{"id":21593618,"url":"https://github.com/Udacity-MachineLearning-Internship/finding_donors","last_synced_at":"2025-07-17T01:31:50.159Z","repository":{"id":241648826,"uuid":"807341662","full_name":"BaraSedih11/finding_donors","owner":"BaraSedih11","description":"First stage project at Udacity on the 'Intro to Machine Learning with TensorFlow' program using sckit-learn in python","archived":false,"fork":false,"pushed_at":"2024-05-28T23:32:13.000Z","size":1146,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-05-29T13:55:15.983Z","etag":null,"topics":["csv","machine-learning","matplotlib","numpy","pandas","python","sckiit-learn","seaborn","sklearn","udacity","udacity-nanodegree"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/BaraSedih11.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":"2024-05-28T23:15:55.000Z","updated_at":"2024-05-29T13:55:19.028Z","dependencies_parsed_at":"2024-05-29T13:55:18.662Z","dependency_job_id":"262b2946-e375-435e-bbb5-ba1f83edde34","html_url":"https://github.com/BaraSedih11/finding_donors","commit_stats":null,"previous_names":["barasedih11/finding_donors"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2Ffinding_donors","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2Ffinding_donors/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2Ffinding_donors/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BaraSedih11%2Ffinding_donors/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BaraSedih11","download_url":"https://codeload.github.com/BaraSedih11/finding_donors/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226193696,"owners_count":17588179,"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":["csv","machine-learning","matplotlib","numpy","pandas","python","sckiit-learn","seaborn","sklearn","udacity","udacity-nanodegree"],"created_at":"2024-11-24T17:13:44.576Z","updated_at":"2025-07-17T01:31:50.146Z","avatar_url":"https://github.com/BaraSedih11.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n\u003cdiv align=center\u003e\n  \n  ![Finding_Donors](https://github.com/BaraSedih11/finding_donors/assets/98843912/600353c1-08fe-4a41-854c-36d896a4952d)\n\n\n   ![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/finding_donors) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/finding_donors) [![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/finding_donors/main)\n[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/finding_donors/releases/tag/v1.0.0)\n[![Contributors](https://img.shields.io/github/contributors/BaraSedih11/finding_donors)](https://github.com/BaraSedih11/finding_donors/graphs/contributors)\n![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/BaraSedih11/finding_donors)\n  \n\u003c/div\u003e\n\nThis repository contains a training and prediction model, along with tuning and testing, to identify the best estimators and features for our dataset.\n\n## Introduction\nWe explored three models and ultimately chose the Random Forest model, which proved to be the most suitable for our dataset. We then fine-tuned the hyperparameters to obtain the best estimators and identified the top 5 features. Finally, we trained a reduced model using these features.\n\n## Contents\n\n- `finding_donors.ipynb`: Jupyter Notebook containing the implementation of Random Forest using Python.\n- `report.html`: An html page presenting the jupyter notebook.\n- `README.md`: This file providing an overview of the repository.\n- `census.csv`: This is the working dataset.\n\n\n## Requirements\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* seaborn\n  \nYou can install these libraries using pip:\n\n```bash\npip install numpy pandas scikit-learn matplotlib seaborn\n```\n\n## Usage\n\n1. Clone this repository to your local machine:\n\n```bash\ngit clone https://github.com/BaraSedih11/finding_donors.git\n```\n\n2. Navigate to the repository directory:\n\n```bash\ncd finding_donors\n```\n\n3. Open and run the Jupyter Notebook `finding_donors.ipynb` using Jupyter Notebook or JupyterLab.\n\n4. Follow along with the code and comments in the notebook to understand how Random Forest and training and tuning 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- [seaborn](https://seaborn.pydata.org/): The seaborn library for data visualization in Python.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FUdacity-MachineLearning-Internship%2Ffinding_donors","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FUdacity-MachineLearning-Internship%2Ffinding_donors","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FUdacity-MachineLearning-Internship%2Ffinding_donors/lists"}