Degen Villa

Degen Villa is a small Python project that collects, analyzes, and surfaces new real-estate (or NFT/market) listings and insights to help rapid decision-making. It combines lightweight data processing, a trained model artifact, and simple scripts/notebooks for exploration and local usage.

Project Description

Degen Villa provides tooling to ingest listing data, run analysis and scoring (via a saved model), and expose quick results locally. The repository includes a runnable script (app.py), analysis notebooks, and a serialized model used for scoring.

Key points:

• Lightweight, local-first tooling for rapid experimentation.
• Uses a persisted model in database/birdeye_new_listings.joblib for ranking/scoring listings.
• Notebook-driven exploration in Notebooks/degen.ipynb.

Features

• Data ingestion helpers and simple preprocessing.
• A pre-trained model snapshot for scoring (database/birdeye_new_listings.joblib).
• Example scripts and notebooks to reproduce analyses and run local demos.

Requirements

• Python 3.9+ (recommended). See requirements.txt for pinned dependencies.

Installation (Windows - PowerShell)

1. Create virtual environment (if not present):

python -m venv degenvilla

2. Activate it:

.\degenvilla\Scripts\Activate.ps1

3. Install dependencies:

pip install -r requirements.txt

Usage

• Run the demo script:

python app.py

• Open and run analysis in: Notebooks/degen.ipynb.

Notes on entry points:

• app.py — lightweight runner / demo harness.
• degenvilla.py — project helper utilities and orchestration.

Data

• Serialized model: database/birdeye_new_listings.joblib — used for scoring new listings.
• Place any additional raw data in a local data/ directory (create if needed).

Development

• Use the included virtual environment under the degenvilla/ folder or create your own.
• Run notebooks for exploratory work. Keep model retraining and heavy processing off the user machine unless necessary.

Project Status & Roadmap

• Status: Prototype / exploratory.
• Next steps:
  • Add end-to-end ingest pipeline with validation.
  • Add unit tests and CI configuration.
  • Provide a simple web UI or API for listing queries.

Note to Collaborators

This repository is an experimental workspace for quick iteration. If you're reviewing or contributing:

• Expect some exploratory scripts and notebook-driven code.
• Check requirements.txt for the environment used during development.
• Treat database/birdeye_new_listings.joblib as a snapshot artifact; retrain with care and version any new model files.

If you want me to add a contribution guide, tests, or CI, tell me which direction (web UI, API, or batch pipeline) and I will scaffold the next steps.

---

Author: Project owner X.COM/SENIORMANFM