CRISP MODEL
A structured approach to problem-solving ensures that we fully understand the problem and are comprehensive in our search solutions. The Cross Industry Standard Process for Data Mining (CRISP-DM) is used as a framework in which we approach all our problems. The fundamental steps in the CRISP-DM consist of:
1. Business understanding
Identifying our clients’ business objectives and requirements to formulate a preliminary executable data science project plan.
2. Data understanding
Collecting and becoming familiar with the raw data and making the necessary alterations to the preliminary plan.
3. Data preparation
Constructing a final data set that would be used in building the statistical or machine learning model.
4. Modeling
The final data set is processed with the appropriate statistical and machine learning techniques. The parameters of the models are optimised to produce the most accurate and precise results that tailors the business understanding.
5. Evaluation
The model is evaluated according to the objectives and requirements in the data science project plan. The initial business understanding phase is revised to ensure that the model produces business problems or solutions that were not previously encountered or considered.
6. Deployment
The deployment phase can commence after all the business objectives, and the data science project plan fulfils the client’s requirements. This provides our client with a detailed explanation of how to utilise the model developed, gain knowledge from the data, and support insightful decision-making.
