Difference between revisions of "ANLY482 Current Practicums: Current Practicums"
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'''Recommended Team Composition''': Students are free to come up with their own teams but forming a team with diverse backgrounds and skill-sets is highly recommended. | '''Recommended Team Composition''': Students are free to come up with their own teams but forming a team with diverse backgrounds and skill-sets is highly recommended. | ||
| − | More information on the dataset | + | More information on the dataset in this [[File:ReferenceDocumentYelp.docx]] |
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Revision as of 11:25, 7 August 2015
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| Team | About the Project | Student Member(s) | Project Supervisor | Sponsor |
|---|---|---|---|---|
| Recommendations Matter to Us !! | Recommendations and opinions from others are a part of our daily lives. From eating out in a restaurants to buying commodities online we yearn to know what others have to say about them. We want to see what others have experienced from a purchase partly because it involves spending our hard earned money but largely because of the plethora of information on available options out there. Seeking recommendations is an attempt towards staying away from bad experiences and maximizing once sense of satisfaction from a purchase.
This project which shares people's opinion of various businesses and their ratings on Yelp. It will give you an opportunity to apply exploratory and predictive analytics techniques such as sentiment analysis, ratings prediction, and item-item similarities to design a flow that can help foodies across the world find the place of their choice faster with improved precision. Stress will be given on how you design a general purpose model using restaurant related data which can be applied to any other practice in hospitality domain such as hospitals, hotels equally. Types of analysis: Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations,sentiment analysis of english text, analyzing text for prominent phrases and topics of discussions, dashboard creation to display the solution.
Suggested Platforms: SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations Libraries to explore: SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), D3.js, C3.js Other recommendations: No prior knowledge of any courses is assumed while designing this project but prior knowledge of Social and Contextual Analytics, Visual Analytics will be good for taking up advanced analysis during project related tasks. Recommended Team Composition: Students are free to come up with their own teams but forming a team with diverse backgrounds and skill-sets is highly recommended. More information on the dataset in this File:ReferenceDocumentYelp.docx |
Prof. Seema Chokshi
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics |
Prof. Seema Chokshi
Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics |
