{"id":24605962,"url":"https://github.com/udacity-machinelearning-internship/feature-scaling","last_synced_at":"2025-03-18T10:41:07.950Z","repository":{"id":239873658,"uuid":"800855534","full_name":"Udacity-MachineLearning-Internship/Feature-Scaling","owner":"Udacity-MachineLearning-Internship","description":"Applying feature scaling with linear regression in python","archived":false,"fork":false,"pushed_at":"2024-05-17T03:40:45.000Z","size":13,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-21T08:24:10.231Z","etag":null,"topics":["feature-scaling","linear-regression","machine-learning","sckit-learn"],"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/Udacity-MachineLearning-Internship.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":"2024-05-15T06:07:41.000Z","updated_at":"2024-12-17T20:45:33.000Z","dependencies_parsed_at":"2024-05-17T04:34:20.443Z","dependency_job_id":"c576b099-f420-4a3a-9aac-57cd26d4b559","html_url":"https://github.com/Udacity-MachineLearning-Internship/Feature-Scaling","commit_stats":null,"previous_names":["barasedih11/feature-scaling","udacity-machinelearning-internship/feature-scaling"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FFeature-Scaling","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FFeature-Scaling/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FFeature-Scaling/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FFeature-Scaling/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Udacity-MachineLearning-Internship","download_url":"https://codeload.github.com/Udacity-MachineLearning-Internship/Feature-Scaling/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244206605,"owners_count":20416086,"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":["feature-scaling","linear-regression","machine-learning","sckit-learn"],"created_at":"2025-01-24T16:49:58.768Z","updated_at":"2025-03-18T10:41:07.891Z","avatar_url":"https://github.com/Udacity-MachineLearning-Internship.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv style=\"display:flex; justify-content: center; align-items: center ; height\" 100vh\" align=center\u003e\n\n![Feature_Scaling](https://github.com/BaraSedih11/Feature-Scaling/assets/98843912/b4c173e1-b139-4032-a2ca-018ee9b06ba0)\n\n   ![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/Feature-Scaling) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/Feature-Scaling) [![Python Version](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/downloads/release/python-380/)\n[![Pip Version](https://img.shields.io/badge/pip-21.0-orange)](https://pypi.org/project/pip/21.0/)\n ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/BaraSedih11/Feature-Scaling/main)\n[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/Feature-Scaling/releases/tag/v1.0.0)\n[![Contributors](https://img.shields.io/github/contributors/BaraSedih11/Feature-Scaling)](https://github.com/BaraSedih11/Feature-Scaling/graphs/contributors)\n![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/BaraSedih11/Feature-Scaling)\n\u003c!-- ![GitHub issues](https://img.shields.io/github/issues-raw/BaraSedih11/Bookstore)  --\u003e\n\u003c/div\u003e\n\n\nThis repository contains an implementation of feature scaling in linear regression using Python.\n\n## Overview\n\nIn this exercise, you'll revisit the same dataset as before and see how scaling the features changes which features are favored in a regularization step. The only thing different for this quiz compared to the previous one is the addition of a new step after loading the data, where you will use sklearn's StandardScaler(opens in a new tab) to standardize the data before you fit a linear regression model to the data with L1 (Lasso) regularization.\n\n## Requirements\n\nTo run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:\n\n- NumPy\n- pandas\n- scikit-learn\n- matplotlib\n\nYou can install these libraries using pip:\n\n```bash\npip install numpy pandas scikit-learn matplotlib\n\n```\n\n## Usage\n\n1. Clone this repository to your local machine:\n\n```bash\ngit clone https://github.com/BaraSedih11/Feature-Scaling.git\n```\n\n2. Navigate to the repository directory:\n\n```bash\ncd Feature-Scaling\n```\n\n3. Open and run the Jupyter Notebook `Feature Scaling.ipynb` using Jupyter Notebook or JupyterLab.\n\n4. Follow along with the code and comments in the notebook to understand how feature scaling in linear regression is implemented using Python.\n\n\n## Acknowledgements\n\n- [scikit-learn](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.\n- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.\n- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.\n- [matplotlib](https://matplotlib.org/): The matplotlib library for data visualization in Python.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudacity-machinelearning-internship%2Ffeature-scaling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fudacity-machinelearning-internship%2Ffeature-scaling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudacity-machinelearning-internship%2Ffeature-scaling/lists"}