Assignment Global Header Logo
Order Now
4.5

+61 480 020 208

Order Now
Recently Updated on June 22, 2023

MIS772 – PREDICTIVE ANALYTICS

Ellipse 68Subtraction 3
MIS772 Predictive Analytics

MIS772 – PREDICTIVE ANALYTICS
Subject Code – MIS772
Subject Name – Predictive Analytics
University Name – Deakin Business School, Australia

In this blog, you will get to know the instructions and everything that a student needs to know before preparing for your Predictive Analytics MIS772 exam.

What is Predictive Analytics?

Predictive Analytics employs data, statistical algorithms, and machine learning techniques to determine the likelihood of future outcomes based on historical data. The motive is to provide the best judgment of what will happen in the future, rather than simply knowing what has happened.
Types of Predictive Models
Simple linear regression
A statistical method for describing the relationship between two continuous variables.

Multiple linear regression
A statistical method for describing the relationship between more than two continuous variables.

Polynomial regression
A nonlinear relationship will result from a nonlinear relationship between residuals and a predictor. A polynomial regression model can be used to archive this.
Y = β0 + β1X +β2X2 + … + βhXh + ϵ

Support vector regression
Another regression method is the Support Vector Machine, which characterizes the procedure based on all relevant features. With a few minor variations, the Support Vector Regression (SVR) uses similar principles to the SVM for categorization.

Decision tree regression
These decision tree models use a tree-like structure to create classification or regression-related algorithms.

Get 20% OffYour Good Grades Are Just A Click Away!
Get 20% Off
Your Good Grades Are
Just A Click Away!

ASSESSMENT DETAILS

The purpose of this assignment is to develop your ability to:
(i) Analyze patterns in a business dataset utilizing descriptive data mining concepts, and
(ii) Develop predictive models to address questions relevant to a particular business.

The business context for this assignment is the international tourism sector, focusing on providers of tourist accommodation. Organizations 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.
Globally, the tourism sector has been heavily impacted by the COVID-19 pandemic. Due to restrictions on international travel, the tourism sector is currently under financial pressure globally and must make prudent decisions to remain viable. Against this background, AirBnB approached you to generate recommendations for their rental listings in Denmark. Airbnb provided you with a dataset of 23,941 listings of rentals for the period of Nov 2016-October 2019. This dataset reflects the pre-COVID period.

DELIVERED ORDERS
EXPERTS
CLIENT RATING

Tasks

Create a geospatial (map-based) visualization of all rental properties, using their geo-locations to automatically categorize those located on the Danish island of Sealand versus those in the rest of Denmark. For your visualization, use the following ranges of longitude and latitude to identify Sealand properties:
• Longitude >= 10.99 and < 13, and
• Latitude < 56.25.
Using these ranges in combination, you should be able to generate a new attribute (say “Sealand”) that determines whether the property is on the island of Sealand (true) or if the property is located in the rest of Denmark (false).

As Sealand incorporates the capital city of Copenhagen, Airbnb wants to know if there are differences between the Sealand properties versus those in the rest of Denmark. Explore this from the perspective of tourists staying at the rentals (define).

Given the financial pressure on the tourism sector, Airbnb wants to advertise properties that are NOT located on Sealand and have been popular with tourists in the past. Define a new attribute that can be used to classify whether these non-Sealand properties are popular or not, using appropriate attributes in the dataset. Develop two different classification models that can be used by Airbnb managers to predict if a particular non-Sealand rental property is likely popular or not. Evaluate the performance of each model, indicating the best predictive model.

Your final deliverables must include:

i) the final report according to the submission template (as a PDF file)
ii) all RapidMiner files (in the RMP format) combined as a single ZIP file.

Share article
Was this helpful?

DisLike

Thank your For Apprication

Get Help Instantly
Service Page Form
Free Features
Revisions
for $31.49 Free
Bibliography
for $17.05 Free
Outline
for $6.55 Free
Title Page
for $6.55 Free
Formatting
for $10.49 Free
Plagiarism Report
for $20.99 Free
Get All These Features
for $93.12 Free
Proceed to Order
Related Services
Assignment Help
Course work
Homework Help
Essay Writing
Article Writing
Research Paper
Programming Help
View More
Subscribe To Avail Our Special Offers
Drop in your mail to receive all the notifications for updates,
offers and exclusive assignment writing guides.
Drop in your mail to receive all the notifications for updates, offers and exclusive assignment writing guides.
Leave this field blank
Subscribe To Avail Our Special Offers
Drop in your mail to receive all the notifications for updates, offers and exclusive assignment writing guides.
Leave this field blank

We accept

Copyright © 2023 Assignment global. All rights reserved.

We accept

Copyright © 2022 Assignment global. All rights reserved.
Assignment Global Footer Logo

We accept

Copyright © 2023 Assignment global. All rights reserved.