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SIT718 Real World Analytics

SIT718 Real World Analytics
Assessment Task 3: Problem Solving
Using aggregation functions for data analysis
Deakin University

Learning Outcomes

This assessment assesses the following Unit Learning Outcomes (ULO) and related Graduate Learning Outcomes (GLO):

Unit Learning Outcome (ULO)

ULO1 - assessed through student ability to apply knowledge of multivariate functions, data transformations and data distributions to summarise data sets.

ULO2 - assessed through the student ability to analyse datasets by interpreting summary statistics, model and function parameters.

ULO4 - assessed through student ability to develop software codes to solve computational problems for real world analytics.

Graduate Learning Outcome (GLO)

GLO1 - Discipline knowledge and capabilities

GLO4 - Critical thinking

GLO5 - Problem solving


This assignment will test your knowledge and understanding of the aggregation functions and their applications for data summarization and prediction. This assignment will also test your ability in R programming, in using specific R commands as well as R packages.


The work is individual. Solutions and answers to the assignment must be explained carefully in a concise manner and presented carefully. Use of books, articles and/or online resources on share price related to SIT718 Real World Analytics is allowed. Students are expected to refer to the suitable literature where appropriate.


Plagiarism occurs when a student passes off as the student’s own work, or copies without acknowledgement as to its authorship, the work of any other person or resubmits their own work from a previous assessment task.

Collusion occurs when a student obtains the agreement of another person for a fraudulent purpose, with the intent of obtaining an advantage in submitting an assignment or other work.

Work submitted may be reproduced and/or communicated by the university for the purpose of assuring academic integrity of submissions: https://www.deakin.edu.au/students/study- support/referencing/academic-integrity

Using aggregation functions for data analysis

Download SIT718_Assessment-Task_3-T1_2019-data and script.zip it contains the data file [ Energy19.txt ] and the R code [ AggWaFit718.R ] to use with the following tasks, include these in your R working directory.

Energy Prediction of Domestic Appliances Dataset

The given dataset, {"Energy19.txt"}, can be used to create models of energy use of appliances in a energy-efficient house. The dataset provides the Energy use of appliances (denoted as Y) using 671 samples. It is a modified version of data used in the study [1]. The dataset includes 5 variables, denoted as X1, X2, X3, X4, X5, and Y, described as follows:

  • X1: Temperature in kitchen area, in Celsius
  • X2: Humidity in kitchen area, given as a percentage
  • X3: Temperature outside (from weather station), in Celsius
  • X4: Humidity outside (from weather station), given as a percentage
  • X5: Visibility (from weather station), in km Y: Energy use of appliances, in Wh

Assignment Tasks

1. Understand the data [20 marks]

  • Download the txt file (Energy19.txt) from Future Learn and save it to your R working directory.
  • Assign the data to a matrix, e.g. using the.data <- as.matrix(read.table({"Energy19.txt "}))
  • The variable of interest is Energy use of appliances (Y). To investigate Y, generate a subset of 300 data, e.g. using: my.data <- the.data[sample(1:671,300),c(1:6)]
  • Using scatter plots and histograms, report on the general relationship between each of the variables X1, X2, X3, X4, X5 and the variable of interest Y. Include 5 scatter plots, 6 histograms, and 1 or 2 sentences for each of the variables, including the variable of interest Y.

2.Transform the data [20 marks]

  • Choose any four from the five variables (X1, X2,..,X5). Make appropriate transformations to the chosen four variables and the variable of interest Y so that the values can be aggregated in order to predict the variable of interest. Assign your transformed data along with your transformed variable of interest to an array (it should be 300 rows and 5 columns). Save it to a txt file titled {"nametransformed.txt"} using write.table(your.data,{"name-transformed.txt"}) where “name” is replaced with your name - you can use your surname or first name.
  • Briefly explain the transformations applied for the selected four variables and the variable of interest. (1- 2 sentences each)

3.Build models and investigate the importance of each variable [40 marks]

(i) Download the AggWaFit718.R file (from Future Learn) to your working directory and load into the R workspace using, source({"AggWaFit718.R"})

(ii) Use the fitting functions to learn the parameters for

  • A weighted arithmetic mean (WAM)
  • Weighted power means (WPM) with p = 0.5, and p = 2,
  • An ordered weighted averaging function (OWA), and
  • A Choquet integral.

(iii) Include two tables in your report - one with the error measures and correlation coefficients, and one summarising the weights/parameters and any other useful information learned for your data.

(iv) Compare and interpret the data in your tables. Comment on

  • a. How good the model is,
  • b. The importance of each of the variables (the four variables that you have selected),
  • c. Any interaction between any of those variables (are they complementary or redundant?) and
  • d. Better models favour higher or lower inputs. (1-3 paragraphs for part 3(iv))

4.Use your model for prediction [20 marks]

  • (i) Choose your best fitting model. Using your best fitting model, predict the Energy use of appliances for the following input X1=18; X2=44; X3=4; X4=74.8; X5=31.4.
  • (ii) Give your result and comment on whether you think it is reasonable. (1-2 sentences).
  • (iii) Comment on the best conditions (in terms of your chosen four variables) under which a low Energy use of appliances will occur. (1-2 sentences).


1. Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix. Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788. http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction

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