates for each student-course interaction.\n- **feedback.csv**: Includes student ratings and written feedback per course.\n\n## 🛠️ Tools \u0026 Libraries\n- Python  \n- Pandas, NumPy  \n- Matplotlib, Seaborn  \n- Jupyter Notebook  \n\n## 🧹 Data Cleaning\n- Handled missing and inconsistent values  \n- Converted date columns to datetime format  \n- Removed duplicates and standardized column types\n\n## 📊 Exploratory Analysis\nKey questions explored:\n- Average course completion rate  \n- Courses with highest and lowest engagement  \n- Engagement trends by age group and location  \n- Course-wise feedback ratings  \n- Correlation between completion rate and satisfaction  \n\n## 📈 Visualizations\n- Bar charts for average time spent per course  \n- Scatter plots to analyze engagement across age groups  \n- Line plots showing monthly activity and completion trends  \n- Course-wise rating comparisons  \n\n## 🧠 Key Insights\n1. Some age groups showed lower completion despite high login time.  \n2. Location-based differences in course engagement were evident.  \n3. Courses with higher ratings generally had better completion rates.  \n4. A few courses had high drop-off after 30% completion — signaling content gaps.  \n5. Engagement time strongly correlated with overall satisfaction.\n\n## ✅ Recommendations\n- Optimize low-performing courses with high drop rates  \n- Personalize engagement strategies by age and region  \n- Use feedback sentiment analysis to improve course structure\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakash-srm%2Fuser-engagement-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakash-srm%2Fuser-engagement-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakash-srm%2Fuser-engagement-analysis/lists"}