Applied Modelling and Visualisation Coursework1 Summative Assessment Brief | BPP

AMP General Assessment Guidance

• Your summative assessment for this module is made up of this 2,500-word submission which accounts for 100% of the marks.
• Please note that late submissions will not be marked.
• You are required to submit all elements of your assessment via Turnitin online access. Only submissions made via the specified mode will be accepted, and hard copies or any other digital form of submissions (like via email or pen drive, etc.) will not be accepted.
• For coursework, the submission word limit is 2,500 words. You must comply with the word count guidelines. You may submit LESS than 2,500 words but not more. Word Count guidelines can be found on your programme home page and the coursework submission page.
• Do not put your name or contact details anywhere on your submission. You should only put your student registration number (SRN), which will ensure your submission is recognised in the marking process.
• A total of 100 marks is available for this module assessment, and you are required to achieve a minimum 50% to pass this module.
• You are required to use only the Harvard Referencing System in your submission. Any content that is already published by other author(s) and is not referenced will be considered a case of plagiarism.
• You can find further information on Harvard Referencing in the online library on the Hub.
• BPP University has a strict policy regarding the authenticity of assessments. In proven instances of plagiarism or collusion, severe punishment will be imposed on offenders. You are advised to read the rules and regulations regarding plagiarism and collusion in the GARs and UPPs, which are available on the HUB in the Help and Support section under Documents and Forms.
• Use of AI in assessments is only allowed to review a draft, correct language errors or if specified in the summative assessment brief. If you have used AI for any of these purposes, you should indicate this on the Assignment Cover sheet. For more information regarding acceptable and unacceptable use of AI, please enrol on the Generative AI Foundations course on the HUB.
• You should include a completed copy of the Assignment Cover sheet. Any submission without this completed Assignment Cover sheet may be considered invalid and not marked.

AMP Assessment Brief

For this assignment, you are working as a Data Analytics Consultant for the Aerojet International Airlines and have been asked to prepare a Consultancy Report based on the airline’s passenger ‘satisfaction’ Data Set. This report and your findings will be used in a ‘visually appealing’ presentation to the CEO, Senior Flight personnel and Cabin Crew in the Annual Staff Conference, and it has been proposed that some interactive elements will be placed securely on the company intranet.

Summative Submission

You are provided with a set of data AREOJET DATA_CW1.csv that summarises the levels of passenger ‘satisfaction’. The file contains over 103,000 rows of information from the Aerojet International Airlines database system for the current calendar year. Your objective is to use machine learning principles to model and visualise key data to help staff better understand what factors impacted levels of ‘satisfaction’ for passengers using the airline. Each feature is listed below:

Your summative submission should be a written report in MSWord format (NOT a PDF file) and should be at most 2,500 words. It should describe how applied modelling and visualisation can be used to present summaries of passenger data. Your report will inform a corporate presentation, so it should be appropriately tailored to a rich and varied audience consisting of the CEO, Senior Flight personnel and Cabin Crew. You are also required to carry out independent research into the different categories of ‘satisfaction’ and techniques used to analyse and forecast data in your report.

You must complete all the following tasks:

(ILO1 - Formulate innovative data-driven solutions to commercial problems)

TASK 1: Develop a data-driven solution to the given scenario (ILO1).

The solution must use two analytical models to predict the scale and accuracy of the airline’s data using the Python programming language and relevant Python libraries, taking into consideration the following guidance notes.

Task 1 - Data-Driven Solution Guidance Notes:

You should provide a data-driven solution that:

• Follows an established design methodology (e.g. PPDAC or CRISP-DM), including flowcharts and pseudocode
• Performs an Extract, Transform, and Load (ETL) process (including import, clean and prepare the data for analysis, whilst ensuring that the relevant test, validation and training sets are created).
• Performs Exploratory Data Analysis (EDA) with appropriate visualisations
• Trains and tests TWO analytical models
• Evaluates the models based on your choice of loss function
• Produces appropriate visualisations of your results
• Describes the solution development process

You should choose two from the following models:

• Logistic regression
• Naïve Bayes
• Decision Tree
• Bagging
• Random Forest
• AdaBoost
• XGBoost
• Artificial neural network
• Another appropriate state-of-the-art algorithm

(ILO2 – Critically evaluate the use of algorithms and model when developing analytical solutions)

Task 2: Critically analyse the two models chosen for your solution in Task 1 (ILO2)

Critically analyse the two models chosen for your solution in Task 1, and in particular, the strengths and limitations of each model using the guidance notes provided below with references to the relevant literature.

Task 2 Guidance Notes:

Your critical analysis must also include:

• An explanation of your chosen loss function
• A short discussion of the accuracy metrics
• A summary table of the accuracy metrics of the two chosen models to support the selection of the best model

(ILO3 – Critically appraise the concepts, tools and techniques for data visualisation)

Task 3: Communicate your findings supported by several outputs from Task 1 (ILO3)

Communicate your findings supported by several outputs from Task 1, including graphical outputs such as correlation matrix, heat map, and confusion matrix, using the guidance notes provided below.

Task 3 Guidance Notes:

Your critical appraisal should be based on your findings in Task 1, and must also include:

• An analysis of how the Exploratory Data Analysis (EDA) output guided your selection of the analytical models
• An explanation of the justification for performing EDA and the use of appropriate descriptive statistics and visualisations to understand the results of that analysis
• A recommendation of the use of one model for sustaining or increasing the rate of ‘satisfaction’

Research and Referencing

Your report should include a list of references used to develop the report and research to support the suggested approach. The list should use only the Harvard Referencing System as highlighted in the General Assessment Guidance section of this document. All the figures/tables used in the report must have captions and, wherever needed, properly referenced and explained in your submission.

Suggested report format

• Cover page (University cover sheet)
• Table of Contents
• List of Abbreviations (if appropriate)
• Introduction (Scope and Background)
• Key Factors that impact passenger satisfaction’ 
• Tasks (with Technical Details and Independent Research)
• Recommendations
• Next steps
• References
• Appendix
• The sections in bold contribute to the word count of 2,500 words

Adding your pre-run code to your report prior to uploading to TurnItIn

Locate the report file and embed your Pre-run Python notebook. If you are unable to embed your python notebook in your MS Word document for any reason, you must provide a shared link to the file. This is easily done within Google Colab by selecting the ‘Share button’ in the top right-hand corner of the screen

IMPORTANT: If you do not embed your notebook or provide a link, you will lose marks

AMP Marking Guidebefore