The question "What is the problem we are trying to solve?" is a fundamental question that is critical to any machine learning project. This question is important because it helps to define the scope of the project and ensures that everyone involved in the project is working towards a common goal.

In the context of machine learning, the problem we are trying to solve could be many things, such as:

1. Predicting future outcomes: This could involve predicting whether a customer is likely to churn, whether a stock will go up or down, or whether a medical test will be positive or negative.

2. Classifying data: This could involve categorizing emails as spam or not spam, images as containing a certain object or not, or customers as high-value or low-value.

3. Clustering data: This could involve grouping similar items together, such as customers with similar purchasing behavior or news articles with similar topics.

4. Recommending items: This could involve recommending products to customers based on their past behavior or recommending movies to users based on their viewing history.

5. Understanding patterns and relationships: This could involve identifying trends in data or finding correlations between different variables.

Defining the problem we are trying to solve is the first step in any machine learning project, and it helps to ensure that the project is focused, well-defined, and has a clear objective