UC00183_Data-Driven_Climate_Resilience_for_Melbourne_Integrating_Sensors_Canopy_Coverage_and_Population_Growth

[harshith27-02]

This pull request includes the final updates and enhancements for the project:

• Completed regression modelling.
• Implemented Prophet forecasting for climate variables at selected sensor sites.
• Developed the Heat Vulnerability Index (HVI) and applied classification to group areas into Low, Medium, and High vulnerability.
• Added final analysis, conclusions, and recommendations in Markdown/HTML.
• Created and added a structured JSON project file describing datasets, models, and methods used.
• Created html files as well

[PJRyn]

Hey Harshith,
Fantastic use case! You really took on a multifaceted use case that could have easily been split up into smaller ones and you handled it really well, taking on a big challenge. To go through the things I needed to check for: First you had followed the required API calls for the data and used the proper template with a good introduction and import of the data. You also followed the Australian English standards with a clear and professional authorial voice. The use case also was easy to follow and logically laid out. I really liked the use of bold text to convey your findings and highlight important points. You also made the use case actionable with your recommendations provided. You have also documented your code well by explaining what you are doing and used good naming conversions as well as commented on your code. That made it all very easy to follow. The use of machine learning methods like random forest and KNN were a great touch and you explained them and their insights well. The visualisations were easily readable and explained well and labeled correctly (where needed).
Overall, the use case was great! It is to a high standard and goes into great detail making the most out of the datasets used. Really great work!

[Nidhi-kanchepalle]

Hi Harshith,

The use case is well-presented with detailed analysis. Australian English is consistently used, the correct template is followed, and the pull request has been raised in the right folder. Your visualisations are neat and clear, with proper legends, and the data has been accessed using API.

Incorporating population growth forecast data by suburbs is such an interesting idea. It adds a forward-looking perspective to the analysis, showing not just the current state of environmental stress but also how it may evolve in the future.

Overall, the code file is well structured, and the insights from each visualisation are clearly and neatly summarised, making the use case feel like a great tutorial. The conclusion and recommendations are thoughtful, practical, and helpful. Overall, your work meets all the standards—amazing job!

[harshith27-02]

Thank for you for the feedback merging to master...