Case Study -QA 216

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Business Analytics and Data Visualization
 
This assignment is based on an often cited Boston housing dataset. Students are expected to practise how to apply data mining techniques to resolve a business problem. Now, assuming that you are taking a business analyst role in a real estate consulting firm, can you leverage your business analytical skills to make better use of the given housing dataset? This assignment has three sections
 

(1) learn how to understand a dataset;
(2) attempt to resolve business problems using typical data mining techniques such as association rule learning, classification and regression; and
(3) present your analysis.
 

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Question #1: Understand the Dataset
 

Read the attached txt file “data_description.txt” which describes the dataset. Report the following statistics regarding the dataset. (1) how many features are included in this dataset? List 3 string features and 3 number features respectively. (2) List 3 least varied features (i.e. smallest number of distinct values), and 3 most varied features.
 

Question #2: Relationships Discovery among Features
 

We attempt to discover the relationships existed among the string features. You are expected only to report any one relationship: X->Y, where X should include at least two features and Y represents one feature. For example, we can have X={BldgType=1Fam, Sale Condition=Normal} -> Y={Sale Type=WD}. Referring to “data_description.txt”, you need to justify what this relationship suggests in practice and how can we make use of it to improve our business (as a real estate consulting firm). Using the same example, we can report that customers who are willing to sell their single-family detached houses via a normal sale tend to choose the sale type of warranty deed – conventional. Our real estate agents can leverage this knowledge when they are dealing with buyers/sellers.
 

Question #3: Classification of ExterCond
 

Choose the features (at least 2) those you think relevant to the external condition of a house. Split the data as 90% for training and 10% for testing, with any classifier (hint: Bayes or J48). Report the performance by “correctly/incorrectly classified instances” and justify why you choose these features very briefly.

 

Question #4: House price prediction
 

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