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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*
Travel Reviews/tripadvisor_review.csv 0 → 100644
User ID,Category 1,Category 2,Category 3,Category 4,Category 5,Category 6,Category 7,Category 8,Category 9,Category 10
User 1,0.93,1.8,2.29,0.62,0.8,2.42,3.19,2.79,1.82,2.42
User 2,1.02,2.2,2.66,0.64,1.42,3.18,3.21,2.63,1.86,2.32
User 3,1.22,0.8,0.54,0.53,0.24,1.54,3.18,2.8,1.31,2.5
User 4,0.45,1.8,0.29,0.57,0.46,1.52,3.18,2.96,1.57,2.86
User 5,0.51,1.2,1.18,0.57,1.54,2.02,3.18,2.78,1.18,2.54
User 6,0.99,1.28,0.72,0.27,0.74,1.26,3.17,2.89,1.66,3.66
User 7,0.9,1.36,0.26,0.32,0.86,1.58,3.17,2.66,1.22,3.22
User 8,0.74,1.4,0.22,0.41,0.82,1.5,3.17,2.81,1.54,2.88
User 9,1.12,1.76,1.04,0.64,0.82,2.14,3.18,2.79,1.41,2.54
User 10,0.7,1.36,0.22,0.26,1.5,1.54,3.17,2.82,2.24,3.12
User 11,1.47,1,0.7,0.75,1.66,2.76,3.18,2.89,1.66,2.62
User 12,0.96,2.96,0.29,0.38,0.88,2.08,3.17,2.93,1.66,3.42
User 13,0.74,1.44,2.75,0.45,0.98,1.74,3.2,2.87,1.38,2.34
User 14,0.58,1.64,2.27,0.45,1.26,1.72,3.19,2.91,2.3,2.74
User 15,0.96,1.68,2.29,0.51,1.2,2.84,3.2,2.82,2.02,2.46
User 16,1.25,2.52,1.76,0.5,1.46,2.08,3.19,2.74,1.41,2.32
User 17,0.86,1.04,1.76,0.34,0.06,1.1,3.18,2.73,1.15,2.98
User 18,0.61,1.96,2.49,0.66,1.34,1.78,3.2,3.04,1.15,2.42
User 19,0.67,1.36,1.36,0.38,0.82,3.38,3.18,2.86,1.79,2.8
User 20,0.8,1.04,2.1,0.58,1.18,1.98,3.19,2.93,1.22,2.48
User 21,0.86,1.44,1.12,0.41,1.2,2.18,3.18,2.7,1.06,2.94
User 22,0.96,1.44,0.14,0.42,1.06,2.08,3.17,2.74,1.15,3.22
User 23,0.93,1.16,0.29,0.41,1.02,1.36,3.16,2.74,1.34,3.66
User 24,0.54,1.44,0.3,0.41,1.02,1.46,3.17,2.71,1.73,3.04
User 25,1.22,0.96,1.15,0.54,1.1,2.02,3.18,2.77,1.92,2.46
User 26,0.61,2.84,2.8,0.48,0.56,1.52,3.19,2.54,1.6,2.54
User 27,0.77,1.68,2.24,0.75,1.28,2.64,3.19,2.57,1.34,2.38
User 28,0.99,1.28,1.22,0.59,0.94,1.9,3.18,2.79,1.22,2.4
User 29,0.58,1.2,0.18,0.38,0.54,0.76,3.17,2.69,1.63,2.94
User 30,0.64,1.16,3.12,0.45,1.84,3.16,3.2,2.75,1.54,2.46
User 31,0.74,1.28,2.14,0.5,0.94,2.26,3.2,2.78,1.5,2.34
User 32,0.7,2.24,2.32,0.63,0.72,2.12,3.19,2.65,1.28,2.42
User 33,0.64,2,1.6,0.41,2.08,2.22,3.19,2.8,1.76,2.72
User 34,1.06,1.12,0.21,0.58,0.98,2.5,3.18,2.9,1.92,2.74
User 35,0.96,1.16,0.45,0.29,0.98,1.42,3.18,2.94,2.02,3.02
User 36,0.88,0.96,1.18,0.43,0.74,1.52,3.18,3.01,1.63,2.94
User 37,0.7,1.28,1.15,0.32,0.4,1.1,3.18,2.78,1.76,2.9
User 38,1.02,1.36,0.91,0.5,0.72,1.22,3.18,2.91,1.92,3.2
User 39,0.7,1.52,2.37,0.62,0.86,1.68,3.2,2.86,1.6,2.58
User 40,1.38,1.08,0.18,0.62,0.14,1.46,3.18,2.88,1.66,2.42
User 41,0.99,1.24,0.15,0.24,1.38,1.98,3.17,2.61,1.02,3.06
User 42,0.58,1.48,0.19,0.41,0.5,0.98,3.16,2.74,1.57,3.38
User 43,0.67,1.24,0.61,0.24,1.06,1.9,3.18,2.82,1.25,2.88
User 44,1.02,2,0.42,0.56,0.46,2.06,3.18,2.86,2.05,2.66
User 45,0.64,1.64,0.34,0.62,0.64,2.22,3.18,3.1,1.98,2.8
User 46,1.7,1.4,0.61,0.39,0.7,1.46,3.17,2.9,1.82,3.38
User 47,0.38,1.08,0.77,0.34,1.06,1.46,3.18,3.15,2.18,3.22
User 48,0.9,1.2,1.24,0.35,0.88,1.86,3.18,2.86,1.47,2.94
User 49,0.45,2.96,0.26,0.4,0.56,1.68,3.18,2.9,1.44,2.72
User 50,0.61,1.08,2.22,0.65,1.2,2.06,3.19,2.62,1.31,2.34
User 51,1.02,1.2,0.3,0.38,1.1,1.9,3.17,2.93,1.34,3.42
User 52,0.42,1.48,2.06,0.48,0.96,1.4,3.18,2.71,1.76,2.86
User 53,0.8,1.88,0.66,0.47,2.54,2.18,3.18,2.59,1.89,3.02
User 54,1.02,0.64,1.12,0.51,0.8,1.82,3.18,2.82,1.5,2.56
User 55,1.02,1.2,0.37,0.58,0.54,1.7,3.17,2.78,1.66,3.42
User 56,0.61,1.68,0.46,0.37,0.86,1.1,3.17,2.94,1.31,3.12
User 57,0.8,1.16,2.43,0.54,1.06,2.28,3.19,2.86,1.44,2.48
User 58,0.93,1.48,0.78,0.39,0.7,1.7,3.18,2.82,1.41,2.88
User 59,0.64,1.2,2.27,0.64,1.42,2.83,3.2,2.79,1.47,2.38
User 60,0.83,1.4,1.23,0.64,1.2,2.54,3.18,2.81,1.6,2.54
User 61,1.06,1.92,3.11,0.55,1.46,3.24,3.2,2.62,1.28,2.34
User 62,1.98,0.96,0.18,0.45,0.16,0.82,3.18,3.03,1.02,2.78
User 63,0.9,1.12,0.22,0.45,0.32,1.1,3.18,2.82,2.08,2.96
User 64,0.51,1.6,0.37,0.42,0.48,1.36,3.18,3.1,1.34,3.04
User 65,1.41,1.12,0.68,0.37,1.14,1.76,3.18,2.9,1.5,3.01
User 66,1.47,0.72,0.53,0.62,0.48,1.9,3.18,2.78,1.63,2.88
User 67,0.99,1.28,2.32,0.43,0.16,1.06,3.18,2.79,1.09,3.34
User 68,0.93,1,0.61,0.5,0.8,1.52,3.18,2.77,1.28,2.94
User 69,0.51,1.08,1.92,0.34,1.04,1.3,3.18,2.8,1.09,2.88
User 70,0.54,1.44,0.72,0.48,0.7,1.36,3.18,2.74,1.5,2.54
User 71,1.6,0.8,0.38,0.67,0.5,1.5,3.18,2.82,1.28,2.46
User 72,0.83,1.04,2.94,0.96,0.88,1.86,3.2,2.82,1.25,2.34
User 73,0.74,2.24,0.14,0.47,1.22,1.88,3.17,2.81,1.98,2.58
User 74,0.9,1.08,2.48,0.41,0.82,1.78,3.18,2.83,1.41,3.46
User 75,1.31,1.36,1.47,0.56,0.78,2,3.19,2.86,1.63,2.42
User 76,0.99,2.96,1.71,0.45,1.36,1.96,3.19,2.69,1.38,2.32
User 77,1.28,1.12,1.04,0.42,0.7,1.3,3.18,2.81,1.86,3.36
User 78,0.8,1.76,2.53,0.65,1.04,2.22,3.19,2.79,1.63,2.46
User 79,0.83,1.08,0.67,0.35,0.54,1.06,3.17,2.78,1.25,3.06
User 80,0.7,1.04,0.19,0.47,0.78,2.42,3.17,2.7,1.12,2.98
User 81,2.08,0.92,0.8,0.4,0.54,1.7,3.17,3.07,1.54,3.42
User 82,0.74,1.32,0.93,0.46,0.66,1.82,3.18,2.83,1.86,2.54
User 83,0.7,1.52,0.45,1.65,0.32,0.86,3.17,2.62,2.05,3.04
User 84,1.25,0.92,0.86,0.58,0.66,1.98,3.18,2.88,1.12,2.66
User 85,1.92,0.28,1.12,0.5,0.88,1.96,3.18,2.96,1.86,2.58
User 86,0.74,1.84,0.24,0.34,0.8,1.66,3.17,2.87,1.5,3.34
User 87,0.7,0.76,0.72,1.1,0.42,1.54,3.18,2.69,1.41,2.56
User 88,0.64,1.08,0.19,0.39,0.5,0.9,3.17,2.79,1.18,3.12
User 89,0.64,2.36,0.14,1.68,1.02,1.76,3.18,2.71,1.22,2.42
User 90,0.67,2.08,1.04,0.54,0.74,1.64,3.18,2.86,1.57,2.64
User 91,1.38,1.12,0.72,0.53,0.88,1.36,3.18,2.81,1.79,3.26
User 92,0.56,1.16,0.13,0.49,0.54,0.9,3.17,2.91,1.54,2.64
User 93,1.1,2.04,2.45,0.54,1.44,2.28,3.19,2.74,1.63,2.42
User 94,0.64,1.2,2.27,0.64,1.42,2.83,3.2,2.79,1.47,2.38
User 95,0.99,2,2.13,0.64,1.82,2.6,3.19,2.67,1.5,2.46
User 96,1.12,0.96,0.3,0.46,0.56,1.92,3.18,2.98,1.22,2.94
User 97,0.9,1.44,2.34,0.55,0.94,2.14,3.19,2.79,1.79,2.82
User 98,0.9,0.56,1.38,0.37,0.8,1.9,3.18,2.91,1.41,2.54
User 99,1.02,0.88,2.67,0.53,1.02,1.78,3.19,2.86,2.14,2.78
User 100,0.67,1.6,0.21,0.47,2.16,1.96,3.17,2.86,3.04,3.1
User 101,0.9,0.96,0.38,0.5,0.78,2.1,3.18,2.9,1.79,2.82
User 102,0.64,1.12,0.4,0.37,0.64,1.12,3.18,2.94,1.76,3.06
User 103,0.99,2.12,1.41,0.65,0.96,2.94,3.19,2.82,1.41,2.42
User 104,0.88,1.2,0.16,0.45,0.5,1,3.18,2.67,1.79,2.86
User 105,1.47,0.84,0.64,0.39,0.32,1.18,3.17,2.9,1.73,3.46
User 106,0.86,0.88,2.85,0.39,0.42,1.54,3.2,2.73,1.7,2.46
User 107,1.07,0.56,0.93,0.53,1.3,2.62,3.18,2.94,2.27,2.74
User 108,0.99,1.2,0.38,0.43,0.8,1.1,3.16,2.74,1.15,3.42
User 109,1.76,0.04,0.43,0.39,1.5,1.62,3.17,2.8,0.96,3.18
User 110,0.8,2.16,1.12,0.53,1.54,2.34,3.18,2.83,2.24,2.78
User 111,0.9,1.8,1.25,0.67,0.86,1.86,3.19,2.96,2.46,2.98
User 112,0.9,1.08,1.65,0.34,0.7,1.44,3.18,2.85,1.15,2.86
User 113,0.9,1.84,0.51,0.66,1.04,1.78,3.18,2.79,1.86,2.54
User 114,0.35,1.36,1.41,0.49,1.1,1.5,3.18,3.04,1.28,2.48
User 115,0.83,1.04,2.94,0.96,0.88,1.86,3.2,2.82,1.25,2.34
User 116,1.15,1.28,0.18,0.61,0.7,2.3,3.18,3.01,1.73,2.34
User 117,0.64,1.72,1.25,0.51,0.72,2.04,3.19,2.83,1.76,2.4
User 118,0.61,1.32,0.78,0.54,1.12,1.74,3.18,2.78,1.79,2.94
User 119,0.54,1.2,1.17,0.45,1.04,1.38,3.18,2.58,1.82,2.94
User 120,1.06,1.6,1.1,0.43,0.86,1.84,3.18,2.7,0.96,3.18
User 121,0.86,1.96,1.25,0.56,1.66,2.9,3.18,2.82,1.44,2.5
User 122,0.77,1.04,0.26,0.66,1.42,2,3.18,2.82,1.6,2.64
User 123,0.93,2.68,1.3,0.42,1.42,2.22,3.19,2.58,1.31,2.38
User 124,1.98,0.48,0.75,0.69,0.88,2.62,3.18,2.86,1.54,2.38
User 125,0.74,1.52,1.38,0.59,1.1,2,3.18,2.72,1.47,2.74
User 126,0.64,1.36,0.19,0.46,0.78,1.58,3.18,2.79,1.31,2.82
User 127,0.51,1.44,0.42,0.33,0.74,1.3,3.18,2.77,1.18,2.98
User 128,1.06,1.68,1.02,0.66,0.72,2.02,3.18,2.81,1.41,2.56
User 129,1.09,1.48,0.99,0.35,1.12,1.78,3.17,2.89,1.31,3.34
User 130,0.54,1.2,0.45,0.39,0.88,1.66,3.18,2.7,1.57,2.78
User 131,0.9,0.88,1.52,0.46,0.8,1.48,3.18,2.71,0.99,2.88
User 132,0.83,1.68,1.14,0.51,1.66,2.78,3.18,2.85,1.7,2.54
User 133,0.93,1.64,1.73,0.55,1.46,1.98,3.18,2.74,1.89,2.94
User 134,0.38,1.28,1.54,0.62,1.7,2.16,3.18,3.02,1.31,2.5
User 135,0.51,1.64,0.27,0.3,0.78,1.86,3.17,2.69,2.5,3.34
User 136,0.8,0.96,0.73,0.61,1.1,2.82,3.18,2.99,2.14,2.54
User 137,1.15,1.72,1.62,0.61,0.78,1.94,3.19,3.02,1.47,2.54
User 138,0.51,1.16,1.66,0.53,1.58,1.9,3.18,2.85,1.28,2.88
User 139,0.96,1.4,0.74,0.37,0.86,1.98,3.18,2.8,1.44,3.06
User 140,1.09,1.32,0.74,0.39,0.5,1.06,3.18,3.01,1.86,3.2
User 141,0.74,1.28,0.26,0.3,1.14,1.96,3.17,2.93,1.73,3.34
User 142,0.8,1.12,0.19,0.46,0.82,1.48,3.18,2.82,1.7,2.96
User 143,0.86,1.96,0.61,0.42,1.22,1.86,3.17,2.7,1.22,3.1
User 144,0.61,1.04,0.34,0.45,0.22,1.9,3.18,2.71,2.37,2.62
User 145,0.9,1.4,0.19,0.59,1.04,1.88,3.17,3.06,2.21,3.02
User 146,0.96,2.96,0.29,0.38,0.88,2.08,3.17,2.93,1.66,3.42
User 147,1.09,0.6,0.19,0.61,1.62,2.44,3.18,2.74,1.38,2.46
User 148,0.8,1.24,1.98,0.67,1.38,2.42,3.19,3.12,1.5,2.62
User 149,0.86,1.28,1.01,0.55,1.26,2.12,3.18,2.86,1.92,2.94
User 150,0.77,1.52,1.33,0.51,1.34,2.46,3.18,2.8,1.6,2.54
User 151,1.18,1.12,1.01,0.39,1.2,2.02,3.18,2.93,1.47,3.1
User 152,0.74,1.64,0.18,0.55,0.94,2,3.18,2.74,1.5,2.98
User 153,0.64,1.6,0.21,0.54,0.62,1.22,3.18,2.85,1.44,2.66
User 154,0.64,1.56,0.22,0.57,0.5,2.02,3.18,2.66,1.34,2.78
User 155,1.09,1.44,0.22,0.37,0.34,1.94,3.18,2.73,1.66,2.62
User 156,1.02,1.16,1.22,0.29,0.94,1.66,3.18,2.8,2.08,3.42
User 157,0.9,1.64,0.32,0.5,1.34,2.02,3.18,3.06,1.57,2.86
User 158,2.05,1.12,0.7,0.37,0.5,1.04,3.17,2.77,1.98,3.44
User 159,1.18,2.04,1.89,0.5,1.66,2.18,3.19,2.83,1.41,2.34
User 160,1.06,1.76,1.02,0.27,0.24,1.16,3.18,2.63,1.28,3.36
User 161,0.58,1.12,1.57,0.45,0.78,1.5,3.18,2.77,1.44,2.9
User 162,1.15,1.08,0.51,0.37,0.34,0.98,3.17,2.58,2.21,3.36
User 163,0.9,1.84,0.51,0.66,1.04,1.78,3.18,2.79,1.86,2.54
User 164,0.7,1.28,0.93,0.47,0.54,1.58,3.18,2.82,1.76,2.98
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  • 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.

  • Eric @ericmenk33 commented

    Absolutely loving the convenience of at-home testing these days—makes life so much easier! Check out Swift Blood Tests for quick and reliable results.

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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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    In which an attempt has been made to phase out traditional taxis based on Hindustani and replace them with environmentally friendly alternatives. Under the e-taxi scheme by Maryam Nawaz, employment will be provided to people. This program will include those who are poor and those who have recently completed their degrees.

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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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