How to organize machine learning projects: Application of Crisp-DM
Crisp-DM is also called the cross-industry data mining standard process. This article introduces how to organize machine learning projects based on the Crisp-DM method.
The steps are as follows:
Step one: Business understanding is the key to solving the problem. In this step, we need to fully understand the nature and severity of the business problem. By researching possible solutions, we can determine whether we need to leverage machine learning to solve the problem. At the same time, we also need to consider potential alternatives and set a clear, quantifiable goal for solving the problem. The purpose of this step is to ensure that we have a clear understanding of the problem so that we can develop an effective solution.
Step 2: Data understanding. Once you understand the business problem, the next step is to understand the complexity of the data being provided. This includes analyzing available data sources and verifying data quality, i.e. is the data accurate, complete, reliable, relevant and up-to-date?
Step 3: Data preparation. Transform data to fit machine learning algorithms, including data cleaning, transformations, feature engineering, and more.
Step 4: Modeling. Use different machine learning algorithms on previously prepared data to select the best performing model.
Step five: Evaluate. Evaluate the performance of the model and determine whether it achieves expected results. How does the model perform based on these points? Has the set goal been achieved? . If the model is good enough, it can be deployed after evaluation, otherwise the process needs to be re-examined.
Step 6: Deployment. Before a machine learning solution can be deployed into production, it needs to be integrated into a software system. Once deployed, the quality and maintainability of the algorithm can be continuously monitored to ensure its effectiveness in real-world applications.
Machine learning solutions often require multiple iterations, starting simple and learning and improving the model through feedback.
Overall, following the Crisp-DM approach results in well-structured projects with a low risk of failure.
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