A project is on user's rating of travelling. There are various 24 attractions included in this database.
1. Amazon SageMaker:
- Steps to start Amazon SageMaker for Machine Learning algorithm
- Amazon sagemaker content and presentation is included under Amazon SageMaker folder.
2. Presentation
- Presentation for the project
- Including details of aim, objective, algorithm explnation, result, amazon sagemaker and future work
3. Python
- main.py - python file to run a code
- requirement.txt - install requirement.txt to run python code
- result.txt - result printed from python code
- Graph - Includes all graph generated while running python code
- jupyter - includes 3 versions of code imporvements and later on combined into main.py file
- Version - python: 3.7.1 - pip: 19.0.3
4. R
- r-code.R - includes implementation in R
- output.txt - output from R
- graph - Includes all graph generated from R code includes RPlot with k-means, k-medoids, clara - Evaluation graph - elbow curve
- Version - r studio: 1.1.4
5. Group-16 - Project report
- Final report of a project in word and pdf
6. train.csv
- Dataset of a project
- Total categories: 24
- Total Number of users: 5456
- ID Column: Users ID
| Name | Description |
|---|---|
| Attribute 1 | Unique user id |
| Attribute 2 | churches |
| Attribute 3 | resorts |
| Attribute 4 | beaches |
| Attribute 5 | parks |
| Attribute 6 | theatres |
| Attribute 7 | museums |
| Attribute 8 | malls |
| Attribute 9 | zoo |
| Attribute 10 | restaurants |
| Attribute 11 | pubs/bars |
| Attribute 12 | local services |
| Attribute 13 | burger/pizza shops |
| Attribute 14 | hotels/other lodgings |
| Attribute 15 | juice bars |
| Attribute 16 | art galleries |
| Attribute 17 | dance clubs |
| Attribute 18 | swimming pools |
| Attribute 19 | gyms |
| Attribute 20 | bakeries |
| Attribute 21 | beauty & spas |
| Attribute 22 | cafes |
| Attribute 23 | view points |
| Attribute 24 | monuments |
| Attribute 25 | gardens |
- Python
- R
- K-Means
- K-medoids
- Clara
Run a command pip install -r requirement.txt
- Celebi, M. E., Kingravi, H. A., and Vela, P. A. (2013). "A comparative study of efficient initialization methods for the k-means clustering algorithm". Expert Systems with Applications. [Link]
- Tryon, Robert C. (1939). Cluster Analysis: Correlation Profile and Orthometric (factor) Analysis for the Isolation of Unities in Mind and Personality. Edwards Brothers. [Link]
- Maulik, Ujjwal, and Sanghamitra Bandyopadhyay. "Performance evaluation of some clustering algorithms and validity indices." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 12 (2002): 1650-1654. [Link]
- Kovács, Ferenc, Csaba Legány, and Attila Babos. "Cluster validity measurement techniques." In a 6th International symposium of hungarian researchers on computational intelligence. 2005. [Link]
- David J. Ketchen, Jr; Christopher L. Shook (1996). "The application of cluster analysis in Strategic Management Research: An analysis and critique". Strategic Management Journal. [Link]
- Trupti M. Kodinariya, Dr. Prashant R. Makwana. “Review on determining number of Cluster in K-Means Clustering”. In International Journal of Advance Research in Computer Science and Management Studies. Volume 1, Issue 6, November 2013 [Link]
- ‘Use and Impact of Online Travel Reviews’, Markus Schuckert , Liu XianweiRob Law [Link]
- ‘Hospitality and Tourism Online Reviews: Recent Trends and Future Directions’, Ulrike Gretzel , Kyung-Hyan Yoo [Link]
- Getting started with Amazon SageMaker: [Link]
- Use the Amazon SageMaker SDK: Python: [Link]
- SageMaker Examples: [Link]
- Python vs R Comparison: [Link]
- Python Libraries: [Link]
- R Libraries : [Link]
- K-means Algorithm: [Link]
- K-means clustering
- K-means in R language
- Multidimension analysis in python
- K-means clustering in python
- Visualize multi dimensional data
Vatsal Shah
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