3 Data Mining Process.pdf

CS 412 - DATA MINING DATA MINING PROCESS JASSER ARABANI T. DAWABI PROCESS OF DATA MINING CRISP-DM Cross Industry Standard Process for Data Mining The CRoss Industry Standard Process for Data Mining...

CS 412 - DATA MINING DATA MINING PROCESS JASSER ARABANI T. DAWABI PROCESS OF DATA MINING CRISP-DM Cross Industry Standard Process for Data Mining The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process. As a methodology, it includes descriptions of the typical phases of a project, the tasks involved with each phase, and an explanation of the relationships between these tasks. PROCESS OF DATA MINING 6 - STEP PROCESS Business/Project Data Data Modelling Understanding Understanding Preparation The analyst evaluates, Focuses on understanding Starting with initial data The data preparation phase selects & applies the the project objectives and collection, the analyst covers all activities to appropriate modelling requirements from a proceeds with activities to construct the final dataset techniques business perspective. get familiar with the data, from the initial raw data identify data quality problems & discover first insights into the data. Evaluation Deployment The analyst builds & Generally this will mean chooses models that deploying a code appear to have high quality representation of the model based on loss functions that into an operating system. were selected. DATA MINING PROCESS BUSINESS UNDERSTANDING Comprehensive data mining projects start by first identifying project objectives and scope. The business stakeholders will ask a question or state a problem that data mining can answer or solve. DATA MINING PROCESS DATA UNDERSTANDING Relevant data is then collected once the business problem is understood. The data to be used in the project may come from multiple source DATA MINING PROCESS DATA PREPARATION Data preparation involves preparing the final data set, which includes all the relevant data needed to answer the business question. Stakeholders will identify the dimensions and variables to explore and prepare the final data set for model creation. DATA MINING PROCESS MODELING In this phase, the analyst selects the appropriate modeling techniques for the given data. These techniques can include clustering, predictive models, classification, estimation, or a combination. DATA MINING PROCESS EVALUATION After creating the models, the analyst need to test them and measure their success at answering the question identified in the first phase. This phase is designed to allow the analyst to look at the progress so far and ensure it’s on the right track for meeting the business goals. DATA MINING PROCESS DEPLOYMENT The deployment can take place within the organization, be shared with customers, or be used to generate a report for stakeholders to prove its reliability.  THANK YOU SOURCE: HTTPS://WWW.TECHTARGET.COM/SEARCHBUSINESSANALYTICS/DEFINITION/DATA-MINING HTTPS://ZIPREPORTING.COM/EN/DATA-MINING/DATA-MINING-PROCESS.HTML HTTPS://WWW.TABLEAU.COM/LEARN/ARTICLES/WHAT-IS-DATA-MINING HTTPS://WWW.GURU99.COM/DATA-MINING-TUTORIAL.HTML HTTPS://WWW.DATASCIENCE-PM.COM/CRISP-DM-2/ HTTPS://WWW.SV-EUROPE.COM/CRISP-DM-METHODOLOGY/ HTTPS://WWW.IBM.COM/DOCS/EN/SPSS-MODELER/18.2.0?TOPIC=DM-CRISP-HELP-OVERVIEW HTTPS://THINKINSIGHTS.NET/DIGITAL/CRISP-DM/