After creating a model, what steps should I take to make predictions?

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To make predictions using a created model, follow these steps:

1. Prepare new data: You need to prepare new data for prediction which are similar to the data used in model training.

2. Load model: Load the machine learning model which was created in training phase.

3. Preprocess the data: Before making predictions, preprocess the new data to make it compatible with the model. Transform the data by encoding categorical features, scaling numerical features, and handling missing values or outliers.

4. Make predictions: Use the loaded model to make predictions on new data by calling the predict() method or equivalent in the machine learning library.

5. Evaluate the predictions: Evaluate the accuracy of the predictions by comparing the predicted values with actual values.

6. Refine the model: If the accuracy of the predictions is not satisfactory, you can refine the model by changing the hyperparameters or modifying the model architecture.

7. Deploy the model: Deploy the model into production for use in real-world applications.