Impress MIS772 Predictive Analytics 1 / 1 MIS XXXXXXXXXXT2 A2 Adv Predictive Models for Business AirbnbAI approached you again to develop a RapidMiner process capable of analysing customer feelings...

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Impress MIS772 Predictive Analytics 1 / 1 MIS772 2020 T2 A2 Adv Predictive Models for Business AirbnbAI approached you again to develop a RapidMiner process capable of analysing customer feelings (sentiment) about their stay in one of the Sydney Airbnb rental properties. AirbnbAI sent you a data set of 36,000 rental listings and the text of 548,000 reviews across 38 Sydney neighbourhoods. The provided information has been partially cleaned up and includes a variety of numerical, nominal and text attributes, description of which can be found on the Inside Airbnb web site (source to upper-right). AirbnbAI would like you to use RapidMiner to analyse (mainly) text contained in the data set. AirbnbAI technical advisers suggested to address the following issues and provided some helpful hints: A) Is there a significant discrepancy between the sentiment of a property host and of the customers? And if so, in which property-types and neighbourhoods is this most pronounced? (use Operator Toolbox sentiment tools, Join and Aggregate) B) What property groups can be identified purely from their textual description, what are their characteristics and the recent (i.e. 2020) sentiment of customers? (use text mining, sentiment analysis, data clustering and segmentation analysis) C) Can the customer sentiment be predicted for the newly listed properties purely by looking at their text description? If not what other aspects of the rental property need to be also considered? (use an estimation model) AirbnbAI wants you to use RapidMiner to cleanup and explore the provided data, then conduct sentiment analysis, text mining and cluster analysis, as well as develop and evaluate an estimator to predict the customer sentiment, to minimise RMSE, MAE and r2. The following mini-case study will be used in assignment A2. Data: http://www.deakin.edu.au/~jlcybuls/pred/data/AirBnB_Reviews_Sydney.zip Source: http://insideairbnb.com/get-the-data.html This assignment aims for students to learn how to ... ● Articulate problems and solutions in business terms ● Gain insights from text data ● Prepare data for different models ● Develop estimation and clustering models ● Assess and report model performance ● Become curious about the world through data and analytics Individual Tasks and Deliverables Partial Submission (Question A - marked with the final submission) Exec Problem: Define your problem in business terms, in doing so answer question A, cross-reference with other report sections for support. Data Preparation: Deal with duplicates, bad and missing values (may use imputation). Transform the selected attributes or create the new ones as needed. Produce supporting charts and tables to answer question A. Final Submission (Questions B and C) Exec Solution: Describe your solution in business terms, in doing so answer questions B and C, cross-reference with other report sections. Data Exploration: Use clustering and segmentation analysis to investigate groups of rental properties based on their text description, consider customer sentiment as one of the aspects. Deal with anomalies. Visualise clusters and anomalies. Provide support for question B. Model: Create two estimation models and variants, i.e. linear regression and decision tree, optionally also an ensemble, to address question C. Evaluation and Optimisation: Optimise clustering and estimation models. Evaluate them. Compare the performance of different models and select the best. Provide support for questions B and C. Honest Testing: Honest test the best estimation model and investigate results. Also apply a model to a single listing and describe the results. ● See CloudDeakin for more info, especially the assignment template and the assessment rubric. ● When in doubt students will be asked to present their work to the markers. ● Weekly and comprehensive progress submissions are compulsory. ● Late penalty of 5% per day on the final submission will apply. No extensions / lateness over 5 days. Unzip and use the files as you see fit. http://www.deakin.edu.au/~jlcybuls/pred/data/AirBnB_Reviews_Sydney.zip http://insideairbnb.com/get-the-data.html Slide 1