What is the correct order of the following phases in a machine learning project?

The correct order of phases in a machine learning project is essential for ensuring that each step is properly executed and contributes to the overall success of the project. The option that begins with the definition of the business use case emphasizes the importance of understanding the problem we are trying to solve before any technical work begins. This phase sets the foundation by clarifying the goals, requirements, and constraints of the project, which guide subsequent steps.

Following this, data exploration is critical, as it allows data scientists to understand the structure, quality, and relevance of the data available. This phase is vital for identifying any data issues that need to be addressed and helps inform how to preprocess that data.

Next, selecting the machine learning algorithm comes into play. It's based on the insights gained from the data exploration phase and the nature of the business problem defined earlier. Choosing the right algorithm is crucial for model performance and effectiveness.

Model development follows, where the selected algorithm is implemented, and the model is trained and evaluated. This phase often involves iterating on the model design and feature engineering to enhance performance.

Once the model is developed, model operationalization is conducted, which includes deploying the model into a production environment where it can begin providing value to stakeholders or end users.

Lastly, model