Part 2
2. Machine Learning Models
**Machine learning models** can be trained on historical price data to predict future prices. This approach involves several steps:
- **Data Collection**: Gathering historical price data for crypto , including open, high, low, close prices, and volume.
- **Feature Engineering**: Creating features from the raw data that may help the model, such as moving averages, volatility measures, and momentum indicators.
- **Model Selection**: Choosing an appropriate model, such as Linear Regression, Decision Trees, Random Forest, or more complex models like Long Short-Term Memory (LSTM) networks, which are a type of Recurrent Neural Network (RNN) particularly suited for time-series data.
- **Training and Validation**: Splitting the data into training and validation sets, training the model on historical data, and validating its performance on unseen data.
