{"id":16576245,"url":"https://github.com/ruturaj4/major_leagues","last_synced_at":"2026-06-19T01:31:22.115Z","repository":{"id":155037326,"uuid":"222181164","full_name":"Ruturaj4/major_leagues","owner":"Ruturaj4","description":"Soccer scores","archived":false,"fork":false,"pushed_at":"2019-11-18T04:46:33.000Z","size":72,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-09-07T11:49:49.518Z","etag":null,"topics":["data-science","jupyter-notebook","linear-regression","prediction-model","regression","sklearn"],"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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ruturaj4.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":"2019-11-17T01:23:15.000Z","updated_at":"2019-11-18T04:46:35.000Z","dependencies_parsed_at":null,"dependency_job_id":"3e4ccbf0-eaeb-4dc1-9ae5-2d9d390ace43","html_url":"https://github.com/Ruturaj4/major_leagues","commit_stats":{"total_commits":5,"total_committers":1,"mean_commits":5.0,"dds":0.0,"last_synced_commit":"9039fa998625b30d59ae5ed1f05447bab0f37756"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Ruturaj4/major_leagues","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ruturaj4%2Fmajor_leagues","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ruturaj4%2Fmajor_leagues/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ruturaj4%2Fmajor_leagues/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ruturaj4%2Fmajor_leagues/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ruturaj4","download_url":"https://codeload.github.com/Ruturaj4/major_leagues/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ruturaj4%2Fmajor_leagues/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34514282,"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":"2026-06-18T02:00:06.871Z","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","jupyter-notebook","linear-regression","prediction-model","regression","sklearn"],"created_at":"2024-10-11T22:07:39.128Z","updated_at":"2026-06-19T01:31:22.064Z","avatar_url":"https://github.com/Ruturaj4.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Major Leagues\n==============================\n\nSoccer scores\n\nAuthor: Ruturaj Kiran Vaidya\n\nProject Organization\n------------\n\n    ├── LICENSE\n    ├── README.md          \u003c- The top-level README for developers using this project.\n    ├── data\n    │   ├── external       \u003c- Data from third party sources.\n    │   ├── interim        \u003c- Intermediate data that has been transformed.\n    │   ├── processed      \u003c- The final, canonical data sets for modeling.\n    │   └── raw            \u003c- The original, immutable data dump.\n    │\n    ├── notebooks          \u003c- Jupyter notebooks. Naming convention is a number (for ordering),\n    │                         the creator's initials, and a short `-` delimited description, e.g.\n    │                         `1.0-jqp-initial-data-exploration`.\n    │\n    ├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n    │   └── figures        \u003c- Generated graphics and figures to be used in reporting\n\n--------\n\n\u003cp\u003e\u003csmall\u003eProject based on the \u003ca target=\"_blank\" href=\"https://drivendata.github.io/cookiecutter-data-science/\"\u003ecookiecutter data science project template\u003c/a\u003e. #cookiecutterdatascience\u003c/small\u003e\u003c/p\u003e\n\n### Before Getting Stated\n\nIf you are only interested in looking at the notebook then go to (There are notebook rendering problems in github ecosystem):\n\nhttps://nbviewer.jupyter.org/github/Ruturaj4/major_leagues/blob/master/notebooks/1.0-rkv-major-leagues.ipynb\n\nAll the graphs are plotted using `matplotlib`.\n\n### Aim\n\u003cul\u003e\n\u003cli\u003ePick datasets from various leagues like NFL, MLB, NBA and Soccer\u003c/li\u003e\n\u003cli\u003eObtain additional value using exploratory data analysis, by formulating ideas using feature engineering\u003c/li\u003e\n\u003cli\u003eUsing regression algorithms to determine scores of each team\u003c/li\u003e\n\u003c/ul\u003e\n\n### Dataset used\n\nI used soccer dataset.\n\nDataset: https://github.com/fivethirtyeight/data/tree/master/soccer-spi\n\nSpecific Dataset Link: https://projects.fivethirtyeight.com/soccer-api/club/spi_matches.csv\n\n### Discussion\n\nFirst, I imported the soccer dataset, as soccer interests me the most. I was unaware of what some of the features mean, particularly Xg and NsXg. With some research, I discovered that Xg belongs to \"Expected goals\" and NsXg belongs to \"Non shot Xg\". It was interesting to find this information. Second, for feature engineering, I split \"date\" into years, months and days. Next, for training, I used linear regression model from sklearn module. The actual and predicted results for score1 and score2 (scores for each team) are as follows:\n\n![alt text](/reports/figures/score1.png)\n![alt text](/reports/figures/score2.png)\n\nI think that the prediction algorithm is quite nice. Lastly, I also calculated \"Mean Absolute Error\", \"Mean Squared Error\" and \"Root Mean Squared Error\" for the scores for each team. These results can be seen in the Jupyter Notebook.\n\nMy reason for splitting date:\n\nI think that each team may change their form based on year, month or even season. I belive that, in future a column or feature which defines season, may be added, as that would make a lot more sense.\n\nPlot and error calculation reference: https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f.\n\n### License\n\n\u003cb\u003eMIT\u003c/b\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruturaj4%2Fmajor_leagues","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fruturaj4%2Fmajor_leagues","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fruturaj4%2Fmajor_leagues/lists"}