The lifecycle of a machine learning project is divided into six phases. The process begins by defining a business problem and restating the business problem in terms of a machine learning objective. The end goal of a machine learning process is to produce accurate results for solving your business problem.

• Workflow
  The machine learning process workflow illustration is based on the CRISP-DM methodology. Each stage in the workflow is illustrated with points that summarize the key tasks. The CRISP-DM methodology is the most commonly used methodology for machine learning.
• Define Business Goals
  The first phase of machine learning process is to define business objectives. This initial phase of a project focuses on understanding the project objectives and requirements.
• Understand Data
  The data understanding phase involves data collection and exploration which includes loading the data and analyzing the data for your business problem.
• Prepare Data
  The preparation phase involves finalizing the data and covers all the tasks involved in making the data in a format that you can use to build the model.
• Develop Models
  In this phase, you select and apply various modeling techniques and tune the algorithm parameters, called hyperparameters, to desired values.
• Evaluate
  At this stage of the project, it is time to evaluate how well the model satisfies the originally-stated business goal.
• Deploy
  Deployment is the use of machine learning within a target environment. In the deployment phase, one can derive data driven insights and actionable information.