https://github.com/iglee/outrunjulesverne
Personalizing unique travel experiences using data science.
https://github.com/iglee/outrunjulesverne
data-mining gis-data jules-verne natural-language-processing nlp personalizing-travels python recommender-system scraping spark travel travelling-salesman-problem tripadvisor unsupervised-learning
Last synced: about 1 month ago
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Personalizing unique travel experiences using data science.
- Host: GitHub
- URL: https://github.com/iglee/outrunjulesverne
- Owner: iglee
- Created: 2017-09-29T02:25:49.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2021-08-26T04:27:06.000Z (about 4 years ago)
- Last Synced: 2025-03-24T05:13:18.259Z (7 months ago)
- 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
- Language: Jupyter Notebook
- Homepage:
- Size: 21.2 MB
- Stars: 7
- Watchers: 2
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
# Outrun Jules Verne!
This 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!
I'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.
## Why?
First 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 Travel recommendations are somewhat of a dated art; travel agencies have largely been replaced by Google in this interwebbed and globally-minded world.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.
## What kind of data did I use?
- reviews from tripadvisor.com
- 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.
- GIS data of local attractions, i.e. the exact geographical locations of each attraction.
## How did I get the data?
- Webscraped tripadvisor.com using `request` and `BeautifulSoup`
- GIS data: publicly available
## How can I recommend a path around globe for you?
- Natural Language Processing to extract features like sentiment, local attractions, characteristics, etc. Namely, I used spark's LDA.
- Monte Carlo Metropolis algorithm to add some randomness.
For detailed implementation, take a look at `README.md` in `src` folder.
## How well did I do?
- Show friends and others, and ask them to evaluate. (I still have the survey scores, and it was around 4 / out of 5!)
## Show & tell
The 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:
And one example of an output path around the world is:
And along the way, you'll see these beauties of the world:
This 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.
## Useful Information
- https://www.goodreads.com/book/show/54479.Around_the_World_in_Eighty_Days
- https://am207.github.io/2017/wiki/lab4.html
- https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining