{"id":18523217,"url":"https://github.com/iglee/outrunjulesverne","last_synced_at":"2025-09-15T03:39:59.268Z","repository":{"id":90698349,"uuid":"105220571","full_name":"iglee/outrunJulesVerne","owner":"iglee","description":"Personalizing unique travel experiences using data science.","archived":false,"fork":false,"pushed_at":"2021-08-26T04:27:06.000Z","size":22221,"stargazers_count":7,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-24T05:13:18.259Z","etag":null,"topics":["data-mining","gis-data","jules-verne","natural-language-processing","nlp","personalizing-travels","python","recommender-system","scraping","spark","travel","travelling-salesman-problem","tripadvisor","unsupervised-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":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iglee.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":"2017-09-29T02:25:49.000Z","updated_at":"2022-09-03T14:30:37.000Z","dependencies_parsed_at":null,"dependency_job_id":"4452283d-3027-44f5-9df7-bb7798818a7b","html_url":"https://github.com/iglee/outrunJulesVerne","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/iglee%2FoutrunJulesVerne","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iglee%2FoutrunJulesVerne/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iglee%2FoutrunJulesVerne/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iglee%2FoutrunJulesVerne/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iglee","download_url":"https://codeload.github.com/iglee/outrunJulesVerne/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248031414,"owners_count":21036393,"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-mining","gis-data","jules-verne","natural-language-processing","nlp","personalizing-travels","python","recommender-system","scraping","spark","travel","travelling-salesman-problem","tripadvisor","unsupervised-learning"],"created_at":"2024-11-06T17:34:37.403Z","updated_at":"2025-09-15T03:39:59.257Z","avatar_url":"https://github.com/iglee.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Outrun Jules Verne!\nThis project aims to beat the best adventurer I know with the best name: Jules Verne.  His book \"Around the World in 80 Days\" chronicles a fictional around-the-world trip on a bet that Phileas Fogg and Passepartout complete it in 80 days.  Let's beat them at their game!\n\n![Jules Verne's Around the World in 80 Days](img/julesverne.jpg)\n\nI'll help come up with an around-the-world trip path based on your selected features like sentiment and interests; you just be an adventurer and find something to bet on.\n\n## Why?\nFirst and foremost, it's fun! Travel recommendations are somewhat of a dated art; travel agencies have largely been replaced by Google in this interwebbed and globally-minded world. However, personalizing travels is still a great interest for the most individualistic modern population with the most autonomy the history has ever seen. This is my attempt at tailoring unique travels and experiences for you.\n\n## What kind of data did I use?\n- reviews from tripadvisor.com\n    - At first, I requested access to their API, but they never got back to me. So, I relied on webscraping to build my dataset from scratch. The resulting 1M+ customer reviews of attractions around the globe are stored in a private AWS S3 bucket.\n- GIS data of local attractions, i.e. the exact geographical locations of each attraction.\n\n\n## How did I get the data?\n- Webscraped tripadvisor.com using `request` and `BeautifulSoup`\n- GIS data: publicly available\n\n## How can I recommend a path around globe for you?\n- Natural Language Processing to extract features like sentiment, local attractions, characteristics, etc. Namely, I used spark's LDA.\n- Monte Carlo Metropolis algorithm to add some randomness.\n\nFor detailed implementation, take a look at `README.md` in `src` folder.\n\n## How well did I do?\n- Show friends and others, and ask them to evaluate. (I still have the survey scores, and it was around 4 / out of 5!)\n\n## Show \u0026 tell\nThe project *used to be* deployed as a webapp: ~~http://outrunjulesverne.com/.~~ However, the URL is no longer active. For how it used to look, please view [this presentation](https://docs.google.com/presentation/d/1JuGs_KHnCfcJgEeBP_-hz9N0NTFQ0OAd7PhthRrp3yY/edit). But as a quick look, here's an example of selected features:\n\n![Feature selected](img/features.png)\n\nAnd one example of an output path around the world is:\n![Your recommended path around the world](img/path.png)\n\nAnd along the way, you'll see these beauties of the world:\n![beauties around the world](img/placesToSee.png)\n\nThis app may come back in the near future :]. I really enjoyed this project, and hope you did too. Welp, at least maybe inspire a grand trip.\n\n## Useful Information\n - https://www.goodreads.com/book/show/54479.Around_the_World_in_Eighty_Days\n- https://am207.github.io/2017/wiki/lab4.html\n- https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figlee%2Foutrunjulesverne","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Figlee%2Foutrunjulesverne","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figlee%2Foutrunjulesverne/lists"}