To classify credit scores using machine learning, you will need to follow these steps:

• Collect data on credit scores: You will need to gather a dataset of credit scores along with other relevant information such as income, debt, and credit history.

• Clean and prepare the data: You will need to clean the data by removing any missing or invalid values and possibly transforming the data in some way to make it more suitable for analysis.

• Select a machine learning algorithm: You will need to choose an algorithm that is appropriate for this classification task. Some popular algorithms for classification include decision trees, support vector machines (SVMs), and logistic regression.

• Train the model: Using your selected algorithm, you will need to train the model on the prepared data. This involves feeding the algorithm the training data and allowing it to "learn" the patterns in the data.

• Test the model: Once the model has been trained, you will need to test its performance on a separate test dataset to see how well it is able to classify credit scores.

• Fine-tune the model: If the model's performance is not satisfactory, you may need to fine-tune the model by adjusting its hyperparameters or using a different algorithm.