{"id":20934901,"url":"https://github.com/xmen3em/kaggle-competitions","last_synced_at":"2026-04-09T18:45:46.422Z","repository":{"id":220563183,"uuid":"751968315","full_name":"Xmen3em/Kaggle-Competitions","owner":"Xmen3em","description":"This collection contains various projects and notebooks developed to tackle a range of Kaggle competitions, showcasing different machine learning techniques, data preprocessing methods, and model optimizations.","archived":false,"fork":false,"pushed_at":"2024-08-19T13:15:43.000Z","size":19070,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-13T02:41:20.690Z","etag":null,"topics":["data","data-science","data-visualization","deep-learning","deployment","ensemble-learning","machine-learning-algorithms","python","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Xmen3em.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-02-02T18:12:46.000Z","updated_at":"2024-08-19T13:15:46.000Z","dependencies_parsed_at":"2025-01-19T19:54:24.157Z","dependency_job_id":"95222fc6-e894-4eeb-906d-802e0f55ed1e","html_url":"https://github.com/Xmen3em/Kaggle-Competitions","commit_stats":null,"previous_names":["xmen3em/titanic-competition"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Xmen3em/Kaggle-Competitions","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xmen3em%2FKaggle-Competitions","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xmen3em%2FKaggle-Competitions/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xmen3em%2FKaggle-Competitions/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xmen3em%2FKaggle-Competitions/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Xmen3em","download_url":"https://codeload.github.com/Xmen3em/Kaggle-Competitions/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xmen3em%2FKaggle-Competitions/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28006014,"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":"2025-12-24T02:00:07.193Z","response_time":83,"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":["data","data-science","data-visualization","deep-learning","deployment","ensemble-learning","machine-learning-algorithms","python","streamlit"],"created_at":"2024-11-18T22:11:52.585Z","updated_at":"2025-12-24T18:12:55.618Z","avatar_url":"https://github.com/Xmen3em.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Kaggle Competitions Repository\n\nWelcome to my Kaggle Competitions repository! This collection contains various projects and notebooks developed to tackle a range of Kaggle competitions, showcasing different machine-learning techniques, data preprocessing methods, and model optimizations.\n\n## 📂 Repository Structure\n\nThe repository is organized into folders, each dedicated to a specific Kaggle competition. Inside each folder, you will find:\n\n- **Notebooks**: Jupyter notebooks containing data exploration, preprocessing, model training, evaluation, and predictions.\n- **Datasets**: Links to the datasets used, often hosted on Kaggle.\n- **Models**: Saved models, including various machine learning algorithms like Random Forest, XGBoost, and Neural Networks.\n- **Results**: Visualizations, predictions, and final competition submissions.\n- **Documentation**: Detailed README files explaining the approach taken for each competition, including any unique challenges and solutions.\n\n## 📊 Competitions Covered\n\n- **House Prices - Advanced Regression Techniques**: A deep dive into predicting home prices using regression models, including feature engineering, and model stacking.\n- **Academic Success Prediction**: A Streamlit app developed to predict student academic success, integrating exploratory data analysis, model training, and hyperparameter tuning with a user-friendly interface.\n- **[Other Competitions]**: Various other competitions that involve classification, regression, and deep learning tasks.\n\n## 🚀 How to Use\n\n1. **Clone the repository**:\n   ```bash\n   git clone https://github.com/Xmen3em/Kaggle-Competitions.git\n\n2. Navigate to a specific competition folder:\n```bash\ncd [competition-name]\n```\n3. Run the Jupyter notebooks to explore the data and models.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxmen3em%2Fkaggle-competitions","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxmen3em%2Fkaggle-competitions","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxmen3em%2Fkaggle-competitions/lists"}