Reference no: EM132993550

MIS772 Predictive Analytics - Deakin University

Data Analysis and Report

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.

Assignment Objectives
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.

Case Study Description

AirbnbAI approached you to develop a RapidMiner process(es) capable of analysing and predicting customer feedback about their stay in Singapore Airbnb rental properties. AirbnbAI provided you with a sample dataset of approximately 4,300 rental listings and 53,000 associated customer reviews. This sample dataset can be downloaded from the unit website.

The provided dataset (MIS772 A2 Data.zip) has been partially cleaned up and includes a variety of numerical, nominal and text attributes, and descriptions of these attributes.
AirbnbAI 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?
AirbnbAI 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.

Task and Deliverables:

• Executive Summary: Define your problem and solution in business terms, in doing so answer questions A, B and C, cross-reference with other report sections for support.

• Data Exploration, Pattern Discovery, and Preparation: Deal with any duplicates, bad and missing values, and anomalies. Transform selected attributes or create the new ones as needed.

Use text mining techniques and simplistic sentiment analysis (i.e. simply calculate positive-negative words) in review comments as illustrated in the lectures/seminars in Week 4; refer also to partial example process provided (Question A).
Identify appropriate attributes to predict the review score ratings of properties (Question B).
Investigate groups of rental properties and identify appropriate attributes to identify different clusters. Visualize clusters (Question C).

• Modelling: Develop a linear regression model to predict the review score rating of properties by selecting appropriate predictive attributes. Test the linear model and investigate results (Question B).
Develop a cluster model to reveal the most meaningful segments (Question C).

• Evaluation and Optimisation: Evaluate and optimise the performance of all models. Report metrics for the best performing models (Questions B, C).

Attachment:- Predictive Analytics.rar