ASSESSMENT DETAILS 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
Purpose
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.
Instructions
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 [1]. The dataset includes 5 variables, denoted as X1, X2, X3, X4, X5, and Y, described as follows:
Assignment Tasks
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]
References:
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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