SWE6204 Machine Learning Assignment: A Comprehensive Analysis of Machine Learning Algorithms and Their Application in Predicting House Prices Using Regression Techniques

Learning Outcomes assessed

LO1: Develop an understanding of a wide selection of Machine Learning Algorithms

LO2: Identify fundamental issues of applying Machine Learning in designing and implementing real-world applications.

LO3: Demonstrate the application of machine learning algorithms to solve real-world problems.

LO4: Critically evaluate the performance of machine learning solutions and identify the scope of improvements and optimisations.

LO5: Identify social, and ethical issues/implications in the application of machine learning.

Task 1 – Machine Learning [40 marks]

a. We all know we have three main types of Machine Learning (ML), such as Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Assume you have given the following scenarios. Your task is to identify what type of ML you can apply to the following scenarios and why explain in your own words. [LO1]                                                                                                               

(4marks)

i. Imagine you work for a financial consulting firm, and one of your responsibilities is to develop a predictive model that can forecast stock prices based on various financial indicators. This model will serve as a valuable tool for investors and traders to make informed decisions about buying or selling stocks, ultimately helping them maximise their returns. 

(1 mark)

ii. Imagine you work for a healthcare organisation, and your objective is to create a patient segmentation plan to optimise and improve patient care and treatment strategies. To effectively tailor medical services, the goal is to identify distinct groups of patients with similar health profiles or medical needs. This segmentation will help the healthcare facility allocate resources efficiently and provide personalised care to each patient group. 

(1 mark)

iii. Imagine that you work for a social media platform, and your role involves creating a system that can automatically identify, and flag inappropriate content posted by users. This system is crucial for maintaining a safe and enjoyable online environment, as it helps in swiftly removing offensive or harmful material from the platform. 

(1 mark)

iv. Imagine you’re tasked with creating an autonomous delivery drone system for a futuristic logistics company. This drone must learn how to efficiently navigate a complex urban environment, follow aviation regulations, and make intelligent decisions on-the-fly. 

(1 mark)

b. What is the difference between K means clustering algorithm and the k nearest neighbors (KNN) classification? [LO1]

(8 marks)

 

c. Can you explain what is a ‘loss’ in machine learning, and how to calculate that for linear regression? [LO2, LO4]                                                                                 

(4 marks)

d. Explain “Overfitting” in Machine Learning. [LO2, LO4]

(8 marks)

e. Given the following training (T) and validation (V) error curves what actions would you take, if any, to improve performance given that m is the number of training pairs being used? Each point of the curve is obtained by training until convergence. Provide an explanation for your reasoning. [LO2, LO4]

i.

(4 marks)

ii.

(4 marks)

 

f. Can you give an example with an explanation of the challenges & risks involved in the application of AI/machine learning in the following table? [LO5]

(8 marks)

 

Task 2 – Predicting House Prices Using Regression Techniques

[60 marks]

Scenario: You are a bachelor’s student enrolled in a Computer Science program at a university in the UK. As part of your undergraduate studies, you have been tasked with a Machine Learning project. The objective of this project is to create a predictive model capable of accurately estimating property prices using a range of input features.

You are provided an example of Python source code to generate a sample dataset comprising details about houses in a specific city, including the size of the house, number of bedrooms, number of bathrooms, location, and other significant features.

Your task is to build a Regression Model that can effectively predict the selling price of a house given its features.

Assignment Tasks:

1. Data Exploration and Pre-processing [LO3]:                                                  

(10 marks)

i. Load/import the dataset from house_prices_dataset.csv, examine its structure, and print the first 20 rows.

(5 marks)

ii. Visualise data for features ‘size’, ‘bedrooms’, ‘location’, and ‘prices’ using appropriate plots or graphs.

(5 marks)

 

2. Model Selection and Evaluation [LO3, LO4]:                                                 

(30 marks)

i. Split the dataset into training and testing sets using an appropriate ratio. For example, split 65% data for training and 35% data for testing.

(5 marks)

ii. Select at least one regression algorithm (e.g., Linear Regression, Decision Tree Regression, Random Forest Regression) to build predictive models.

(10 marks)

ii. Train each model using the training data and evaluate their performance using appropriate evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

(15 marks)

3. Model Fine-tuning and Optimisation [LO3, LO4]:                                         

(10 marks)

i. Perform hyperparameter tuning on the selected regression model using techniques like Grid Search or Random Search.

(10 marks)

4. Conclusion and Presentation:

(10 marks)

i. Summarise the entire project, including the problem statement, data exploration, model selection, optimisation, and interpretation of results.

(5 marks)

ii. Prepare a concise presentation summarising the key findings, challenges faced, and lessons learned during the project.

(5 marks)

Note: You must use Python programming language and feel free to use any machine learning libraries (e.g., scikit-learn, TensorFlow) to complete the assignment. Remember to document your code, provide appropriate comments, and include necessary visualisations to support your analysis.

Note: The following code serves as an example for generating random data using Python. If you already have a specific data source, you may use that instead. To execute this code successfully, you might need to install or import additional libraries or dependencies, depending on your environment.

Assessment Deliverables:

You are required to produce a report (+/- 3000 words) that discusses all the above factors in Task 1 and Task 2.

Formatting requirements

• References list must include a minimum of 5-10 academic sources with a minimum of 3 peer-reviewed academic journals. Harvard referencing format must be used to credit secondary research sources. In-text citations should be included within your discussion (where relevant) using the author-date format and full reference details should be included in your bibliography.
• Diagrams should be captioned and discussed in the body of your report.
• A table of contents should be included.
• Page numbers should be inserted in the centre of the footer.
• The student ID number be placed in the header of each page.

Submission

Please submit to the Turnitin assignment section through Moodle.

Grading

A percentage mark will be provided based on General Assessment Guidelines for Written Assessments. Grading is as follows:

Specific Assessment Criteria/Marking Scheme:

Grade A (70% and above): Students will demonstrate

• An exceptional understanding of machine learning algorithms, including thorough explanations of their selection, application, and implications in various scenarios (LO1-LO4).
• Professional presentation of the code with detailed inline comments, adhering to industry standards.
• Exceptional critical analysis of machine learning challenges, risks, and ethical implications, providing nuanced and well-supported insights (LO5).
• Comprehensive evaluation of model performance with justified optimisation strategies and evidence-based improvements (LO3, LO4).
• Extensive and high-quality research drawing from a wide range of credible academic sources. Academic writing, referencing, and presentation style will be of the highest standard.
• A highly professional, cohesive report that exceeds academic expectations and highlights originality and creativity.

Grade B (60-69%): Students will demonstrate

• An incredibly good understanding of machine learning algorithms, highlighting insightful analysis and accurate application to specified tasks (LO1-LO4).
• A professional code presentation, including meaningful inline comments and documentation.
• Clear critical analysis of machine learning challenges, risks, and ethical considerations (LO5).
• Thorough evaluation of model performance and appropriate fine-tuning strategies, with logical reasoning (LO3, LO4).
• High-quality research, engaging with a range of current and well-selected academic sources. Academic writing, referencing, and presentation will be particularly good.
• A professional report meeting academic standards with minor areas for enhancement.

Grade C (50-59%): Students will demonstrate

• A sound understanding of machine learning algorithms, with satisfactory application and explanations in the tasks (LO1-LO4).
• Clear and acceptable code presentation, including relevant inline comments.
• Adequate analysis of challenges, risks, and ethical issues, though some points may lack depth or breadth (LO5).
• Competent evaluation of model performance and optimisation strategies, though some approaches may be underexplored (LO3, LO4).
• Reasonable research quality, engaging with appropriate academic sources, though referencing and writing may have minor issues.
• A report meeting academic standards, though lacking in polish and sophistication.

Grade D (40-49%): Students will demonstrate

• A basic understanding of machine learning algorithms, with limited application and explanations (LO1-LO4).
• Acceptable code presentation, though comments and documentation may be minimal.
• Superficial analysis of challenges, risks, and ethical considerations, lacking critical depth (LO5).
• Limited evaluation of model performance, with basic optimisation strategies and insufficient justification (LO3, LO4).
• Basic research engagement, with some academic sources cited but lacking depth. Academic writing and presentation may have noticeable deficiencies.
• A report that addresses the brief but is simplistic and lacking coherence in places.

Grade F – Fail (Below 40%): Students will demonstrate

• An insufficient understanding of machine learning algorithms, with inadequate application and explanations (LO1-LO4).
• Poorly presented code, lacking meaningful comments or organisation.
• Minimal or incorrect analysis of challenges, risks, and ethical considerations (LO5).
• Inadequate evaluation of model performance, with little to no evidence of optimisation (LO3, LO4).
• Weak or absent research, failing to engage with credible academic sources. Academic writing, referencing, and presentation will not meet academic standards.
• A report that does not address the assignment requirements or fails to meet the specified learning outcomes.

Glossary:

• Analyse: Break an issue or topic into smaller parts by looking in depth at each part. Support each part with arguments and evidence for and against (Pros and cons)
• Critically Evaluate/Analyse: When you critically evaluate you look at the arguments for and against an issue. You look at the strengths and weaknesses of the arguments. This could be from an article you read in a journal or from a textbook.
• Discuss: When you discuss you look at both sides of a discussion. You look at both sides of the argument. Then you look at the reason why it is important (for) then you look at the reason why it is important (against).
• Explain: When you explain you must say why it is important or not important.
• Evaluate: When you evaluate you look at the arguments for and against an issue.
• Describe: When you give an account or representation of in words.
• Identify: When you identify you look at the most important points.
• Define: State or describe the nature, scope, or meaning.
• Implement: Put into action/use/effect
• Compare: Identify similarities and differences
• Explore: To find out about
• Recommend: Suggest/put forward as being appropriate, with reasons why

General Assessment Criteria for Written Assessments

GENERAL ASSESSMENT GUIDELINES – LEVEL HE6

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