Here is a project outline for a Renzo Binance price prediction project:
*Project Title:* Renzo Binance Price Predictor
*Objective:* Develop a machine learning model to predict the future prices of Renzo (REN) on Binance, using historical data and technical indicators.
*Scope:*
- Collect and preprocess historical data on Renzo prices and technical indicators (e.g., moving averages, RSI, Bollinger Bands)
- Develop and train a machine learning model (e.g., LSTM, Random Forest) to predict future prices
- Evaluate the model's performance using metrics (e.g., mean absolute error, mean squared error)
- Deploy the model as a web application or API for users to input parameters and receive predictions
*Data Collection:*
- Sources: Binance API, CoinMarketCap, or other cryptocurrency data APIs
- Historical data: Renzo prices, trading volumes, technical indicators (e.g., 1-year worth of data)
- Features: extract relevant features from data (e.g., moving averages, RSI, Bollinger Bands)
*Machine Learning Model:*
- Type: LSTM (Long Short-Term Memory) or Random Forest
- Input features: extracted features from historical data
- Output: predicted future price
- Training: split data into training and testing sets (e.g., 80% for training, 20% for testing)
*Evaluation Metrics:*
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Percentage Error (RMSPE)
*Deployment:*
- Web application: user inputs parameters (e.g., time frame, technical indicators), receives predicted price
- API: returns predicted price for given parameters
*Technical Requirements:*
- Programming languages: Python, JavaScript
- Libraries: Pandas, NumPy, scikit-learn, TensorFlow (for LSTM), Binance API library
- Data storage: CSV or database (e.g., MySQL)
*Future Development:*
- Incorporate additional features (e.g., sentiment analysis, news events)
- Expand to predict prices for multiple cryptocurrencies
- Implement real-time data updates and predictions
