Understand and apply predictive analytics techniques

Reference no: EM133684921 , Length: word count:3000

Predictive Analytics

Learning Outcome 1: Understand and apply predictive analytics techniques in real-world situations.

Learning Outcome 2: Apply an integrated understanding of current techniques and trends in predictive analytics to the business environment.

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 to develop RapidMiner processes 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.
AirBnB would like you to use RapidMiner to address the following tasks:

Task A: Develop a process model to determine if a significant correlation exists for all properties 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, using relevant predictor attributes in the data set.

Task C: What are the most meaningful distinct clusters of properties located in the Melbourne Central Business District (CBD)? Use the following ranges of longitude and latitude to identify CBD properties:

Longitude > 144.9 and < 145.06
Latitude > -37.95 and < -37.75

Specific requirements

The dataset, report templates, and additional important notes for this assignment.
The dataset is unique to each student:
open the dataset (Listings) in Microsoft Excel.
enter your student number (in the yellow cell on the second worksheet).
wait until your dataset is generated, and note down your unique key
this unique key must be entered in your report template for the assignment
save and close the dataset
You must NOT make any other modifications to the dataset for this assignment

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) 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 Altair RapidMiner Studio 10.3 for your analytical process modelling. Refer to the first seminar on how to obtain the relevant educational edition of the software for use in this unit.
The consistency of your RapidMiner file(s) will be checked against the results in your report.

Reference no: EM133684921

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