Question 2: What is the CRISP-DM methodology in data analytics?
Answer:
CRISP-DM (Cross-Industry Standard Process for Data Mining) is a popular and widely used methodology for data analytics and data mining projects. It provides a structured approach to tackling data-related problems and consists of six phases:
- Business Understanding:
- Objective: Define the project’s goals and requirements from a business perspective.
- Key Questions: What is the business problem? What are the success criteria?
- Data Understanding:
- Objective: Collect initial data, explore it, and identify any quality issues.
- Activities: Analyze data distributions, identify outliers, and understand relationships between variables.
- Data Preparation:
- Objective: Prepare the final dataset for modeling.
- Activities: Data cleaning, transformation, feature engineering, and handling missing values.
- Modeling:
- Objective: Select and apply machine learning or statistical models to the prepared data.
- Activities: Choose algorithms, tune parameters, and assess performance.
- Evaluation:
- Objective: Evaluate the model’s performance against the business goals.
- Activities: Validate accuracy, precision, recall, or other metrics, and ensure the model meets business needs.
- Deployment:
- Objective: Implement the results in a way that solves the business problem.
- Activities: Create dashboards, deploy models to production, and present findings to stakeholders.
Why is CRISP-DM Important?
- Provides a systematic, repeatable process for solving data problems.
- Bridges the gap between business goals and technical solutions.
- Encourages iterative work and stakeholder collaboration.
