modeling elevator data colection - Cristobal Cordova

[crcordova]

This pull request includes the implementation for the elevator demand prediction task.

• Flask API with endpoints to register elevator states and demands
• Domain modeling with SQLAlchemy (new file)
• ML-ready dataset generation endpoint based on timestamp and holiday features
• README explanation of domain modeling, assumptions, and ML pipeline rationale

Repo forked from: https://github.com/Citric-Sheep/devtest
My fork: https://github.com/crcordova/Elevator-DataScience

Looking forward to your feedback!

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 35%

Reasoning: The pull request shows signs of human authorship, including personalized metadata, manual implementation patterns, and context-aware explanations. However, there are some notable features that could suggest the assistance of an AI system, particularly in the README generation, which contains structured bullet points and templated explanation formats. Overall, the content blends technical implementation with personalized development, which makes it likely human-authored with potential AI-assisted writing.

Key Indicators:

• Personalized content: The author refers to their name ("Cristobal Cordova") and their fork of the repository, which indicates a real user identity and contextual involvement.
• Custom implementation: The Flask application and SQLAlchemy models are implemented in a style consistent with human problem-solving, showing thoughtful choices in the database schema and endpoint design. There are also bugs or incomplete code (e.g., new_demand and new_state lines not placed within route functions), which AI typically avoids as it tends to produce syntactically complete code.
• README writing style: While structured and clear, the README has a templated feel, which could be AI-assisted. However, the assumptions and modeling discussions reflect a deeper understanding than typical AI outputs without prompt.
• Metadata and project structure: The inclusion of .gitignore and init.py placeholders is common boilerplate generated by tools, not necessarily AI, and don't indicate authorship strongly.
• No excessive verbosity or formatting overkill, which are often telltale signs of AI-generated PRs or documentation.

In summary, the pull request appears mostly human-authored, potentially with assistance from AI in documentation or code snippets.

✅ No strong indicators of AI generation detected