CRISP-DM (CRoss-Industry Standard Process for Data Mining)

1. 定义 (Definition)

• CRISP-DM 是数据挖掘领域的跨行业标准流程，它提供了一个清晰的框架，用于数据挖掘项目的各个阶段，从项目的启动到最终的部署。
  (CRISP-DM is the cross-industry standard process for data mining, providing a clear framework for the stages of a data mining project, from initiation to final deployment.)

2. 阶段 (Phases)

• 业务理解 (Business Understanding):

• 定义:
  在这一阶段，团队需要明确业务目标，并理解这些目标如何转化为数据挖掘任务。
  (In this phase, the team needs to define the business objectives and understand how these objectives translate into data mining tasks.)

• 数据理解 (Data Understanding):

• 定义:
  这一阶段涉及收集初步数据，并通过探索性数据分析（EDA）理解数据的特点和质量。
  (This phase involves collecting initial data and understanding its characteristics and quality through exploratory data analysis (EDA).)

• 数据准备 (Data Preparation):

• 定义:
  在数据准备阶段，数据科学家对数据进行清洗、转换和特征选择，为建模阶段做好准备。
  (In the data preparation phase, data scientists clean, transform, and select features from the data to prepare it for the modeling phase.)

• 建模 (Modeling):

• 定义:
  这一阶段通过选择合适的建模技术和算法，对数据进行建模，并调整参数以优化模型性能。
  (In this phase, the appropriate modeling techniques and algorithms are selected, and the data is modeled, with parameters tuned to optimize model performance.)

• 评估 (Evaluation):

• 定义:
  在评估阶段，模型的性能与业务目标进行对比，以确定模型是否成功地解决了业务问题。
  (In the evaluation phase, the performance of the model is compared against business objectives to determine whether the model successfully addresses the business problem.)

• 部署 (Deployment):

• 定义:
  最后，成功的模型被部署到生产环境中，供实际业务使用，并且模型的效果需要持续监控和优化。
  (Finally, the successful model is deployed into the production environment for actual business use, with ongoing monitoring and optimization of the model's performance.)