This task provides you with opportunities to understand and apply predictive analytics techniques in real- world situations (ULO2)

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Description

Purpose

This task provides you with opportunities to understand and apply predictive analytics techniques in real- world situations (ULO2), and to apply your understanding of current techniques and trends in predictive analytics to a particular business environment (ULO3), as outlined below. By completing this task, you will develop discipline-specific knowledge and capabilities to demonstrate your understanding of the application of business analytics (GLO1), including digital literacy and expertise in using analytics technologies to analyse complex business data and disseminate findings (GLO3).

 

Business context and scenario

The business context for this assignment is the domestic tourism sector, focusing on providers of tourist accommodation. Organisations such as AirBnB provide a digital platform that tourists can use to rent properties in particular locations around the world. The properties are owned by private individuals (property hosts), and AirBnB takes a commission for bookings via their digital platform.

AirBnB approached you again to develop RapidMiner process(es) capable of analysing and predicting customer feedback about their stay at Melbourne Airbnb rental properties. AirBnB provided you with a sample dataset of approximately 1,000 rental listings and 100,000 associated customer reviews. This sample dataset can be downloaded from the unit website.

The provided dataset (A2-AirBNB-Melbourne-dataset.zip) has been partially cleaned up and includes a variety of numerical, nominal and text attributes, and descriptions of these attributes.

You are also provided with a list of commonly used positive and negative sentiment words to be used in your analysis. These lists can also be downloaded from the unit website.

AirbnbAI would like you to use RapidMiner to address the following tasks:

Task A: Develop a process model to determine if a significant correlation exists in the dataset between:

• the raw sentiment score (calculated as total positive words - total negative words) in all customer review comments of a property, and

• each property’s review score rating.

Task B: Develop a predictive model to estimate the review score ratings of all properties located outside of the Melbourne Central Business District (CBD), using relevant predictor attributes in the data set. For this purpose, Melbourne CBD area is defined as being within the following boundaries:

• Longitude > 144.9 and < 145.06

• Latitude > -37.95 and < -37.75

 

Task C: AirBNB is concerned when any of the review score attributes (accuracy, checkin, cleanliness, communication, location and value) of a property drops below 10, especially when this happens for multiple review score attributes for a particular property. For this reason, you are asked to develop a process model to identify the top 3 most frequently co-occurring review score attributes that drop below 10 for properties across the full dataset.

 

 

• The dataset, report templates, and additional important notes for this assignment are available on the unit site on CloudDeakin.

• You must use the provided template for your report. Your final report must adhere to page the page limits as only pages within the limits will be marked. It is essential that the executive summary section of your report is written for a non-technical reader (e.g., a senior manager at AirBnB) and that the remaining parts of the report are written for a technical reader (e.g., a business analyst or data scientist).

• You must use RapidMiner available from Deakin Apps and Desktops Anywhere (ADA) for your analytical process modelling.

• The consistency of your RapidMiner file(s) will be checked against the results in your report. You must not modify the data file provided for this assignment before importing it into RapidMiner.

 

Learning Outcomes

This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs) which have been aligned to the Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe the knowledge and capabilities graduates acquire and can demonstrate on completion of their course. This assessment task is an important tool in determining your achievement of the ULOs. If you do not demonstrate achievement of the ULOs you will not be successful in this unit. You are advised to familiarise yourself with these ULOs and GLOs as they will inform you on what you are expected to demonstrate for successful completion of this unit.

 

 

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