{"id":24605981,"url":"https://github.com/udacity-machinelearning-internship/decision-trees-in-sklearn","last_synced_at":"2026-05-19T07:13:00.115Z","repository":{"id":239881175,"uuid":"800878638","full_name":"Udacity-MachineLearning-Internship/Decision-Trees-in-SKLearn","owner":"Udacity-MachineLearning-Internship","description":"Applying Decision Trees in SKLearn with pandas and numpy","archived":false,"fork":false,"pushed_at":"2024-05-17T03:38:34.000Z","size":11,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-21T08:24:10.252Z","etag":null,"topics":["decision-trees","machine-learning","sklearn"],"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":"2024-05-15T07:08:52.000Z","updated_at":"2024-12-17T20:46:10.000Z","dependencies_parsed_at":"2025-01-21T08:34:36.701Z","dependency_job_id":null,"html_url":"https://github.com/Udacity-MachineLearning-Internship/Decision-Trees-in-SKLearn","commit_stats":null,"previous_names":["barasedih11/decision-trees-in-sklearn","udacity-machinelearning-internship/decision-trees-in-sklearn"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FDecision-Trees-in-SKLearn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FDecision-Trees-in-SKLearn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FDecision-Trees-in-SKLearn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Udacity-MachineLearning-Internship%2FDecision-Trees-in-SKLearn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Udacity-MachineLearning-Internship","download_url":"https://codeload.github.com/Udacity-MachineLearning-Internship/Decision-Trees-in-SKLearn/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244206647,"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":["decision-trees","machine-learning","sklearn"],"created_at":"2025-01-24T16:50:01.307Z","updated_at":"2025-10-19T06:03:03.507Z","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![Decision_Trees_in_SKLearn](https://github.com/BaraSedih11/Decision-Trees-in-SKLearn/assets/98843912/dc0a34bc-66ff-45e4-92cd-5f92108a06f3)\n\n   ![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/Decision-Trees-in-SKLearn) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/Decision-Trees-in-SKLearn) [![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/Decision-Trees-in-SKLearn/main)\n[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/Decision-Trees-in-SKLearn/releases/tag/v1.0.0)\n[![Contributors](https://img.shields.io/github/contributors/BaraSedih11/Decision-Trees-in-SKLearn)](https://github.com/BaraSedih11/Decision-Trees-in-SKLearn/graphs/contributors)\n![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/BaraSedih11/Decision-Trees-in-SKLearn)\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 decision trees using sckit-learn.\n\n## Overview\n\nMultiple linear regression is an extension of simple linear regression, where the relationship between a dependent variable and two or more independent variables is modeled. It assumes a linear relationship between the input variables (features) and the output variable (target), allowing for more complex modeling scenarios.\n\nIn this repository, we demonstrate how to perform multiple linear regression using Python. We utilize libraries such as NumPy, pandas, and scikit-learn to implement the decision trees model. Additionally, we provide a simple example along with explanations to help you understand how to apply decision trees to your own datasets.\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\nYou can install these libraries using pip:\n\n```bash\npip install numpy pandas scikit-learn \n\n```\n\n## Usage\n\n1. Clone this repository to your local machine:\n\n```bash\ngit clone https://github.com/BaraSedih11/Decision-Trees-in-SKLearn.git\n```\n\n2. Navigate to the repository directory:\n\n```bash\ncd DecisionTreesinSKLearn\n```\n\n3. Open and run the Jupyter Notebook `DecisionTreesinSKLearn.ipynb` using Jupyter Notebook or JupyterLab.\n\n4. Follow along with the code and comments in the notebook to understand how decision trees 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudacity-machinelearning-internship%2Fdecision-trees-in-sklearn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fudacity-machinelearning-internship%2Fdecision-trees-in-sklearn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fudacity-machinelearning-internship%2Fdecision-trees-in-sklearn/lists"}