The model splits a data mining project into six phases and it allows for needing to go back and forth between different stages.
1. Business Understanding
- Understanding the business goal
- Situation assessment
- Translating the business goal in a data mining objective
- Development of a project plan
2. Data understanding
- Considering data requirements
- Initial data collection, exploration, and quality assessment
3. Data preparation
- Selection of required data
- Data acquisition
- Data integration and formatting
- Data cleaning
- Data tranaformation and enrichment
4. Modeling
- Selection of appropriate modeling technique
- Splitting of the dataset into training and testing subsets for evaluation purposes
- Development and examination of alternative modeling algorithms and parameter settings
- Fine tuning of the model settings according to an initial assessment of the model’s performance
5. Model evaluation
- Evaluation of the model in the context of the business success criteria
- Model approval
6. Deployment
- Create a report of findings
- Planning and development of the deployment procedure
- Deployment of the model
- Distribution of the model results and integration in the organisation’s operational system
- Development of a maintenance / update plan
- Review of the project
- Planning the next steps
References: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
