Crisp DM: A Comprehensive Guide In 2020

Introduction

As you know, people who work in the domain of Data Science use Data Analysis for finding solutions to various problems. Every Data Science project aims for better data quality that will lead to valuable insights. Be it Data Science, Data Mining, or Machine Learning projects, having a proper methodology improves the quality of end-results and insights significantly. Therefore, Data Scientists need to have a strong understanding of the methods.

A streamlined framework will include the necessary steps and workflows for the successful implementation of a Data Science project. That is where CRISP DM garners attention. It is one of the most popular and prominent Data Science models practiced in the industry today. This methodology has witnessed exponential growth in recent years and is a future-proof solution.

Let us understand the CRISP DM process in detail and how it helps your career as a data scientist.

1. What Is CRISP-DM?
2. Why Learn Crisp DM?

1. What Is CRISP DM?

The full form of CRISP DM is the Cross-Industry Standard Process for Data Mining. It was formulated in 1996 as a standardized model for Data Science projects under the ESPRIT initiative. It offers an end-to-end structured approach to solve an issue that needs Data Science.

The CRISP DM framework includes six necessary steps – Business Understanding, Data Understanding, Data Preparation, Data Modelling, Evaluation, and Deployment.

Here is the pictorial depiction of the CRISP model.

https://st3.ning.com/topology/rest/1.0/file/get/2808314343?profile=RESIZE_480x480

Let us understand each of the CRISP DM steps in detail.

A) Business Understanding

The CRISP DM model’s primary step is to understand the Data Science project’s objective from a business perspective. The first step’s goal is to understand the key factors that are ought to influence the project’s outcome.

The first phase of the CRISP DM approach will include:

• Determining the desired outputs
• Assessing the current situation
• Determining the Data Mining goals
• Developing a project plan

Each of the steps further gets classified for the ease of project implementation. For example, to determine the desired outputs, the following three steps are carried out –

• Set The Objectives

This step is significant to ensure the project is on a goal-oriented track. You need to describe the project’s primary objective and the associated questions that you want to solve.

• Build A Project Plan

In the second step, you need to produce a project plan to meet the respective objectives. You should come up with the necessary steps required for the other part of the project. It should mention the essential tools and techniques, as well.

• The Success Criteria

The third stage focuses on building the criteria to determine the project’s success from the business perspective. It should have specific, measurable parameters.  

B) Data Understanding

The CRISP DM process model’s second step focuses on collecting the data listed on the resources. This step includes data loading as it helps in data understanding CRISP DM. In the case of multiple data sources, you need to figure out its time and place for integration. Phase 2 will execute using the following steps –

• Data collection report
• Data description report
• Data exploration report
• Data quality report

In low data quality, you need to come up with possible solutions in this phase itself. To develop the answers, you require a better understanding of the business as well as data.

C) Data Preparation

Preparing the data forms the third phase of the CRISP methodology. Here you will determine the data that you are going to use for the analysis. The CRISP DM data preparation steps include:  

• Data Selection

We should choose data based on its relevance to the Data Mining goals, quality of the data, and other technical constraints.

• Data Cleaning

This step focuses on improving the quality of data to fit in the analytical techniques chosen for the project.

• Data Construction

It refers to the production of derived attributes of the transformation of values for existing features.

• Data Integration

In this step of CRISP data preparation, information from various databases and tables get combined.

D) Data Modelling

In the CRISP DM process’s fourth step, you need to select the basic modeling technique that you want to use for the project. In the CRISP DM Business Understanding phase, you will choose the tools required, but you will make it more specific in the fourth phase. There are three primary tasks involved in this step.

• Generate Test Design

Here, you will describe the plan ahead for training, testing, and evaluating the models.

• Build The Model

This step involves running the tool on the data prepared to create one or multiple models.

• Model Assessment

In this stage, you will interpret the models based on three factors – your test design, success criteria, and domain understanding.   

E) Evaluation

In this phase of the CRISP DM approach, you will assess the point to which the designated model meets the business objectives. You can use a CRISP DM example project, or if you have budget and time, you can analyze the real application of the model to evaluate it.

• Process Review

This step aims to achieve two goals – to determine if any essential factor is ignored and address the quality assurance issues.

• Determine The Further Steps

At this step, you decide whether you should proceed further or not, depending on the process review and assessment results.

F) Deployment

In the final phase of the CRISP model, you will frame the deployment strategy based on your evaluation results. The deployment phase is critical for the success of a Data Mining project. You should consider it while you are at the CRISP DM business understanding phase as well. Predictive Analysis is helpful mainly here as it improves the operational side of the business. Here is a summary of the processes involved.

• Plan Monitoring And Maintenance

This process is applicable if the Data Mining project continues daily. It helps in the timely monitoring and maintenance of the project.

• Maintenance Policy

In this step, you will prepare the strategy for maintenance. It will include the steps needed to prevent incorrect usage of Data Mining results and perform them.

• Final Report Production

It is the final representation of the results of the Data Mining project.

• Project Review

At this final phase, you need to assess whether the project was carried out rightly and identify areas that require improvement.  

2. Why Learn CRISP DM?

CRISP DM is one of the commonly-used analytics models. It offers a well-structured approach to Data Mining projects. It helps Data Science teams to plan, implement, and organize Data Science projects successfully.

Being a robust and well-proven methodology for Data Science projects, CRISP DM methodology is a preferred choice for Business Analysts as well as Data Scientists. It continues to be a reliable method for Data Science projects and can execute irrespective of the domain.

Conclusion

Given the current industry trends, it is imperative to understand the CRISP DM model to succeed in your career as a Data Scientist. Are you looking forward to building your career as a data scientist? Join our Full Stack Data Science Program now!

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