{"id":19845809,"url":"https://github.com/jesussantana/feature-engineering","last_synced_at":"2026-05-18T19:42:52.744Z","repository":{"id":113635129,"uuid":"362814407","full_name":"jesussantana/Feature-Engineering","owner":"jesussantana","description":"Learn to manage parameters with Python","archived":false,"fork":false,"pushed_at":"2021-06-02T14:24:31.000Z","size":2926,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-08T16:04:38.794Z","etag":null,"topics":["correlation","feature-engineering","normalize","python","scaling"],"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/jesussantana.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":"2021-04-29T12:44:47.000Z","updated_at":"2021-06-21T14:42:30.000Z","dependencies_parsed_at":"2023-03-22T11:53:09.806Z","dependency_job_id":null,"html_url":"https://github.com/jesussantana/Feature-Engineering","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jesussantana/Feature-Engineering","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FFeature-Engineering","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FFeature-Engineering/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FFeature-Engineering/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FFeature-Engineering/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jesussantana","download_url":"https://codeload.github.com/jesussantana/Feature-Engineering/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jesussantana%2FFeature-Engineering/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":271280830,"owners_count":24732081,"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":"2025-08-20T02:00:09.606Z","response_time":69,"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":["correlation","feature-engineering","normalize","python","scaling"],"created_at":"2024-11-12T13:09:21.259Z","updated_at":"2026-05-18T19:42:52.684Z","avatar_url":"https://github.com/jesussantana.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# IT Academy - Data Science with Python\n## Sprint 9: Correlation, Feature Scaling \u0026 Feature Engineering\n\n[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)  \n[![Made withJupyter](https://img.shields.io/badge/Made%20with-Jupyter-orange?style=for-the-badge\u0026logo=Jupyter)](https://jupyter.org/try)  \n[![wakatime](https://wakatime.com/badge/github/jesussantana/Feature-Engineering.svg)](https://wakatime.com/badge/github/jesussantana/Feature-Engineering)  \n\n### Description\n\nLearn to manage parameters with Python.\n\n\n### Level 1\n\n- Exercise 1: \n  - Grab a sports-themed dataset that you like and normalize categorical attributes in dummy. Normalize numeric attributes with StandardScaler.\n  \n### Level 2\n\n- Exercise 2: \n  - Continue with the sports theme data set you like and apply the main component analysis.\n\n### Level 3\n\n- Exercise 3: \n  - Continue with the sports theme data set you like and normalize the data taking into account the outliers.\n\n\n### Targets\n\n- Pre-process the data by performing feature engineering\n- Interpret the different concepts of feature engineering","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesussantana%2Ffeature-engineering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjesussantana%2Ffeature-engineering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjesussantana%2Ffeature-engineering/lists"}