Dr. V.S. Kontogiannis 5DATA002W 2021/22
University of Westminster
School of Computer Science & Engineering

5DATA002W Machine Learning & Data Mining – Coursework (2021/22)
Module leader Dr. V.S. Kontogiannis
Unit Coursework
The current version of CW can be considered as provisional, as it needs to be moderated
by external examiner. Therefore, it may be subjected to slight changes following module
leader’s agreement for such amendments. If there are any changes, students will be
informed.
Weighting: 50%
Qualifying mark 30%
Description
Show evidence of understanding of various Machine Learning/Data Mining
concepts, through the implementation of clustering & regression algorithms
using real datasets. Implementation is performed in R environment, while
students need to discuss important aspects related to these problems and perform
some critical evaluation of their results.
Learning Outcomes Covered
in this Assignment:
This assignment contributes towards the following Learning Outcomes (LOs):
• Suitably prepare a realistic data set for data mining / machine learning
and discuss issues affecting the scalability and usefulness of learning
models from that set
• Evaluate, validate and optimise learned models
• Effectively communicate models and output analysis in a variety of
forms to specialist and non-specialist audiences

Handed Out: 17/02/2022
Due Date 03/05/2022, Submission by 13:00
Expected deliverables Submit on Blackboard only one pdf file containing the required details. All
implemented codes should be included in your documentation together with the
results/analysis/discussion.
Method of Submission:

Electronic submission on BB via a provided link close to the submission time.

Type of Feedback and Due
Date:
Feedback will be provided on BB, after 15 working days
Assessment regulations
Refer to section 4 of the “How you study” guide for undergraduate students for a clarification of how you are assessed,
penalties and late submissions, what constitutes plagiarism etc.
Penalty for Late Submission
If you submit your coursework late but within 24 hours or one working day of the specified deadline, 10 marks will be
deducted from the final mark, as a penalty for late submission, except for work which obtains a mark in the range 40 – 49%,
in which case the mark will be capped at the pass mark (40%). If you submit your coursework more than 24 hours or more
than one working day after the specified deadline you will be given a mark of zero for the work in question unless a claim of
Mitigating Circumstances has been submitted and accepted as valid.
Dr. V.S. Kontogiannis 5DATA002W 2021/22
It is recognised that on occasion, illness or a personal crisis can mean that you fail to submit a piece of work on time. In such
cases you must inform the Campus Office online with a mitigating circumstances form, giving the reason for your late or
non-submission. You must provide relevant documentary evidence with the form. This information will be reported to the
relevant Assessment Board that will decide whether the mark of zero shall stand. For more detailed information regarding
University Assessment Regulations, please refer to the following website: https://www.westminster.ac.uk/current-
students/guides-and-policies/assessment-guidelines/mitigating-circumstances-claims
Instructions for this coursework
During marking period, all coursework assessments will be compared in order to detect possible cases of
plagiarism/collusion. For each question, show all the steps of your work (codes/results/discussion). In addition, students need
to be informed, that although clarifications for CW questions can be provided during tutorials, coursework work has to be
performed outside tutorial sessions.

Coursework Description

Clustering Part
In this assignment, we consider a set of observations on a number of white wine varieties involving their
chemical properties and ranking by tasters. Wine industry shows a recent growth spurt as social drinking is on
the rise. The price of wine depends on a rather abstract concept of wine appreciation by wine tasters. Pricing of
wine depends on such a volatile factor to some extent. Another key factor in wine certification and quality
assessment is physicochemical tests which are laboratory-based and takes into account factors like acidity, pH
level, presence of sugar and other chemical properties. For the wine market, it would be of interest if human
quality of testing can be related to the chemical properties of wine so that certification and quality assessment
and assurance process is more controlled. One dataset (whitewine_v2.xls) is available of which is on white wine
and has 4710 varieties. All wines are produced in a particular area of Portugal. Data are collected on 12 different
properties of the wines, one of which is Quality (i.e. the last column), based on sensory data, and the rest are on
chemical properties of the wines including density, acidity, alcohol content etc. All chemical properties of wines
are continuous variables. Quality is an ordinal variable with possible ranking from 1 (worst) to 10 (best). Each
variety of wine is tasted by three independent tasters and the final rank assigned is the median rank given by the
tasters.

Description of attributes:
1. fixed acidity: most acids involved with wine or fixed or non-volatile (do not evaporate readily)
2. volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant,
vinegar taste
3. citric acid: found in small quantities, citric acid can add ‘freshness’ and flavour to wines
4. residual sugar: the amount of sugar remaining after fermentation stops, it’s rare to find wines with less
than 1 gram/litre and wines with greater than 45 grams/litre are considered sweet
5. chlorides: the amount of salt in the wine
6. free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved
gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine
7. total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly
undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and
taste of wine
8. density: the density of water is close to that of water depending on the percent alcohol and sugar
content
9. pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most
wines are between 3-4 on the pH scale
10. sulphates: a wine additive which can contribute to sulphur dioxide gas (S02) levels, which acts as an
antimicrobial and antioxidant
11. alcohol: the percent alcohol content of the wine
12. Output variable (based on sensory data): quality (score between 0 and 10)


1st Objective (partitioning clustering)

You need to conduct the k-means clustering analysis of this white wine dataset problem. The dataset of 4710
wine samples is defined by 11 attributes (i.e. input variables) and one output (i.e. quality). There are 4 quality
classes. In this assignment, do not attempt any merging of adjacent classes which have few samples. In this
specific clustering part, initially the analysis will be performed with all initial features, as the main aim is to
assess different clustering results under the initial conditions. In the next phase however, principal component
analysis (PCA) will be applied to reduce the input dimensionality and the newly produced dataset will be
clustered using the same k as the winner case from the initial phase. Before conducting the k-means, perform the
Dr. V.S. Kontogiannis 5DATA002W 2021/22
following pre-processing tasks: scaling and outliers removal and briefly justify your answer. (Suggestion: the
order of scaling and outliers removal is important. The outlier removal topic is not covered in tutorials, so you
need to explore it yourself). Define the number of cluster centres (via manual & automated tools). The
automated tools should include NBclust, Elbow and one from Gap statistics or silhouette methods. You need to
provide the related R-outputs and discussion on these outcomes. Using all input variables, perform a kmeans
analysis with k=2, 3 &4. For each of the above k-means attempts, show all related R-based kmeans outputs,
including information for the centres as well as the ratio of between_cluster_sums_of_squares (BSS) over
total_sum_of_Squares (TSS). In addition, for each of these k-means attempts, check your produced cluster
outcome against the information obtained from 12th column and provide the related results/discussion (evidence
of a “confusion-like” matrix (CM) and calculation of the accuracy/recall/precision indices from it). Choose the
best “winner” clustering case (justify your response) and briefly explain the meaning of
accuracy/recall/precision indices.
As this is a typical multi-dimensional, in terms of features problem, you need also to apply the PCA method to
this wine dataset. You need to show all related to PCA R-outputs. Create a new “transformed” dataset with
principal components (PC) as attributes. Choose those PCs that provide a cumulative score > 96%. Apply,
kmeans analysis on this “new transformed” dataset using same k as the winner from the previous step. Show the
related R-outputs of this kmeans analysis. Discuss the performance of this “PCA-based” kmeans model by
calculating the related BSS, ratio BSS/TSS and within_cluster_sums_of_squares (WSS) indices and compare
these produced indices against the related ones from the winner model (i.e. all attributes) from the previous
stage.
Write a code in R Studio to address all the above issues (codes/results/discussion need to be included in your
report). At the end of your report, provide also as an Appendix, the full code developed by you. The usage of
kmeans R function is compulsory.

(Marks 40)

Energy Forecasting Part (part of Work Based Learning activity)
Buildings represent a large percentage of a country’s energy consumption and associated greenhouse gas
emissions. The energy needed in order to maintain internal conditions within buildings, is responsible for a
significant portion of the overall energy usage and greenhouse emissions. Thus, improving energy efficiency in
buildings is of great importance to our overall sustainability. Over the past few decades, a lot of research has
been carried out in order to improve building energy efficiency through various techniques and strategies. The
forecasting of energy usage in an existing building is essential for a variety of applications like demand
response, fault detection & diagnosis, optimization and energy management. This is a typical time-series based
application. Data-driven forecasting models typically include two main approaches; statistical and machine
learning based schemes. The statistical approach typically applies a pre-defined mathematical function and has
shown good performance for medium to long term energy forecasting. In addition, such models have shown
acceptable performance for short-term forecasting of consumption electricity loads. Machine learning approach
in contrast, typically applies an algorithmic approach (which may non-linearly transform the data), in order to
provide a forecast.

For this forecasting part of the coursework, you will be working on a specific case study, which involves a real-
life organisation and a real dataset. More specifically, in collaboration with the Estates Planning & Services
Department, at University of Westminster, we have been supplied (via LG Energy Group) with the hourly
electricity consumption data (in kWh) for the University Building at 115 New Cavendish Street London for the
years 2018 and 2019. Although full data information has been supplied to us, you will use only a small portion
of that information in this coursework. The provided (UoW_load.xlsx) file includes daily electricity
consumption data for three hours (11:00, 10:00 & 09:00) for the 2018 and partly 2019 periods (in total 500
samples). The objective of this question is to use a multilayer neural network (MLP-NN) to predict the next
step-ahead (i.e. next day) electricity consumption for the 11:00 hour case. The first 430 samples will be used as
the training data, while the remaining ones will be used as the testing set.

2nd Objective (MLP)

You need to construct an MLP neural network for this forecasting problem. The definition of the input vector
for NNs is a very important component for energy forecasting analysis. Therefore, initially you need to provide
a brief discussion of the various schemes/methods used to define this input vector in electricity load forecasting
problems. (Suggestion: consult related literature and add some relevant references). In this specific forecasting
part, however, you are going to utilise only the “autoregressive” (AR) approach, i.e. time-delayed values of the
11th hour attribute as input variables. As the order of this AR approach is not known, you need to experiment
with various (time-delayed) input vectors and for each one of these cases you need to construct an input/output
matrix (I/O) for the MLP training/testing (using “time-delayed” electricity loads). Experiment with various input
vectors up to (t-4) level. According to literature, the electricity consumption forecast depends also on the (t-7)
(i.e. one week before) value of the load. Thus, in your “AR” analysis, you need also to investigate the influence
Dr. V.S. Kontogiannis 5DATA002W 2021/22
of this specific time-delayed load to the forecasting performance of your NN models. In addition, to this
“classic” AR approach, you need also to consider additional input vectors by including information from the
10th and 9th hour attributes. In that case, your NN models could be considered as NARX (nonlinear
autoregressive exogenous) models. Each one of these I/O matrices needs to be normalised, as this is a standard
procedure especially for this type of NN. You need to explain briefly the rationale of this normalisation
procedure for this specific type of NN. For the training phase, you need to experiment with various MLPs,
utilising these input vectors and various internal network structures (such as hidden layers, nodes, learning rate,
activation function, etc.). For each case, the testing performance (i.e. evaluation) of the networks will be
calculated using the standard statistical indices (RMSE, MAE and MAPE). Create a comparison table of their
testing performances (using these specific statistical indices). Briefly explain the meaning of these three stat.
indices. From this comparison table, check the “efficiency” of your best one-hidden layer and two-hidden layer
networks, by checking the total number of weight parameters per network. Briefly, discuss which approach is
more preferable to you and why. Finally, provide for your best MLP network, the related results both
graphically (your prediction output vs. desired output) and via the stat. indices. Write a code in R Studio to
address all these requirements. Show all your working steps (code & results, including comparison results from
models with different input vectors and internal structure). As everyone will have different forecasting result,
emphasis in the marking scheme will be given to the adopted methodology and the explanation/justification of
various decisions you have taken in order to provide an acceptable, in terms of performance, solution. Full
details of your results/codes/discussion are needed in your report. At the end of your report, provide also as an
Appendix, the full code developed by you. The usage of neuralnet R function for MLP modelling is compulsory.


(Marks 60)
Coursework Marking scheme
The Coursework will be marked based on the following marking criteria:
1st Objective (partitioning clustering)
• Pre-processing tasks (2 marks for scaling and 3 marks for outliers removal) 5
• Define the number of cluster centres by showing all necessary steps/methods via
manual & automated tools (2 marks for each one of these automated tools) 6
• K-means analysis for each k attempt (2 marks for each k attempt) (all attributed used) 6
• Evaluation of the produced outputs against 12th column
(1 mark for the “CM” table, 6 marks for calculation of these requested indices) 7
• Define the final “winner” cluster case and provide brief explanation of evaluation indices 4
• Apply a PCA for this white wine dataset (2 marks). Create a new dataset with
those PCs with a cumulative score > 96%, as attributes (2 marks). 4
• Apply kmeans on this new “PCA-based” dataset 2
• Discuss the performance for this “PCA-based” dataset though the calculation of WSS,
BSS, BSS/TSS indices (3 marks) and compare them against the ones produced from
the previous “winner” model utilising all attributes (3 marks). 6


2nd Objective (MLP)

• Brief discussion of the various methods used for defining the input vector in electricity
load forecasting problems 5
• Evidence of various adopted input vectors and the related input/output matrices for both
AR (5 marks) and NARX (4 marks) based approaches 9
• Evidence of correct normalisation (3 marks) and brief discussion of its necessity (3 marks) 6
• Implement a number of MLPs for the AR approach, using various structures
(layers/nodes)/input parameters/network parameters and show in a table, their
performances comparison (based on testing data) through the provided stat. indices.
(4 marks for structures with different input vectors, 8 marks for different internal NN
structures and 4 for the comparison table). 16
Repeat the above step for the NARX also approach (2 marks for structures with different
input vectors, 4 marks for different internal NN structures and 2 for the comparison table). 8
• Discussion of the meaning of these stat. indices 6
• Discuss the issue of “efficiency” with your two best NN structures 4
• Provide your best results both graphically (your prediction output vs. desired output)
and via performance indices (3 marks for the graphical display and 3 marks for showing
the requested statistical indices) 6