Practical Application-Describe how the model’s performance will be evaluated, including metrics and validation techniques.

Project proposal & CRISP-DM Framework Application: Each student will select a real-world problem that can be addressed using data mining (e.g., customer churn prediction, fraud detection, sentiment analysis).

Draft a project proposal that includes the following:
Problem Definition: Clearly define the problem and explain the significance of solving it.
Objective: State the goals of the data mining project.

CRISP-DM Framework Application:
Business Understanding: Detail the business objectives and requirements.
Data Understanding: Describe the data collection process, including the types of data, data sources, and any initial data exploration.
Data Preparation: Outline the steps for data cleaning, transformation, and feature engineering.
Modeling: Select appropriate data mining techniques (e.g., clustering, classification, regression) and explain the rationale behind the choices.

Evaluation: Describe how the model’s performance will be evaluated, including metrics and validation techniques.
Deployment: Discuss the steps for deploying the model in a real-world environment and monitoring its performance.
Essential Activities:

Watching the video, “CRISP-DM and the laws of data mining” will assist you in writing your paper.

 

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