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Subject Code – ITNPBD6
Subject Name – Machine Learning
University Name – University of Stirling, Scotland, UK


Machine Learning Assignment
Machine learning is a sub-branch of artificial intelligence that allows systems to learn and develop without being explicitly programmed. Machine learning is concerned with the creation of computer programs that can access data and learn for themselves.

In today’s technical world, Machine Learning has become important because it allows businesses to see trends in customer behavior and business operating patterns while also assisting in the development of new goods. Machine learning is at the heart of many of today’s most successful businesses, like Facebook and Google. For many businesses, machine learning has become a crucial competitive differentiation.

Types of Machine Learning

Supervised Learning
Supervised learning is the most basic type of machine learning. Supervised learning is a method of developing artificial intelligence (AI) that involves training a computer algorithm on input data that has been labeled for a certain output.

Unsupervised Learning
The use of artificial intelligence (AI) systems to find patterns in data sets including data points that are neither categorized nor labeled is known as unsupervised learning. Unsupervised machine learning has the advantage of being able to work with unlabeled data.

Reinforcement Learning
Reinforcement learning is a branch of machine learning that studies how intelligent agents should operate in a given environment to maximize the concept of cumulative reward.

University of Stirling, UK
The University of Stirling is a reputable public university in Sterling, Scotland. At the university, students can pursue bachelor’s and master’s degree programs. Accountancy, biology, business, computer science, creative writing, media, and politics are among the subjects covered by the university. The Association of MBAs (AMBA) has accredited the university, and it is a member of the Association of Commonwealth Universities (ACU). In 2016, the institution was awarded five stars (the highest rating) in QS World’s university rankings, which included employability, teaching, inclusion, facilities, and internationalization.
Assessment Details
Please refer to Stircome Case Study for this assignment.
Your assignment is to answer that question using machine learning techniques and produce a system that would be able to tell StirCom which customers it should target the marketing at.
You can use Orange, Python, R, or any data mining package of your choice. The data
for the assignment is in a file stircom.csv provided for you.

Note – This is an individual assignment.

You should submit a report describing the modeling process you followed and your
results. You should try to frame the problem in the form of the CRISP-DM framework to
better facilitate the discussion. Refer to the relevant CRISP-DM stages at each stage
of your report. You do not need to submit code or data. The report is worth 100 marks
in total and must cover the following (with weightings per section as shown):

Business Understanding 10%
Describe the task you were given: is it clustering, classification, or regression?;
describe the data you received and the requirements of the finished system, including
why machine learning is suitable for this task. Define any terminology that you will
use in the report (for example, model, variable, task, etc.). Comment on any issues
around ethics or trust that may be relevant to the framing of the problem.

Data Summary 10%
List the variables that you found in the file provided by the company. For each one,
say whether it should be treated as categorical or numeric; nominal, ordinal,
continuous or discrete; and whether or not it is likely to be of use in building the

Data Preparation 10%
Describe what you did with the data prior to the modeling process. Show histograms
of the data before and after any pre-processing that you carried out.

Modeling 30%
You must use three different techniques and build models with each: these should
include one tree-based model, one based on logistic regression, and one based on
neural networks. Try to make each model perform as well as it can: if you varied the
hyperparameters of a model, show which hyperparameters you varied and how this
impacted the results.

Evaluation 10%
Analyze and describe the level of accuracy the model achieves and the errors your
model makes.

Overall Approach & Creativity 30%
You should adopt a structured approach to the whole process and clearly identify the
five CRISP-DM stages excluding deployment in your report.


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