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CMT307
Applied Machine Learning
Assessment Title: Implementation and Evaluation of a Case Study Using Machine
Learning Techniques
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.
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.