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Add travel review dataset

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Travel Reviews/README.md 0 → 100644
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level:** Intermediate <br/>
**Recommended Use:** Clustering/Classification Models<br/>
**Domain:** Web<br/>
## Travel Reviews Data Set
### Group similar travel reviews
---
![](180.jpg)
---
This *intermediate* level data set has 980 rows and 11 columns.
The data set includes reviews on destinations in 10 categories mentioned across East Asia. Each traveler rating is mapped as Excellent(4), Very Good(3), Average(2), Poor(1), and Terrible(0) and average rating is used against each category per user.
This data set is populated by crawling TripAdvisor.com.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, **clustering** and **classification modelling techniques**.
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |---------------- |--------------------------------------------------- |-------------- |-------------------------------- |--------------- |
| 1 | User ID | Unique user id | Qualitative | "User 4", "User 13", "User 19" | 0 |
| 2 | Category 1 | Average user feedback on art galleries | Quantitative | 0.45, 0.74, 0.67 | 0 |
| 3 | Category 2 | Average user feedback on dance clubs | Quantitative | 1.8, 1.44, 1.36 | 0 |
| 4 | Category 3 | Average user feedback on juice bars | Quantitative | 0.29, 2.75, 1.36 | 0 |
| 5 | Category 4 | Average user feedback on restaurants | Quantitative | 0.57, 0.45, 0.38 | 0 |
| 6 | Category 5 | Average user feedback on museums | Quantitative | 0.46, 0.98, 0.82 | 0 |
| 7 | Category 6 | Average user feedback on resorts | Quantitative | 1.52, 1.74, 3.38 | 0 |
| 8 | Category 7 | Average user feedback on parks/picnic spots | Quantitative | 3.18, 3.2, 3.18 | 0 |
| 9 | Category 8 | Average user feedback on beaches | Quantitative | 2.96, 2.87, 2.86 | 0 |
| 10 | Category 9 | Average user feedback on theaters | Quantitative | 1.57, 1.38, 1.79 | 0 |
| 11 | Category 10 | Average user feedback on religious institutions | Quantitative | 2.86, 2.34, 2.8 | 0 |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Travel Reviews Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Travel+Reviews).<br/>
The UCI page mentions the following publication as the original source of the data set:<br/>
*Renjith, Shini, A. Sreekumar, and M. Jathavedan. 2018. Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain. In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 12731. IEEE*
  • Ayla @aylarose495 commented

    Nice addition! The dataset structure and detailed README make it ideal for testing clustering techniques on tourism-related data. You might also want to explore real-world socio-economic features like how to check PMT score to enhance user profiling or demographic segmentation.

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  • Sofia @ranamk0003 commented

    Wow, this travel review dataset looks like a fantastic resource for diving into data analysis! With 980 reviews across 10 categories, it’s perfect for practicing clustering and classification techniques, especially for anyone interested in understanding traveler preferences in East Asia. As a WGU student exploring data science, this kind of dataset could be a great way to sharpen your skills for real-world applications.

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  • Nol @nolcard70 commented

    The UCI travel reviews dataset, with 980 user ratings on East Asian attractions, is great for clustering travelers into groups like "culture seekers" or "beach lovers" using K-means. Visualizing correlations with matplotlib could reveal insights, like museum fans also loving parks. Pairing this with Dubai Metro zones 2025 could help plan personalized trips to Dubai Mall or Jumeirah Beach.

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