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CIS8008 Business Intelligence

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Assignment 3 consists of three main tasks and a number of sub tasks Task 1  

Task 1

Critically review and discuss My Health Record system (www.myhealthrecord.gov.au) in terms of current privacy provisions for patients electronic health records drawing the Australian Digital Health Agency’s privacy policy (https://www.myhealthrecord.gov.au/about/privacy-policy) and recent changes to the My Health Record Act which  will be brought into line with the existing Australian Digital  Health Agency policy.

Your review and discussion of My Health Record system and its privacy provisions for patients should be guided by the following:

  • Australian Privacy Principles (APPs) in the Privacy Act (https://www.digitalhealth.gov.au/policies/privacy),
  • Requirements of the (2) My Health Records Act (https://www.legislation.gov.au/Details/C2017C00313)and
  • Healthcare Identifiers Act(https://www.legislation.gov.au/Details/C2017C00239

Task 2 

The goal of Task 2 is to predict the likelihood of a customer becoming a loan delinquency and forfeiting on a loan for ACME Bank (see Table 1 Data Dictionary for loan-delinq.csv data set below). It is important you understand this data set in order to complete Task 2 and four sub tasks.

Task 2.1 Conduct an exploratory data analysis of the training data set loan-delinq.csv using RapidMiner Studio data mining tool.

Provide the following for Task 2.1:

  • A screen capture of your final EDA process and briefly describe your final EDA process
  • Summarise the key results of your exploratory data analysis in a table namedTable

2.1 Results of Exploratory Data Analysis for loan-delinq.csv

  • Discuss the key results of your exploratory data analysis and provide a rationale for selecting your top 5 variables for predicting loan delinquency as the outcome based on the results of your exploratory data analysis and a review of the relevant literature on key factors contributing to a loandelinquency

Note: Table 2.1 should include the key characteristics of each variable in the loan- delinq-train.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc

Hint: The Statistics Tab and the Chart Tab in RapidMiner provide a lot of descriptive statistical information and the ability to create useful charts like Barcharts, Scatterplots etc for the EDA analysis. You might also like to look at running some correlations or chi sq tests whichever is appropriate for the loan-delinq.csv data set to indicate which variables are the top 5 key variables and contribute most to predicting a loan delinquency as an outcome.

Task 2.2 Build a Decision Tree model for predicting loan delinquency based on the data set loan-delinq.csv using RapidMiner and an appropriate set of data mining operators and a reduced set of variables from loan-delinq.csv determined by your exploratory data analysis  in Task 2.1. Provide the following for Task 2.2:

  • (1) Final Decision Tree Model process, (2) Final Decision Tree diagram, and (3) Decision tree
  • Briefly explain your final Decision Tree Model Process,  and discuss the results of the Final Decision Tree Model drawing on the key outputs (Decision Tree Diagram, Decision Tree Rules) for predicting loan delinquency. This discussion should be based on the contribution of each of the top five variables to the Final Decision Tree Model and relevant supporting literature on the interpretationof decision trees.

Table 1 Data dictionary: loan-delinq.csv data set

Variable Name

Description

Type

SeriousDlqin2yrs

Person experienced 90 days past due delinquency or worse

Y/N

 

RevolvingUtilizationOfUnsecuredLines

Total balance on credit cards and personal lines of credit except real estate and

no installment debt like car loans divided by sum of credit limits

 

percentage

age

Age of borrower in years

integer

NumberOfTime30-59DaysPastDueNotWorse

Number of times borrower 30-59 days past due but no worse in last 2 years.

integer

DebtRatio

Monthly debt payments, alimony, living costs divided by monthly gross income

percentage

MonthlyIncome

Monthly income

real

 

NumberOfOpenCreditLinesAndLoans

Number of Open loans (installment like car loan or mortgage) and Lines of credit

(e.g. credit cards)

 

integer

NumberOfTimes90DaysLate

Number of times borrower has been 90 days or more past due.

integer

NumberRealEstateLoansOrLines

Number of mortgage and real estate loans including home equity lines of credit

integer

 

NumberOfTime60-89DaysPastDueNotWorse

Number of times borrower has been 60-89 days past due but no worse in last 2

years.

 

integer

NumberOfDependents

Number of dependents in family excluding themselves (spouse, children etc.)

integer


Task 2.3 Build a Logistic Regression model for predicting loan delinquency based on the loan-delinq.csv data set using RapidMiner and an appropriate set of data mining operators and a reduced set of variables determined by your exploratory data analysis in Task 2.1. Provide the following for Task 2.3:

  • (1) Final Logistic Regression Model process and (2) Coefficients, and (3) Odds Ratios. Hint you can RapidMiner Studio Logistic Regression operator or you can to install the Weka Extension in RapidMiner Studio and use Logistic Regression Operator for this Task3.
  • Briefly explain your final Logistic Regression Model Process and discuss the results of the Final Logistic Regression Model drawing on the key outputs (Coefficients, Odds Ratios) for predicting loan delinquency. This discussion should be based on   the contribution of each of the top five variables to the Final Logistic Regression Model and relevant supporting literature on the interpretation of logistic regression models

Task 2.4 Conduct a comparative performance evaluation of your Final Decision Tree Model with your Final Logistic Regression Model for predicting loan delinquency. Note you will need to use the Cross Validation Operator; Apply Model Operator and Performance (Binominal Classification) Operator in your final data mining process models (Decision Tree, Logistic Regression) to generate the required model performance metrics (Accuracy, Miscalculation Rate, True Positive Rate, False Positive Rate, Area under Roc Chart (AUC), Precision, Recall, Lift, Sensitivity, F Measure) required for Task 2.4.

Provide the following for Task 2.4:

  • A screen snapshot of the Confusion Matrix and AUC for each Final Model (Decision Tree, LogisticRegression)
  • A table named Table 2.2 Results of Model Performance Evaluation (Decision Tree, Logistic Regression) that compares the key results of the performance evaluation for the Final Decision Tree Model and Final Logistic Regression Model in terms of Model Accuracy, Miscalculation Rate, True Positive Rate, False Positive Rate, Precision, Recall, Lift, Sensitivity, F
  • Discuss and compare the key results of your performance evaluation of two final models (Decision Tree, Logistic Regression) presented in parts i and ii of the Task 2.4, indicate which model is better and explainwhy.

The important outputs from data mining analyses conducted using RapidMiner for Task 2 should be included in your Assignment 3 report to provide support for conclusions reached regarding each analysis conducted for Task 2.1, Task 2.2, Task 2.3 and Task 2.4.

Note export the important outputs from RapidMiner as jpg image files and include these screenshots in the relevant Task 2 sections and/or appendices of your Assignment 3 Report.

Note you will find the Sharda et al. 2018 and North Text books useful references for the data mining process activities conducted in Task 2 in relation to the exploratory data analysis, decision tree analysis, logistic regression analysis and evaluation of the comparative performance of the Final Decision Tree model and the Final Logistic Regression model.

Task 3 

Australian Weather dataset (see Data Dictionary Table 3.1) contains over 145,000 daily observations from January 2008 through to June 2017 from 49 Australian weather station locations for rainfall and evaporation recorded. Note for some weather station locations such as Uluru, the data set is incomplete. The daily observations are available from http://www.bom.gov.au/climate/data Bureau of Meteorology. Variable definitions adapted from http://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml.

Table 3.1 Data dictionary for Australian Weather Data set variables

Variable Name

Data Type

Description

Date

Date

Date of weather observation

Location

Text

Common name of location of weather station.

Rainfall

Real

Amount of rainfall recorded for day in mm.

Evaporation

Real

So-called Class A pan evaporation (mm) in 24 hours to

9am.

Task 3 requires you build a Tableau Dashboard Australian Weather by Location (AWL) which includes four different views of the weatherAus.csv data set as specified in sub Tasks 3.1, 3.2, 3.3 and 3.4. An additional data set weatherAus-locations.csv is provided which will need to be joined with weatherAus.csv on the common variable/field location in order to provide location specific data views in the AWL dashboard.

See first record of weatherAUS-locations.csv data set

stnID

Location

stnNum

latitude

longitude

postcode

state

2002

Albury

72160

-36.069

146.9509

2640

nsw

It’s a simple operation in Tableau to join two different files on a common variable/field name – for Assignment 3 it is locations variable/field

Task 3.1 Create a Tableau View of rainfall by day for each location and a specific state and related locations. Provide a screen capture of and describe the Tableau view you have created and comment on the rainfall over one month across state locations and does this differ much for the different states.

Task 3.2 Create a Tableau View of total rainfall by year for each location and a specific state. Provide a screen capture of and describe the Tableau view you have created and comment on variation of total rain across locations for a specific state .

Task 3.3 Create a Tableau View that compares locations by total evaporation for a specific state over months. Provide a screen capture of and comment on the levels of evaporation for different locations and states .

Task 3.4 Create a Tableau GeoMap View of all Australian weather stations that displays the provides latitude and longitude and total rainfall for a selected year. Provide a screen capture of and describe the Tableau Geomap view you have created and comment on one selected location for state. 

Task 3.5 Provide screen snapshot of your AWL Dashboard and an accompanying rationale (drawing on the relevant literature for good dashboard design) for the graphic design and functionality that is provided by your AWL Dashboard for the four specified Tableau views for sub Tasks 3.1, 3.2, 3.3 and 3.4 

Note Stephen Few is considered to be the Guru for good Dashboard Design and has wrote a number of books on this topic. Worth having a look at his website https://www.perceptualedge.com/about.php and in particular his examples of poorly designed dashboard views and his suggestions for better dashboard views.

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My Assignment Help. (2021). Business Intelligence. Retrieved from https://myassignmenthelp.com/free-samples/cis8008-business-intelligence/decision-tree-procedure.html.

My Assignment Help (2021) Business Intelligence [Online]. Available from: https://myassignmenthelp.com/free-samples/cis8008-business-intelligence/decision-tree-procedure.html
[Accessed 20 April 2021].

My Assignment Help. 'Business Intelligence' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/cis8008-business-intelligence/decision-tree-procedure.html> accessed 20 April 2021.

My Assignment Help. Business Intelligence [Internet]. My Assignment Help. 2021 [cited 20 April 2021]. Available from: https://myassignmenthelp.com/free-samples/cis8008-business-intelligence/decision-tree-procedure.html.


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