Project Groups
ISSS608 Visual Analytics and Applications
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Project Groups
Please provide project description the project title and an abstract of your project. The abstract should not be more than 350 words. You are also required to include project blog link and the names of team member.
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Understanding Airbnb listing in Australia The abundance of Airbnb data provides great opportunity to conduct a variety of data analyses to understand the residential short-lease rental market. The dataset that has be scrapped on the Airbnb web and made publicly available by Inside Airbnb provides geospatial, textual, and quantitative data on each of the listings listed on the web. This project provides an analytics platform for interested parties (especially non-data specialists) to conduct exploratory spatial data, text , cluster, and regression analysis on the Australia Airbnb dataset using simple and user-friendly interactive dashboards that does not require programming knowledge. |
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Project Title and Abstract Understanding Key Stories Covered In the Media and How Readers Engaged With News As we become more and more inundated with news from various digital sources today, understanding what the key stories are across the digital spectrum is becoming more and more challenging. As such, we are interested in understanding how to best present a visual snapshot of the key stories that are covered in local media and identifying how readers engaged with the news. |
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Predicting whether an individual would go for the H1N1 vaccine Vaccination is a crucial public health measure to flatten the curve in a pandemic. By looking at a dataset that contains the personal demographics and attitudes of respondents in the USA towards H1N1 vaccination, we hope to predict whether an individual would go for the vaccine. |
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Our Shiny PET: A Predictive, Exploratory and Text Application for Airbnb Data The increasing availability of data has resulted in the increased demand for data driven decisions. Although there is an extensive range of commercial statistical tools, they are often subscription-based and demand good technical knowledge to mine and draw insights from. Therefore, it may not appeal to the average user. As such, our project aims to develop a user-friendly application that will enable users to make data-driven decisions without the need to understand programming languages or have extensive statistical knowledge. We will use Airbnb data as our baseline for this project - data generated is rich in information, which consists of structured, unstructured (textual), and location data. With this application, users will be able to perform text analysis on review and listing data to generate more quantitative insights. The exploratory module allows users to identify interesting patterns based on selected variables. Findings from the exploratory module will be further augmented in the confirmatory module where selection of statistical methods will be guided based on user’s chosen variables. Finally, the predictive module enables users to prepare and build a variety of prediction models without needing to have in-depth understanding of the predictive models and its algorithms. |
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The impact of lifestyle and family background on grades of high school students. |
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