{"id":21683495,"url":"https://github.com/marked01one/rio-airbnb-predictive-model","last_synced_at":"2026-01-07T16:48:00.846Z","repository":{"id":140754242,"uuid":"568297626","full_name":"marked01one/rio-airbnb-predictive-model","owner":"marked01one","description":"Airbnb predictive model code repository for Cal Poly Pomona's RIO research program","archived":false,"fork":false,"pushed_at":"2023-05-05T01:14:59.000Z","size":237695,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-03T23:54:27.396Z","etag":null,"topics":["data-science","machine-learning"],"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/marked01one.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":"2022-11-20T04:10:47.000Z","updated_at":"2023-04-11T15:58:28.000Z","dependencies_parsed_at":"2024-08-27T18:31:14.205Z","dependency_job_id":null,"html_url":"https://github.com/marked01one/rio-airbnb-predictive-model","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marked01one%2Frio-airbnb-predictive-model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marked01one%2Frio-airbnb-predictive-model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marked01one%2Frio-airbnb-predictive-model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/marked01one%2Frio-airbnb-predictive-model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/marked01one","download_url":"https://codeload.github.com/marked01one/rio-airbnb-predictive-model/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246162148,"owners_count":20733357,"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":["data-science","machine-learning"],"created_at":"2024-11-25T16:12:04.316Z","updated_at":"2026-01-07T16:47:55.821Z","avatar_url":"https://github.com/marked01one.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RIO Airbnb Predictive Model\n\n\u003ch3 align='center'\u003e Table of Contents: \u003c/h3\u003e\n\n* [Acknowledgements](#acknowledgments)\n* [Abstract](#abstract)\n* [Getting Started](#getting-started)\n* [Dependencies](#dependicies)\n* [Web Portal](#web-portal)\n* [Jupyter Notebooks](#notebooks)\n\n\u003ch2 align=\"center\" id=\"acknowledgments\"\u003e Acknowledgments \u003c/h2\u003e\n\n### Purpose of Research\nOur research aims to create price predictive models \u0026 online tools for Airbnb properties in Los Angeles using a set of key features. To set the context for our investigation, we reviewed recent literature related to these features and methods, paying particular attention to their frequency, importance, and usage.\n\n### Faculty Mentors:\n- #### **Sonya Zhang**\n  - Developed the literature review and coding structure, reviewing articles, coding, advising on data collection, methodologies, data analysis, and visualization, revising, proofreading, and formatting the report.\n\n### Fellow Researchers:\n- #### **Minh Khoi Tran**\n  - Reviewed articles, data cleaning, preliminary analysis and visualization on overall variables, regression and classification models, as well as Clustering and Text Mining.\n  - Developed interactive website (linked below) for hosting analyses and visualizations.\n\n- #### **Jin Im**\n  - Reviewing articles, coding, preliminary analysis and visualization on `listing` Attributes, Clustering, and Text Mining.\n  - Testing classification models with feature selection techniques.\n\n- #### **Christine Pugay**\n  - Generated preliminary analyses and data cleaning on Airbnb `policy`, `neighborhood`, and `points-of-interest` (POIs) attributes.\n  - Constructed visualizations for these attributes using Tableau.\n- #### **Kelly Lee**\n   - Generated preliminary analyses and visualizations on Airbnb `reviews` and `host` attributes.\n   - Performed analysis of classification models with `Scikit-learn`\n\nThis repository contains Jupyter Notebooks and Python libraries used in this project, as well as more detailed information on what we have accomplished throughout this project.\n\n\n\u003ch2 align=\"center\" id=\"getting-started\"\u003e Getting Started \u003c/h2\u003e\n\nThe Jupyter Notebook `.ipynb` files can be opened using either [Google Colaboratory](https://colab.research.google.com) or locally using [Visual Studio Code](https://code.visualstudio.com) and 3-rd party extensions.\n\nIf you decide to use Visual Studio Code to open the `.ipynb` files locally, be sure to download these required software:\n* [Python](https://www.python.org/downloads/) (3.10 or above)\n* [Jupyter Extension](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter) (can be installed within Visual Studio Code through the `Extensions` tab)\n\nSince the `listings.csv` was too large to be directly published to GitHub, it has been included inside `dataset.zip`. Remember to unzip the folder and make sure the folder's name is `dataset`.\n\n**CAUTION:** The `airbnblib` folder is meant to house custom-made functions used in the notebooks. Remember to include the `airbnblib` folder in the same directory as your notebooks, or else many functionalities would not work.\n\n\u003ch2 align='center' id=\"dependencies\"\u003e Dependencies \u003c/h2\u003e\n\nAll required dependencies are now listed in the `requirements.txt` file\n\nTo install these Python libraries, run `pip install -r requirements.txt` in the root directory of the project, i.e.:\n```bash\n$ pip install -r requirements.txt\n```\n\n\u003ch2 align='center' id=\"web-portal\"\u003e Web Portal \u003c/h2\u003e\n\nThis is a web portal created to serve this project by showcasing exploratory data analysis, model results, and interactive demos of the model itself. \n\nThe repository for the web portal itself has been migrated a separate repository. You can check them here: [RIO Airbnb Web Portal](https://github.com/marked01one/rio-airbnb-web-portal)\n\n\u003ch2 align='center' id=\"notebooks\"\u003e Jupyter Notebooks \u003c/h2\u003e\n\n### `rough_clean`\n* Notebook for making broad strokes to clean and refine the data. \n* For more involved data refining processes such as sentimental analysis, future notebooks will be created specifically for those purposes \n\n### `exploratory_analysis`\n* Notebook for doing exploratory data analysis into the data, which will be used to determine which variables to delete, edit, etc.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarked01one%2Frio-airbnb-predictive-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarked01one%2Frio-airbnb-predictive-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarked01one%2Frio-airbnb-predictive-model/lists"}