Cardiff School of Computer Science and Informatics
Coursework Assessment Pro-forma

Module Code: CMT307
Module Title: Applied Machine Learning
Lecturer: Yuhua Li
Assessment Title: Implementation and Evaluation of a Case Study Using Machine
Learning Techniques
Assessment Number: 1
Date Set: 3 December 2021
Submission Date and Time: 10 January 2022 at 9:30am
Return Date: 11 February 2022

This assignment is worth 50% of the total marks available for this module. If coursework is
submitted late (and where there are no extenuating circumstances):
1 If the assessment is submitted no later than 24 hours after the deadline,
the mark for the assessment will be capped at the minimum pass mark;
2 If the assessment is submitted more than 24 hours after the deadline, a
mark of 0 will be given for the assessment.
Your submission must include the official Coursework Submission Cover sheet, which can be
found here:
https://docs.cs.cf.ac.uk/downloads/coursework/Coversheet.pdf

Submission Instructions

Your submission must include:
• A filled-in copy of the official Coursework Submission Cover sheet.
• A Jupyter Notebook (.ipynb) file containing all your code and execution outputs/figures.
• A typeset PDF report (see next section for details).

Ensure that your student number is included on the report and as a comment at the top of
each Python file that makes up your submission.

You must submit to Learning Central three files (each named using your student number in
the format of [student number]_CW1.pdf, e.g., C1234567_CW1.pdf) which contains the
following files:

Description Type Name
Cover sheet Compulsory One PDF (.pdf) file [student number]_CW1.pdf
Assessment 2 Compulsory One Jupyter Notebook (.ipynb)
file.
[student number]_CW1_code.ipynb
Report Compulsory One PDF (.pdf) file containing
your calculation for question 1
and your report for question 2.
[student number]_CW1_report.pdf

Before submitting your Jupyter Notebook file (.ipynb), make sure to restart the kernel and
execute each cell such that all outputs and figures are visible. Any code submitted will be
run in Python 3 and must be submitted as stipulated in the instructions above.

Any deviation from the submission instructions above (including the number and types of
files submitted) will result in a mark of zero for the assessment or question part.

You can submit multiple times on Learning Central. ONLY files contained in the last attempt
will be marked, so make sure that you upload all files in the last attempt.

Staff reserve the right to invite students to a meeting to discuss coursework submissions


Assignment

There are two questions in this coursework, marks for each part are in brackets.

Question 1
Your algorithm gets the following results in a classification experiment, where in the table,
‘Id’ is the index number, ‘Target’ is the ground truth that the classifier aims to achieve,
‘Prediction’ is the predicted results. Please compute the confusion matrix, precision, recall,
f1-measure and accuracy manually (without the help of your computer/Python, please
provide all steps and formulas). Include the process to get to the final results. [10%]

Id Target Prediction
1 True True
2 True True
3 True False
4 True True
5 True True
6 True False
7 True True
8 True True
9 True True
10 False False
11 False False
12 False False
13 True True
14 True False
15 True True
16 False False
17 False False
18 False True
19 False True
20 False False


Question 2
In this question, you will develop machine learning models to predict e-commerce visitors’
purchasing intention. The given dataset Coursework_1_data.csv, which can be downloaded
from Learning Central, contains shoppers’ online activity information including clickstream
and session information data, where the last column Revenue represents visitors’
purchasing intention. Your tasks will include data exploration, data pre-processing, machine
learning method selection and implementation, and model performance evaluation. In
addition to aforementioned tasks, you will write a concise report (around 1000 words,
excluding tables and figures) to summarise your work and provide an analysis and
discussion of the results.

i) Data exploration [10%]:
Conduct exploratory inspection of the dataset to provide a good understanding of
data characteristics.
ii) Data pre-processing [30%]:
Carry out well thought pre-processing procedures to prepare the data into a form
that is likely to lead to better performance.
iii) Model implementation [30%]:
Select three representative classification methods with a clear justification of your
choice. Implement and optimise the classifiers for your chosen classification
methods.
iv) Performance evaluation [10%]:
Organise the data in a suitable form to ensure the trained classifiers to provide
reliable results. Evaluate models using suitable performance metrics.
v) Result analysis and discussion [10%]:
Provide an insightful analysis and comparison on results that you obtained from
above steps, draw conclusions based on the results and analysis.

Learning Outcomes Assessed

Completion of this coursework allows students to demonstrate that they can:
1. Implement and evaluate machine learning methods to solve a given task
2. Explain the basic principles underlying common machine learning methods
3. Choose an appropriate machine learning method and data pre-processing strategy
to address the needs of a given application setting
4. Reflect on the importance of data representation for the success of machine learning
methods


Criteria for assessment

Question 1: Your answer needs to provide all steps and formulas, including the process to
get to the final results.

Question 2: Your submitted Jupyter Notebook file (.ipynb) will be marked in conjunction
with your Report. Credit will be awarded against the following criteria.
Criteria Fail (0-49%) Pass (50-59%) Merit (60-69%) Distinction(>=70%)
Data
exploration
[10%]
No or arbitrary
data
exploration.
Suitable but limited
data exploration.
Good data
exploration but
miss some
insightful
analysis.
Thorough and
insightful data
exploration.
Data pre-
processing
[30%]
No or very little
data pre-
processing.
Some necessary pre-
processing is
conducted.
Adequate pre-
processing to
prepare the
data for model
development.
Extensive pre-
processing to deal
with all aspects of
non-ideal
characteristics of
the data with an
aim to achieve a
best classification
performance.
Model
implementation
[30%]
Less than 3
classification
methods are
implemented.
Classifiers are
not correctly
implemented
and optimised.
Three different
classifiers are
implemented but
not
properly/sufficiently
trained.
Three
classification
methods are
selected to give
a good
representation
of various
classification
techniques. All
implemented
classifiers are
properly
trained and
optimised.
Three classification
methods are
carefully selected
with a good
justification of your
selection, which
give a good
representation of
various
classification
techniques. All
classifiers are
excellently
implemented,
properly trained
and systematically
optimised.
Performance
evaluation
[10%]
Little
performance
evaluation.
Performance
evaluation
using arbitrary
metrics.
Performance
evaluation using
metrics without
considering data
characteristics.
Good
performance
evaluation with
suitable
metrics.
Use most suitable
performance
metrics to assist the
selection of the best
model on
characteristics of
this dataset.
Result analysis
and discussion
[10%]
No or little
meaningful
result analysis
and discussion.
General result
analysis and
discussion. Vague
conclusion.
Good result
analysis and
discussion but
lack of depth.
Insightful result
analysis and
discussion,
conclusion clearly
drawn.
Feedback and suggestion for future learning

Feedback on your coursework will address the above criteria. Feedback and marks will be
returned via Learning Central. There will be opportunity for individual feedback during an
agreed time.

Feedback for this assignment will be useful for subsequent skills development, such as data
science, natural language processing and deep learning (which will be studied during the