CRISP-DM Methodology Essay Sample
In the following essay sample, we shall focus and discuss CRISP-DM Methodology in detail. This particular word of the CRISP-DM stands for a cross-industry process for data mining.
This is a well-planned and structured methodology that provides an approach for a detailed plan of a data mining project. It is an open standard process model that describes the approaches that are used by the data mining experts in the analytics models.
In this essay sample writing, we shall discuss elaborately the six phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM).
Six Phases Of CRISP-DM
The six phases of the CRISP-DM or process model comprises of the following phases:-
- Business Understanding
- Data Understanding
- Data Preparation
Data management science has the highest purpose of data extraction of the highest quality where the different IT companies can come up with different and own approaches for data mining. Each phase under CRISP-DM methodology has a specific type of activity that becomes a part of the project in data science.
(1) Business Understanding
This is the initial phase of any of the business projects where deep understanding and knowledge of the customer’s needs is of prime importance. The individuals who are taking up the project must be aware of the requirements of a data science project, business idea and perspective, and objectives of the business.
There are tasks related to the project management activities apart from the three other tasks. The internal or external factors responsible for a certain outcome of a project must be understood.
The business perspectives must be understood, objectives of the business must be determined set, business success criteria must be defined. Data mining goals along with the availability of resources and requirements of the project must be determined. Risk assessment and conduct of the cost-benefit analysis must be done.
Finally, the project plan should be produced by selecting the tools and techniques that need to be guided and detailed for each phase of the project.
(2) Data Understanding
The second phase in the data science project includes an understanding of the data through identification, collection, and analyzing the data sets. Such analysis of data helps in attaining the goals of the project.
In this, the data must be acquired first which are named or listed in the project resources and after that data needs to be loaded into the specific tool.
An initial data collection report must be made which shall include the details about data sources and locations of data, methods used for acquiring data and recording of problems and resolutions.
After this data description report describing the data must be done and prepared. This data must be closely examined for its various properties like the data format, filed identities, etc.
The purpose of a data exploration report is to answer the queries and questions related to data mining, data visualization, and reporting techniques. Data must be verified of its quality through addressing the problems related to it in the data quality report.
(3) Data Preparation
A data preparation shall comprise of a selection of data, cleaning data, constructing data, integrating and formatting data. The selection of the data depends upon the goals of data mining, quality of data, any technical constraints on data. The data must be selected (included) or excluded on the basis of several factors.
In the next stage, data needs to be cleaned describing the actions taken up to address the data quality problems in the data cleaning report.
There is a task of constructive data preparation operations with derived attributes and generated records. The multiple data needs to be combined from the multiple records, databases to create new values or records.
Modeling is the shortest phase of a data science project where there will be an assessment of various models based on the different modeling techniques.
These modeling techniques do make some specific assumptions about the data. Building of model requires a modeling tool that can set and adjust a large number of parameters. It can describe the resulting models, report on the interpretation of such models and problems or issues that needs to be documented.
The model needs to be finally assessed by summarising the results of the task and listing the qualities of generated models. Parameter settings need to be revised and prepared for the next modeling run. The stages include a selection of modeling techniques, generating test designs, building models and finally assessing the model.
The phase of evaluation states the questions of whether the models have met the business success criteria and which particular model should be approved for business. Work that has been accomplished must be reviewed by observing all the required steps that have been taken properly and executed.
Findings need to be summarized by correcting the major flaws. On the basis of the performance of the previous tasks, a deployment process must be determined to decide whether to initiate new projects or not.
This is the final phase of a project in data science which is pretty much a complex process. A model can only be considered useful if the customers can easily access the results.
The phase of deployment comprises four tasks which include the development of the plan and documentation of the plan for the deployment of the model. A plan for monitoring and maintenance must be developed to avoid any issues in the operational phase.
Finally, a final report should be produced which may include the summary of the project and final presentation of the data mining results. The project needs to be reviewed in order to improve the future.
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The above-written essay sample highlights and discusses the CRISP-DM methodology and its phases.
Data Analytics QQI Level 8, Business Data Analysis QQI Level 7, Software Development and Data Analytics QQI Level 5, Training Needs Identification and Design QQI Level 6 and Project Management (6N4090) QQI Level 6 Irish students can read this above-written essay sample to enhance their knowledge, writing skills and vocabulary.
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