| --- |
| 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. |
