ShinyPET
Authors: Ang Su Yiin, Joey Chua, Kevin Gunawan Albindo, Kam Tin Seong
Abstract: Shiny PET is 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. There are 3 analytics modules: exploratory, text, predictive.
Full Description: 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 as data generated is rich in information, which consists of structured, unstructured (textual), and location data.
The Exploratory module allows users to perform exploratory and confirmatory analysis on selected variables to identify interesting patterns.
The Text module allows users to analyse textual data from reviews to generate more quantitative insights.
The Predictive module enables users to prepare and build a variety of prediction models.
For more information on this project, please visit our website
To optimise your user experience, please refer to our user guide
Keywords: exploratory, confirmatory, text, predictive, airbnb, analytics, leaflet, tidytext, tidymodels
Shiny app: https://kgalbindo.shinyapps.io/shinyPET
Repo: GitHub - suyiinang/ourshinyPET: A Predictive, Exploratory and Text Shiny application for Airbnb data.
RStudio Cloud: Posit Cloud
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