docs

Documentation for the Dublin Bikes project, including the machine-learning chapter (machine-learning-model.md).

Project Report (course requirements)

A textual report of MAXIMUM 25 pages + Title Page in Times New Roman, 11pt, including the following sections, with recommended content (more content can be added, if space allows).

Title Page

• Authors; agreed percentage of contribution of each author; type of contribution (code—specifying the features, report, management)
• URL to a 6–8 minutes (MAX) screen recording video in which:
  • 3–5 minutes: you show and comment the app features with your voice: it needs to clearly show the address of the page so that it can be verified that it's on EC2.
  • ~3 minutes: each group member presents for 1 minute their contributions.
• URL of GitHub project
• URL of product and sprint backlogs
• URL to a document in GitHub for Generative AI chats (one for each student)

Overview

• Project objectives
• Target users
• Main features (with main screenshot of the final app)

Requirements

• Description of the process adopted for requirements elicitation
• Mockup(s) of the app (just most relevant ones)
• List of user stories and associated acceptance criteria

You can add a link to a specific folder of your GitHub repository where you can have additional material associated with the elicitation process, e.g., mockups, personas, interview transcripts, besides those shown in the report.

Architecture and Design

• Diagram of the overall architecture
• Class diagram of the web application and its main elements
• Sequence diagram of the interactions between web application elements
• Description of the aforementioned diagrams
• Database design, with description of design choices

You can add a link to a specific folder of your GitHub repository where you can have additional material associated with architecture and design (e.g., additional diagrams)

Machine Learning Model

• Selected features, feature extraction/data cleaning process, and target variables
• Training and testing process for model selection
• Results and reflection

The code used to train and test the ML model should be shared in the GitHub repository.

Testing

• Description of the testing activities performed and results

Process

• Description of the organisation and management of the project, including adopted tools
• For each sprint, include the following:
  • Implemented features/completed work and design decisions in narrative form
  • Burndown chart
  • Sprint review
  • Sprint retrospective (max 250 words)

IMPORTANT: the sprint retrospective (max 250 words) should be submitted by each group at the end of each sprint using this form:

https://forms.gle/jeAqgXarzr7R6YBU8

Conclusion

• Final remarks, and future works.

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The quality of the report will be evaluated in terms of clarity, completeness, and in terms of evidence of a clear and well structured process.