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Develop a rapidminer process capable of analysing

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Case Study Description AirbnbAl approached you to develop a RapidMiner process(es) capable of analysing and predicting customer feedback about their stay in Hong Kong Airbnb rental properties. AirbnbAl provided you with a sample dataset of approximately 6,000 rental listings and 104,000 associated customer reviews. This sample dataset can be downloaded from the unit website.

The provided dataset has been partially cleaned up and includes a variety of numerical, nominal and text attributes, and descriptions of these attributes. AirbnbAl would like you to use RapidMiner to address the following questions:

A. Is there a significant correlation between the sentiment (positive vs negative) of customer reviews of a property, and their review score ratings?

B. Can the review score ratings of properties be predicted (estimated) based on relevant attributes?

C. What are the most meaningful different segments that exist in the retail properties?

AirbnbAl wants you to use RapidMiner to process and explore the provided data, conduct text mining, sentiment analysis, develop, evaluate, and optimise linear regression and cluster analysis models.

Reference no: EM133230718

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