ASSESSMENT DETAILS SIT718 Real World Analytics Assessment Task 3: Problem Solving Using aggregation functions for data analysis Deakin University
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
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.
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 . The dataset includes 5 variables, denoted as X1, X2, X3, X4, X5, and Y, described as follows:
1. Understand the data [20 marks]
2.Transform the data [20 marks]
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
(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
4.Use your model for prediction [20 marks]
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