Training the model involves feeding it with historical data and allowing it to learn the patterns. The steps include:
1. Splitting the Data: Divide the data into training and testing sets, typically in an 80-20 ratio. 2. Training: Use the training set to fit the model. 3. Validation: Use cross-validation techniques to tune hyperparameters and avoid overfitting. 4. Testing: Evaluate the model's performance on the test set to ensure it generalizes well.