{"id":19709086,"url":"https://github.com/cmoussa1/phys243-project","last_synced_at":"2025-06-21T12:38:52.424Z","repository":{"id":185143045,"uuid":"665226469","full_name":"cmoussa1/phys243-project","owner":"cmoussa1","description":"a repository to hold the contents for our group PHYS243 project","archived":false,"fork":false,"pushed_at":"2023-08-23T22:00:10.000Z","size":7236,"stargazers_count":0,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-27T18:31:50.880Z","etag":null,"topics":[],"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/cmoussa1.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":"2023-07-11T18:13:53.000Z","updated_at":"2023-08-02T16:59:49.000Z","dependencies_parsed_at":null,"dependency_job_id":"d95c3e1f-f6a9-4d77-84c9-58b6b0039ecf","html_url":"https://github.com/cmoussa1/phys243-project","commit_stats":null,"previous_names":["cmoussa1/phys243-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/cmoussa1/phys243-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmoussa1%2Fphys243-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmoussa1%2Fphys243-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmoussa1%2Fphys243-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmoussa1%2Fphys243-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cmoussa1","download_url":"https://codeload.github.com/cmoussa1/phys243-project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cmoussa1%2Fphys243-project/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261126198,"owners_count":23113286,"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":[],"created_at":"2024-11-11T21:46:17.098Z","updated_at":"2025-06-21T12:38:47.410Z","avatar_url":"https://github.com/cmoussa1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## House Improvement ROI Prediction Model\n\n#### Group 4\n#### James Siefert, Liam Hsieh, Panawannage Fernando, and Christopher Moussa\n\n---\n\n### Background\n\nThe housing market plays a crucial role in today’s national economy,\nrepresenting one of the most significant sectors in terms of financial\ntransactions and investment opportunities. The valuation and prediction of\nhousing prices have garnered significant attention from researchers,\npolicymakers, and real estate professionals, as it directly impacts numerous\nstakeholders, including homeowners, buyers, sellers, investors, and government\ninstitutions.\n\nBeing able to predict housing prices based on a number of factors using\nhistorical data can effectively capture intricate relationships and identify\ncrucical factors influencing housing prices. In this project, we separate and\ndefine these factors into two categories: **changeable features** (square\nfootage, number of bedrooms and bathrooms, number of stories, presence of a\nguest room, central air conditioning, and furnishing status) and\n**non-changeable features** (proximity to a main road, presence of a basement,\nhot water heating, parking spaces, and preferred area status). These factors,\nwhen analyzed, can help buyers make informed decisions.\n\n### Implementation\n\nThis project looks to investigate the value increase associated with a number\nof home improvement projects to help homeowners maximize their **return on**\n**investment (ROI)**. It uses a [Kaggle](https://www.kaggle.com/datasets/harishkumardatalab/housing-price-prediction)\ndataset along with a number of trusted articles to guage values and other\nnecessary information regarding home features and their relation to overall\nhome price.\n\nThis project is broken into multiple phases: module creation, model training,\nmodel evaluation and refinement, and ROI calculation and integration. Each phase\nbuilds off of the previous in the Jupyter notebook to describe the machine learning\nprocess for the housing dataset, extensions of the data provided in the dataset\nthrough the creation of Python modules, and web appplication interfaces that link\nthe trained model with the Python modules.\n\n### Goals\n\nThe goal of this project is to extend beyond the basic machine learning process\nwith sample data and challenge ourselves to think about an extensible problem.\nWhile this project takes a number of steps past creating a linear regression model\nfor the Kaggle dataset, it can be extended and advanced with the addition of more\nparameters and more data and training.\n\n### How to Run the Web UI locally\n* Step 1: Go to `web_app/` -\u003e `web_app.py`\n* Step 2: Run `web_app.py`\n* Step 3: Go to `http://127.0.0.1:5000` in your web browser\n* Step 4: Alternate: Go into the `project.ipynb` file to view the application after `web_app.py` is running\n* Step 5: Alternate 2: Contact James Siefert (james.siefert@email.ucr.edu) and I can run a local server and send a URL for easy access.\n\n### How to Run the Web UI locally in a virtual environment in Windows and Python 3.10.9\n* Step 1: open command prompt (\"cmd\" in search bar)\n* Step 2: ensure you are using `3.10.9` by running `python --version`\n* Step 3: navigate to project directory with `cd \u003cproject folder path\u003e`\n* Step 4: run `python -m venv venv`\n* Step 5: run `venv\\Scripts\\activate`\n* Step 6: install requirements: `pip install -r requirements.txt`\n* Step 7: change directories to the `web_app/` directory: `cd web_app`\n* Step 8: run the web app: `python web_app.py`\n* Step 9: Go to `http://127.0.0.1:5000` in your web browser\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmoussa1%2Fphys243-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcmoussa1%2Fphys243-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcmoussa1%2Fphys243-project/lists"}