建立了数据模型后,将模型应用到实践当中去之前，需要先评估数据模型的有效

After the data model is established, before applying the model to practice, it is necessary to evaluate the validity of the data model to ensure that the model is stable and effective and can solve business problems well. It is necessary to comprehensively review the entire data mining process to determine whether some important factors have been overlooked, and whether there are still some issues that have not been considered in the modeling process. The next step is to verify the model. Data that is not involved in data modeling can be applied to the model to verify the validity of the model. Compare the model output results with the actual situation and calculate the accuracy of the model data results. In this process, if you find that the model is not good enough, or fail to achieve a specific business goal, you need to go back to the previous steps to make adjustments. <br>(6) Deployment <br>of the data model After the model is established and evaluated, the validity of the model is determined, which does not mean that the entire data mining process is over. The fundamental purpose of establishing a data model is to solve business problems encountered in practice. Therefore, at the end of the implementation of the entire project, it is necessary to deploy the established model to actual applications, and formulate based on the model results to improve the business Operating strategy.

After the data model is established and applied to practice, it is necessary to evaluate the validity of the data model to ensure that the model is stable and effective and can solve business problems well. A comprehensive review of the entire data mining process is needed to determine whether some of the important factors have been ignored and whether some issues have not been taken into account in the modeling process. The model is then validated, and data that is not involved in data modeling can be applied to the model to verify the validity of the model. Comparing the model output with the actual situation and calculating the accuracy of the model data results, in the process, if the model is found to be not good enough or has failed to meet a specific business objective, you will need to go back to the previous steps to adjust.<br>(6) The deployment of the data model<br>Having established and evaluating the model and determining its validity does not mean that the entire data mining process is over. The fundamental purpose of building a data model is to solve the business problems encountered in practice, so at the end of the entire project implementation, you need to deploy the established model to the actual application and develop an operational strategy that can improve the business based on the results of the model.

After the data model is established, before the model is applied to practice, we need to evaluate the effectiveness of the data model to ensure that the model is stable and effective and can solve business problems well. It is necessary to comprehensively review the whole data mining process to determine whether some important factors have been ignored and whether there are still some problems that have not been considered in the modeling process. The next step is to verify the model. The data not involved in data modeling can be applied to the model to test the effectiveness of the model. Compare the output results of the model with the actual situation, and calculate the accuracy of the data results of the model. In this process, if the model is not good enough or fails to achieve specific business goals, you need to go back to the previous steps for adjustment.<br>(6) Deployment of data model<br>After the model is established and evaluated, the validity of the model is determined, which does not mean that the whole data mining process is over. The fundamental purpose of establishing data model is to solve the business problems encountered in practice. Therefore, at the end of the whole project implementation, it is necessary to deploy the established model to the practical application, and formulate the operation strategy to improve the business according to the model results.<br>