sequential model

A sequential model in machine learning is a type of model that processes data in a specific order, often used for tasks like time-series analysis or natural language processing. It involves analyzing data step-by-step to make predictions based on previous inputs.

How do project complexity and size impact the choice of model?

The choice of software development model is influenced by the complexity and size of a project. For smaller and less complex projects, agile methodologies such as Scrum or Kanban are often preferred. These methodologies emphasize flexibility, collaboration, and iterative development to quickly deliver value. On the other hand, for larger and more complex projects, a sequential or waterfall model may be suitable. This model involves a linear and structured approach with distinct phases like requirement gathering, design, development, testing, and deployment. The choice of model depends on factors like project requirements, team expertise, stakeholder involvement, and budget constraints.

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