Instructions to use DaProgammer/crypto_radar_brain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use DaProgammer/crypto_radar_brain with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("DaProgammer/crypto_radar_brain", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
File size: 977 Bytes
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license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: tabular-classification
library_name: sklearn
tags:
- scikit-learn
- finance
- cryptocurrency
- sentiment-analysis
---
## Model Description
This is a supervised machine learning model trained to forecast short-term cryptocurrency trend bias (Bullish, Bearish, or Neutral). It is the core prediction engine for the CryptoRadar full-stack platform.
* **Model Type:** Scikit-Learn Classifier
* **Primary Use Case:** Predicting directional market momentum based on technical and sentiment data.
## Input Features
The model evaluates a 10-dimensional feature vector:
* `volume`, `dxy_index`, `price_change_pct`, `rsi`, `volatility`, `dist_from_sma`
* `sentiment_coin`, `sentiment_trend_coin`, `sentiment_btc`, `sentiment_trend_btc`
## Limitations
This model is for educational and portfolio purposes only. Cryptocurrency markets are highly volatile, and this model should not be used for actual financial trading. |