What is CRISP ? Explain the different phasses of crisp?
CRISP, which stands for Cross-Industry Standard Process for Data Mining, is a widely used framework for data mining and data science projects. It provides a structured approach to planning, executing, and managing data mining projects, ensuring that they meet business objectives and deliver actionable insights. The CRISP-DM model consists of six main phases, which are iterative and can be revisited as needed throughout the project lifecycle. Here’s a breakdown of each phase:

### 1. Business Understanding
- **Objective**: Understand the project objectives and requirements from a business perspective.
- **Key Activities**:
  - Define the business problem or opportunity.
  - Establish project goals and success criteria.
  - Identify stakeholders and their expectations.
  - Develop a project plan outlining the timeline and resources needed.

### 2. Data Understanding
- **Objective**: Collect and explore the data to gain insights and identify data quality issues.
- **Key Activities**:
  - Gather initial data from various sources.
  - Describe the data (e.g., data types, attributes).
  - Explore the data through visualizations and summary statistics.
  - Verify data quality and identify any issues such as missing values or outliers.

### 3. Data Preparation
- **Objective**: Prepare the data for modeling by cleaning and transforming it.
- **Key Activities**:
  - Select relevant data for analysis.
  - Clean the data by handling missing values, correcting errors, and removing duplicates.
  - Transform the
 CRISP stands for Cross-Industry Standard Process for Data Mining. It is a robust and well-recognized methodology for planning and executing data mining projects. CRISP is a practical and flexible framework that provides a systematic approach to data mining, enabling organizations to extract valuable insights from their data and make informed decisions.

The CRISP methodology consists of six phases, which are as follows:

1. Business Understanding: This is the initial phase of the CRISP methodology, where the business objectives and goals are defined. The data mining project is scoped, and the project team identifies the key stakeholders, their needs, and the success criteria for the project. The business understanding phase involves gathering information about the business problem, defining the problem statement, and identifying the data sources that will be used for data mining.
2. Data Understanding: In this phase, the data is collected, explored, and prepared for data mining. The data is cleaned, transformed, and integrated to ensure that it is in a format that can be used for data mining. Data understanding involves performing initial data analysis, identifying data quality issues, and developing a preliminary understanding of the data.
3. Data Preparation: This phase involves preparing the data for data mining. Data preparation involves selecting the relevant data, transforming it into a format that can be used for data mining, and ensuring that the data is of high quality. Data