Areeb playground

[AreebAshraf96]

This pull request adds the Educational Equity Study use case notebook and supporting files.

Changes Made

• Added UC00200_Educational_equity_study.ipynb (full analysis, data cleaning, visualisations, regression).
• Exported JSON version: UC00200_Educational_equity_study.json
• Exported HTML version: UC00200_Educational_equity_study.html
• Included supporting datasets: dv309_schoollocations2021.csv, public_transport_stops.geojson, school_stop_all_records.csv

Impact

• Provides a complete, reproducible notebook for the Melbourne educational equity analysis.
• Enables both interactive (ipynb) and static (html/json) sharing of results.
• Supports future group use cases with cleaned datasets and clear visualisations.

Next Steps

• Review outputs and merge into main.
• Plan for integration with other use cases (e.g., healthcare accessibility).

[MuzamilRafiq]

Hi Areeb,
Great job on this submission — the analysis and insights are excellent. Just two quick points for improvement:
CSV files: Please remove the large CSV files from the submission. These can instead be uploaded to GitHub and accessed directly from there, which will make the notebook lighter and ensure better accessibility for others.

File paths: I noticed absolute paths like
file_path = r"C:\Users\hp\City_Of_Melbourne_DataScience\school_stop_all_records.csv"

This will only work on your local machine. For portability, please switch to relative paths (e.g., ./school_stop_all_records.csv) or link to the GitHub-hosted dataset.
Once those are adjusted, the notebook will be fully accessible and reproducible for all users.

[jennynguyen2112]

Hi Areeb,

Great work with your use case. Below are my comments based on the pull request checklist:

• Dataset Import: Datasets are accessed through a Github link. However this is not the practice that the team is using. Instead please try to access the datasets through APIs (school location dataset can be accessed through Victoria dept of Education API), or you could upload the datasets to Dependencies folder (MOP code-datascience-usecases-Dependencies). Your code should access the dataset from there.
• Code Quality & Readability: Code is well-structured. Variable names are logical and easy to follow.
• Use Case Naming Conventions: Use case naming conventions are followed with the code file.
• Pull Request Location: Pull request is correctly put in Playground folder
• Language: Please note to change wordings in your markdowns from US English to Aus (for example: analyze -> analyse)
• Visualisation: All visualisations have proper legends, labels and interpretations
• Tutorial quality: Use case is formatted with clear structure of a tutorial, containing explanation for each code chunk and insights from visualisations
• Use Case Template: Correct template is used. However, please change the header, it is currently "1.1 Introduction to the Use Case for Project B". Please rectify it into the name of your use case which is "Educational Equity Study".

Thank you Areeb!

[jennynguyen2112]

Thank you for addressing the comments Areeb!

[MuzamilRafiq]

Hi Areeb,
Thanks for the resubmission, your work looks good and is now ready to be merged. I’ve gone ahead and approved the request.