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| <td>[http://www.smu.edu.sg/faculty/profile/108496/Michael-GENKIN Prof. Michael GENKIN] | | <td>[http://www.smu.edu.sg/faculty/profile/108496/Michael-GENKIN Prof. Michael GENKIN] |
| Assistant Professor of Sociology, SOSS | | Assistant Professor of Sociology, SOSS |
− | </td>
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− | </tr>
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− | <tr>
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− | <td>[[Improved Decisions for Ocean Freights]]</td>
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− | <td>
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− | The Green Transformation Lab (GTL) is a joint initiative by SMU and DHL aimed at accelerating the evolution of sustainable logistics across Asia Pacific. Leveraging SMU’s multi-faculty academic excellence and DHL’s sustainability services, expertise and capability in supply chains, the Green Transformation Lab is focused on creating solutions that help companies transform their supply chains, becoming greener, more resource efficient and sustainable. <br/>
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− | As part of on-going effort to improve fill-rate of ocean freight, GTL needs a dashboard to visualize and allow decision-makers to select the most appropriate mode of shipment. There are two modes of ocean freight shipments: FCL (Full-container-load) and LCL (Less-than-container-load).<br/>
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− | More information about general things that GTL does can be found at GTL official [http://gtl.smu.edu.sg website]
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− |
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− | '''Types of analysis''':
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− | The aim of this analytics project is to perform data analytics and build a dashboard using a visualization tool such as Tableau.
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− | The features on the visualization tool includes (but not limited to):<br/>
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− | a. Ocean freight profiling for the selected customer<br/>
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− | b. Visualization of major trade-lanes (pair of source and destination of shipments)<br/>
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− | c. Simple what-if analysis of a different mode of shipment is selected (e.g., customer choose LCL instead of FCL) on key defined KPIs such as cost, CO2 emissions
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− | <br />
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− |
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− | '''Suggested Platforms''': Project Sponsor would like the team to use Tableau for developing visualizations ;<br />
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− |
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− | '''Other recommendations''': No prior knowledge of any courses is assumed while designing this project but prior knowledge Visual Analytics will be good for taking up advanced analysis during project related tasks.<br />
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− |
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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.<br/>
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− | More information on the dataset will be shared soon.
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− | </td>
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− | <td>
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− | Terence CHU Tailun<br />
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− | PHANG Ming Min<br />
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− | NG Zhen Jie
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− | </td>
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− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
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− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
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− | </td>
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− | <td>[http://sis.smu.edu.sg/faculty/profile/104156/TAN-Kar-Way| Prof. TAN Kar Way]
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− | Assistant Professor of Information Systems (Practice)
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− | </td>
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− | </tr>
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− | <tr>
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− | <td>[[Team BEK|Recommendations Matter to Us! - Team BEK]]</td>
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− | <td> 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.<br/>
| |
− | This project shares people's opinion and their ratings on Yelp about businesses operating in various cites from U.K., Germany, Canada to Unites States. It will give you an opportunity to apply exploratory and predictive analytics techniques such as n-gram analysis, topic identification, sentiment analysis, ratings prediction, item-item similarities to design a flow that can help users 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''': The teams can analyse the data to unravel many aspects such as:<br/>
| |
− | 1. What seasonal trends and patterns can be detected in data. Are there places being reviewed only on certain occasions or time periods around the year. Are their cities where people tend to eat out more then people from other cities. Try to predict what type of restaurants or bars will be reviewed more based on an upcoming event or festival.
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− |
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− | 2. Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations.
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− |
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− | 3. Sentiment analysis of English text, use of n-gram analysis to filter out prominent phrases and tips from the reviews, identification of topics of discussions. Identifying sarcasm and sarcastic reviewers. Identifying what are the main complainants, suggestions and wishes of reviewers.
| |
− |
| |
− | 4. Apply above mentioned analysis techniques to warn businesses when their overall image starts to go down, highlights new ideas for them to increase sales. By using a dashboard to display findings.
| |
− | <br />
| |
− |
| |
− | '''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
| |
− |
| |
− | '''Libraries to explore''': SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), scikit-learn (Machine Learning in Python), D3.js, C3.js<br />
| |
− |
| |
− | '''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.<br />
| |
− |
| |
− | '''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.<br/>
| |
− | More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
| |
− | </td>
| |
− | <td>
| |
− | Keith TAN Xiang Wei<br/>
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− | YAO Min Gee<br/>
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− | ZHENG Boyang
| |
− | </td>
| |
− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
| |
− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
| |
− | </td>
| |
− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
| |
− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td>[[Team Accuro|Recommendations Matter to Us!]] - Group 2 (Team Accuro)</td>
| |
− | <td> 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.<br/>
| |
− | This project shares people's opinion and their ratings on Yelp about businesses operating in various cites from U.K., Germany, Canada to Unites States. It will give you an opportunity to apply exploratory and predictive analytics techniques such as n-gram analysis, topic identification, sentiment analysis, ratings prediction, item-item similarities to design a flow that can help users 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''': The teams can analyse the data to unravel many aspects such as:<br/>
| |
− | 1. What seasonal trends and patterns can be detected in data. Are there places being reviewed only on certain occasions or time periods around the year. Are their cities where people tend to eat out more then people from other cities. Try to predict what type of restaurants or bars will be reviewed more based on an upcoming event or festival.
| |
− |
| |
− | 2. Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations.
| |
− |
| |
− | 3. Sentiment analysis of English text, use of n-gram analysis to filter out prominent phrases and tips from the reviews, identification of topics of discussions. Identifying sarcasm and sarcastic reviewers. Identifying what are the main complainants, suggestions and wishes of reviewers.
| |
− |
| |
− | 4. Apply above mentioned analysis techniques to warn businesses when their overall image starts to go down, highlights new ideas for them to increase sales. By using a dashboard to display findings.
| |
− | <br />
| |
− |
| |
− | '''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
| |
− |
| |
− | '''Libraries to explore''': SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), scikit-learn (Machine Learning in Python), D3.js, C3.js<br />
| |
− |
| |
− | '''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.<br />
| |
− |
| |
− | '''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.<br/>
| |
− | More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
| |
− | </td>
| |
− | <td>
| |
− | Li Xiang<br>
| |
− | Rhea Chandra<br>
| |
− | Piyush Pritam Sahoo<br>
| |
− | Malvania Smeet Saunil
| |
− | </td>
| |
− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
| |
− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
| |
− | </td>
| |
− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
| |
− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
| |
− | </td>
| |
− | </tr>
| |
− | <tr>
| |
− | <td>[[Recommendations Matter to Us !!]] - Group 3</td>
| |
− | <td> 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.<br/>
| |
− | This project shares people's opinion and their ratings on Yelp about businesses operating in various cites from U.K., Germany, Canada to Unites States. It will give you an opportunity to apply exploratory and predictive analytics techniques such as n-gram analysis, topic identification, sentiment analysis, ratings prediction, item-item similarities to design a flow that can help users 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''': The teams can analyse the data to unravel many aspects such as:<br/>
| |
− | 1. What seasonal trends and patterns can be detected in data. Are there places being reviewed only on certain occasions or time periods around the year. Are their cities where people tend to eat out more then people from other cities. Try to predict what type of restaurants or bars will be reviewed more based on an upcoming event or festival.
| |
− |
| |
− | 2. Clustering similar businesses, Item-Item to user-user similarities to develop and refine recommendations.
| |
− |
| |
− | 3. Sentiment analysis of English text, use of n-gram analysis to filter out prominent phrases and tips from the reviews, identification of topics of discussions. Identifying sarcasm and sarcastic reviewers. Identifying what are the main complainants, suggestions and wishes of reviewers.
| |
− |
| |
− | 4. Apply above mentioned analysis techniques to warn businesses when their overall image starts to go down, highlights new ideas for them to increase sales. By using a dashboard to display findings.
| |
− | <br />
| |
− |
| |
− | '''Suggested Platforms''': SAS Visual Analytics suite; SAS EM, python or R for text analytics; tableau or D3.js for developing visualizations<br />
| |
− |
| |
− | '''Libraries to explore''': SAS EM and SAS VA sentiment analysis package, Natural language tool kit & libraries (both Python 2.7 and R), scikit-learn (Machine Learning in Python), D3.js, C3.js<br />
| |
− |
| |
− | '''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.<br />
| |
− |
| |
− | '''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.<br/>
| |
− | More information on the dataset in this [[File:ReferenceDocumentYelp.docx]]
| |
− | </td>
| |
− | <td>
| |
− | Ng Hui Ying <br/>
| |
− | Aldred Lau Wen Yang <br/>
| |
− | Michelle Teo Sok Lee <br/>
| |
− | Tan Yi Hao
| |
− | </td>
| |
− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
| |
− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
| |
− | </td>
| |
− | <td>[http://sis.smu.edu.sg/faculty/profile/83109/Seema%20CHOKSHI Prof. Seema Chokshi]
| |
− | Lecturer of Information Systems, Programme Head, SMU Undergraduate Second Major in Analytics
| |
| </td> | | </td> |
| </tr> | | </tr> |
| | | |
| </table> | | </table> |