CRISP-DM data mining model consisting of six phases. It is a cyclical process that provides a structured approach to the data mining process. The six phases can be implemented in any order but it would sometimes require backtracking to the previous steps and repetition of actions.

CRISP-DM 6 steg:

#1) Business Understanding: In this step, the goals of the businesses are set and the important factors that will help in achieving the goal are discovered.

#2) Data Understanding: Collect the whole data and populate the data in the tool (if using any tool). The data is listed with its data source, location, how it is acquired and if any issue encountered. Data is visualized and queried to check its completeness.

#3) Data Preparation: Selecting the appropriate data, cleaning, constructing attributes from data, integrating data from multiple databases.

#4) Modeling: Selection of the data mining technique such as decision-tree, generate test design for evaluating the selected model, building models from the dataset and assessing the built model with experts to discuss the result is done in this step.

#5) Evaluation: This step will determine the degree to which the resulting model meets the business requirements. Evaluation can be done by testing the model on real applications. The model is reviewed for any mistakes or steps that should be repeated.

#6) Deployment: In this step a deployment plan is made, strategy to monitor and maintain the data mining model results to check for its usefulness is formed, final reports are made and review of the whole process is done to check any mistake and see if any step is repeated.

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