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Subject Code : BUS5PA
Country : Australia
Assignment Task

 

Objective:
a) Revise BUS5PA material on predictive modelling
b) Demonstrate knowledge of data exploration and selection of variables to apply for the predictive models
c) Demonstrate knowledge of building different types of predictive models using R
d) Demonstrate knowledge on comparing and evaluating different predictive models
e) Relate theoretical knowledge of predictive models and best practices to application scenarios

 


Business Case 
A property assessor’s office in a midwestern state of the USA is in the process of updating their  property (housing) price assessment method where they want to apply a data driven technique. The  trial dataset consists of 113 variables describing 3970 property sales in the area between 2006 and  2010. The management is very keen to apply predictive modelling for this task where the trail data set  is to be used to build and evaluate predictive models to ascertain the feasibility of such an approach.  The company has outsourced the task to you.

 

Part A – Problem Formulation

The objective of this section (Part A) is to introduce students to the ‘domain understanding and  familiarisation’ phase data analysts go through prior to the actual analytics. Since you may have to  carry out analytics projects in different domains in the future, where you may not have domain  knowledge, it is important to develop this skill.  

1. Carryout an exploratory study to identify the background and relevant aspects of properties which influence their value in USA and methods used for property price evaluation and  assessment? 

2. Identify the data sources that would contain information useful for property value assessment.  What is the possible format of such information? Will you face any problems in accessing these  data? 

3. What variables would be useful to build a predictive model to assess the property price? 

 

Part B – Data Exploration and Cleaning

Use the provided dataset to answer this section. You are given access to 31 variables that are directly  related to property sales from the above-mentioned dataset. Most of these variables are similar to the type of information that an assessor will use to evaluate and assess the price of a property (e.g.  when was it built? How big is the lot? What is the size of the living room? Is the basement developed  and completed? Number of bathrooms?). You need to answer the following questions with evidence  and justifications.

 

 

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  • Posted on : April 05th, 2019
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