{"id":25483590,"url":"https://github.com/brendandasilva/applieddatascience","last_synced_at":"2026-04-27T16:32:25.788Z","repository":{"id":276818507,"uuid":"889180634","full_name":"BrendanDasilva/AppliedDataScience","owner":"BrendanDasilva","description":"4 projects from Applied Data Science ","archived":false,"fork":false,"pushed_at":"2025-02-10T15:40:33.000Z","size":1044,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-07T05:01:57.837Z","etag":null,"topics":["data-science","numpy","pandas","python"],"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/BrendanDasilva.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":"2024-11-15T19:11:32.000Z","updated_at":"2025-02-10T15:40:36.000Z","dependencies_parsed_at":"2025-02-10T16:45:33.376Z","dependency_job_id":null,"html_url":"https://github.com/BrendanDasilva/AppliedDataScience","commit_stats":null,"previous_names":["brendandasilva/applieddatascience"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BrendanDasilva/AppliedDataScience","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BrendanDasilva%2FAppliedDataScience","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BrendanDasilva%2FAppliedDataScience/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BrendanDasilva%2FAppliedDataScience/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BrendanDasilva%2FAppliedDataScience/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BrendanDasilva","download_url":"https://codeload.github.com/BrendanDasilva/AppliedDataScience/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BrendanDasilva%2FAppliedDataScience/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32345802,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-26T23:26:28.701Z","status":"online","status_checked_at":"2026-04-27T02:00:06.769Z","response_time":128,"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-science","numpy","pandas","python"],"created_at":"2025-02-18T17:37:26.700Z","updated_at":"2026-04-27T16:32:25.771Z","avatar_url":"https://github.com/BrendanDasilva.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Applied Data Science\n\nThis repository contains assignments from my Applied Data Science coursework. Each assignment explores different aspects of data analysis, machine learning, and visualization using Python.\n\n## Assignments\n\n### 1. Working with NumPy and Matplotlib\nThis assignment covers fundamental operations in NumPy for numerical computing and visualization using Matplotlib.\n\n📺 **Video Walkthrough:** [YouTube Link](https://youtu.be/OxPikMOtPuY)\n\n### 2. Using a Decision Tree Model in SKLearn\nImplementation of a decision tree classifier using the `sklearn` library, covering data preprocessing, model training, and evaluation.\n\n📺 **Video Walkthrough:** [YouTube Link](https://youtu.be/0dGOBe4qfzQ)\n\n### 3. House Prices - Advanced Regression Techniques\nParticipation in the Kaggle competition \"House Prices - Advanced Regression Techniques,\" utilizing regression models to predict house prices.\n\n📺 **Video Walkthrough:** [YouTube Link](https://youtu.be/-jZjGtT4hqQ)\n\n### 4. Pandas Library\nExploring the Pandas library for data manipulation, including data cleaning, aggregation, and transformation techniques.\n\n📺 **Video Walkthrough:** [YouTube Link](https://youtu.be/09iK47ARspg)\n\n---\n\n### 🔧 Technologies Used\n- Python\n- NumPy\n- Pandas\n- Matplotlib\n- Scikit-Learn\n\nFeel free to explore the repository and check out the linked videos for detailed explanations! 🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrendandasilva%2Fapplieddatascience","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbrendandasilva%2Fapplieddatascience","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbrendandasilva%2Fapplieddatascience/lists"}