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Assignment 3 Written Practical Report 100 marks 40% Weighting 4000Cou 20/05/19
The key frameworks and concepts covered in modules 1–10 are particularly relevant for this assignment. Assignment 3 relates to the specific course learning objectives 1, 2, 3 and 4:
1. apply knowledge of people, markets, finances, technology and management in a global context of business intelligence practice (data warehousing and big data architecture, data mining process, data visualisation and performance management) and resulting organisational change and understand how these apply to the implementation of business intelligence in organisation systems and business processes
2. identify and solve complex organisational problems creatively and practically through the use of business intelligence and critically reflect on how evidence based decision making and sustainable business performance management can effectively address real-world problems
3. comprehend and address complex ethical dilemmas that arise from evidence based decision making and business performance management
4. communicate effectively in a clear and concise manner in written report style for senior management with the correct and appropriate acknowledgment of the main ideas presented and discussed.
Note you must use RapidMiner Studio for Task 1 and Tableau Desktop for Task 3 in this Assignment 3. Failure to do so may result in Task 1 and/or 3 not being marked and zero marks awarded.
Note carefully University policy on Academic Misconduct such as plagiarism, collusion and cheating. If any of these occur they will be found and dealt with by the USQ Academic Integrity Procedures. If proven, Academic Misconduct may result in failure of an individual assessment, the entire course or exclusion from a University program or programs.
Assignment 3 consists of three main tasks and a number of sub tasks Task 1 (Worth 30 Marks)
The goal of Task 1 is to predict the likelihood of rainfall for tomorrow (next day) based on today’s weather conditions. In Task 1 of Assignment 3 you are required to use the data mining tool RapidMiner to analyse and report on the weatherAUS.csv data set provided for Assignment 3. You should review the data dictionary for weatherAUS.csv data set (see Table
1 below). The Australian Weather dataset contains over 138,000 daily observations from January 2008 through to January 2017 from 49 Australian weather stations. Observations were drawn from numerous weather stations. The daily observations are available from http://www.bom.gov.au/climate/data Bureau of Meteorology. Definitions for each variable are adapted from http://www.bom.gov.au/climate/dwo/IDCJDW0000.shtml. In completing Task 1 of Assignment 3 you will need to apply the business understanding, data understanding, data preparation, modelling and evaluation phases of the CRISP DM data mining process.
Table 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 the location of the weather station.
MinTemp Real Minimum temperature in degrees Celsius.
MaxTemp Real Maximum temperature in degrees Celsius.
Rainfall Real Amount of rainfall recorded for the day in mm.
Evaporation Real So-called Class A pan evaporation (mm) in the 24 hours to 9am.
Sunshine Real Number of hours of bright sunshine in the day.
WindGustDir Polynominal Direction of the strongest wind gust in the 24 hours to midnight.
WindGustSpeed Integer Speed (km/h) of the strongest wind gust in the 24 hours to midnight.
WindDir9am Polynominal Direction of wind at 9am
WindDir3pm Polynominal Direction of wind at 3pm
WindSpeed9am Integer Wind speed (km/hr) averaged over 10 minutes prior to 9am.
WindSpeed3pm Integer Wind speed (km/hr) averaged over 10 minutes prior to 3pm.
Humidity9am Integer Relative humidity (percent) at 9am.
Humidity3pm Integer Relative humidity (percent) at 3pm.
Pressure9am Real Atmospheric pressure (hpa) reduced to mean sea level at 9am.
Pressure3pm Real Atmospheric pressure (hpa) reduced to mean sea level at 3pm.
Cloud9am Integer Fraction of sky obscured by cloud at 9am. This is measured in -oktas-, which are a unit of eighths. It records how many eights of the sky are obscured by cloud. A 0 measure indicates completely clear sky whilst an 8 indicates that it is completely overcast.
Cloud3pm Integer Fraction of sky obscured by cloud (in -oktas-: eighths) at 3pm. See Cload9am for a description of the values.
Temp9am Real Temperature (degrees C) at 9am.
Temp3pm Real Temperature (degrees C) at 3pm.
RainToday Nominal Integer: Yes if precipitation (mm) in the 24 hours to 9am exceeds 1mm, otherwise No.
RISK_MM Real Amount of rain. A kind of measure of the -risk-.
RainTomorrow Nominal Target variable. Did it rain tomorrow? Yes or No
Task 1.1 Conduct an exploratory data analysis of the weatherAUS.csv data set using RapidMiner to understand the characteristics of each variable and the relationship of each variable to the other variables in the data set. Summarise the findings of your exploratory data analysis in terms of describing key characteristics of each of the variables in the weatherAUS.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc and relationships with other variables if relevant in a table named Task 1.1 Results of Exploratory Data Analysis for weatherAUS Data Set.
Hint: Statistics Tab and Chart Tab in RapidMiner provide a lot of descriptive statistical information and useful charts like Barcharts, Scatterplots etc. You might also like to look at running some correlations and chi square tests. Indicate in Task 1.1 Table which variables you consider to be the key variables which contribute most to determining whether it is likely to rain tomorrow.
Briefly discuss the key results of your exploratory data analysis and the justification for selecting your five top variables for predicting whether it is likely to rain tomorrow based on today’s weather conditions. (About 250 words)
Task 1.2 Build a Decision Tree model for predicting whether it is likely to rain tomorrow based on today’s weather conditions using RapidMiner and an appropriate set of data mining operators and a reduced weatherAUS.csv data set determined by your exploratory data analysis in Task 1.1. Provide these outputs from RapidMiner (1) Final Decision Tree Model process, (2) Final Decision Tree diagram, and (3) associated decision tree rules.
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 whether it is likely to rain tomorrow based on today’s weather conditions and relevant supporting literature on the interpretation of decision trees (About 250 words).
Task 1.3 Build a Logistic Regression model for predicting whether it is likely to rain tomorrow based on today’s weather conditions using RapidMiner and an appropriate set of data mining operators and a reduced weatherAUS.csv data set determined by your exploratory data analysis in Task 1.1. Provide these outputs from RapidMiner (1) Final Logistic Regression Model process and (2) Coefficients, and (3) Odds Ratios. Hint for this Task 1.3 Logistic Regression Model you may need to change data types of some variables.
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 whether it is likely to rain tomorrow based on today’s weather conditions and relevant supporting literature on the interpretation of logistic regression models (About 250 words).
Task 1.4 You will need to validate your Final Decision Tree Model and Final Logistic Regression Model. Note you will need to use the Cross-Validation Operator; Apply Model Operator and Performance Operator in your data mining process models here.
Discuss and compare the accuracy of your Final Decision Tree Model with the Final
Logistic Regression Model for whether it is likely to rain tomorrow based on today’s weather conditions based the results of the confusion matrix, and ROC charts for each final model. You should use a table here to compare the key results of the confusion matrix for the Final Decision Tree Model and Final Logistic Regression Model (About 250 words).
Note the important outputs from your data mining analyses conducted in RapidMiner for Task 1 should be included in your Assignment 3 report to provide support for your conclusions reached regarding each analysis conducted for Task 1.1, Task 1.2, Task 1.3 and Task 1.4. Note you can export the important outputs from RapidMiner as jpg image files and include these screenshots in the relevant Task 1 parts of your Assignment 3 Report.
Note you will find the North Text book a useful reference for the data mining process activities conducted in Task 1 in relation to the exploratory data analysis, decision tree analysis, logistic regression analysis and evaluation of the accuracy of the Final Decision Tree model and the Final Logistic Regression model. Note the Logistic Regression Model and Decision Tree Model and Evaluation of Models using RapidMiner are covered in Chapters 9, 10 and 13 of North Textbook and in some of the tutorials contained within
RapidMiner
Task 2 (Worth 30 marks)
Research the relevant literature on how big data analytics capability can be incorporated into a data warehouse architecture. Note Chapter 3 Data Warehousing and Chapter 7 Big Data Concepts and Tools of Sharda et al. 2018 Textbook will be particularly useful for answering some aspects of Task 2. You will also need to conduct some independent research on current knowledge and practice on how data analytics capability and more traditional data warehousing are being gradually merged and accommodated to accommodate a big data analytics capability for the organisation scenario outlined in Task 2.1.
Task 2.1 Provide a high level data warehouse architecture design for a large stated owned water utility that incorporates big data capture, processing, storage and presentation in a diagram called Figure 1.1 Big Data Analytics and Data Warehouse Combined.
Task 2.2 Describe and justify the main components of your proposed high level data warehouse architecture design with big data capability incorporated presented in Figure 1.1 with appropriate in-text referencing support (about 1250 words).
Task 2.3 Identify and discuss the key security privacy and ethical concerns for a large stated owned water utility in using big data analytics capability combined with data warehousing that is based on an algorithmic approach to decision making with appropriate in-text referencing support (about 750 words).
Task 3 (Worth 30 marks)
Scenario Dashboard
Los Angeles Police Department (LAPD) are responsible for enforcing law and order in the
City of Los Angeles which is the cultural, financial, and commercial centre of Southern California. With a census-estimated 2015 population of 3,971,883, it is the second-most populous city in the United States (after New York City) and the most populous city in California. Located in a large coastal basin surrounded on three sides by mountains reaching up to and over 10,000 feet (3,000 m), Los Angeles covers an area of about 469 square miles (1,210 km2).
LAPD Crime Analytics Unit would like to have a Crime Events dashboard built with the aim of providing a better understanding of the patterns that are occurring in relation to different crimes across the 21 Police Department areas over time in the City of Los Angeles. In particular, they would like to see if there are any distinct patterns in relation to (1) types of crimes, (2) frequency of each type of crime across each of the 21 Police Department areas for years 2012 through to first quarter of 2016 based on the LACrimes2012-2016.csv data set. Note this is a large data set containing over 1 Million records. This Crime Events dashboard will assist LAPD to better manage and coordinate their efforts in catching the perpetrators of these crimes and be more proactive in preventing these crimes from occurring in the first place.
The LAPD Crime Analytics Unit wants the flexibility to visualize the frequency that each type of crime is occurring over time across each of the 21 Police Department areas/districts in the City of Los Angeles. They want to be able to get a quick overview of the crime data in relation to category of crimes, location, date of occurrence and frequency that each crime is occurring over time and then be able to zoom in and filter on particular aspects and then get further details as required.
LA Crimes Data Set Data Dictionary
variable name type Description
year_id 1. character Original dataset id
date_rptd 2. date Date crime was reported
dr_no 3. character Count of Date Reported
date_occ 4. date Date crime occurred
time_occ 5. date Time crime occurred on a day
area 6. character Area Code
area_name 7. character Area geographical location
rd 8. character Nearby road identifier
crm_cd 9. character Crime type code
crm_cd_desc 10. character Crime type description
Status 11. character Status code
status_desc 12. character Status outcome of crime
location 13. character Nearby address location
cross_st 14. character Nearby cross street
lat 15. numeric Latitude of crime event
long 16. numeric Longitude of crime event
year 17. numeric Year of crime occurred
month 18. numeric Month of crime occurred
day_of_month 19. numeric Day of month crime occurred
hour_of_day 20. numeric Hour of day crime occurred
month_year 21. Month and year when crime occurred
day_of_week 22. character Day of week crime occurred
weekday 23. character Weekday/weekend classification for crime event
intersection 24. character Occurred at an intersection
crime_classification 25. character subjective binning of crimes
Task 3 requires a Tableau dashboard consisting of four crime event views of the LA Crimes 2012-2016 data set.
Task 3.1 Specific Crimes within each Crime Category for a specific Police Department Area and specific year
Task 3.2 Frequency of Occurrence for a selected crime over 24 hours for a specific Police Department Area
Task 3.3 Frequency of Crimes within each Crime Classification by Police Department Area and by Time
Task 3.4 Geographical (location) presentation of each Police Department Area for given crime(s) and year. Note for this task you will need to make use of the geo-mapping capability of Tableau Desktop.
You should briefly discuss the key findings for each of these four views in your Crimes Event Dashboard (about 60 words each and 250 words in total)
Task 3.5 Provide a rationale (drawing on relevant literature for good dashboard design) for the graphic design and functionality that is provided in your LAPD Crimes Event dashboard for the required four specified crime events views for Tasks 3.1, 3.2, 3.3 and 3.4 (About 750 words). 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.
For your Assignment 3 submission, you will need to submit your Task 3 Tableau workbook in .twbx format which will contain your dashboard, four views and the associated data set as a separate document together with your Assignment 3 Main Report in word docx format.
Report presentation writing style and referencing (worth 10 marks)
Presentation: use of formatting, spacing, paragraphs, tables and diagrams, introduction, conclusion, table of contents
Writing style: Use of English (Correct use of language and grammar. Also, is there evidence of spelling-checking and proofreading?)
Referencing: Appropriate level of referencing in text where required, reference list provided, used Harvard Referencing Style correctly
Assignment 3 Report should be structured as follows:
Assignment 3 Cover page
Table of Contents
Task 1 Main Heading
Task 1 Sub Tasks – Sub headings for Tasks 1.1, 1.2 and 1.3 Task 2
Task 2 Sub Tasks – Sub headings for Task 2.1, 2.2, 2.3 and 2.4
Task 3
Task 3 Sub Tasks – Sub headings for Task 3.1, 3.2, 3.3, 3.4 and 3.5
List of References
List of Appendices

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