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CRISP.docx - SAMIUL ISLAM ID: 369418 Application Of...

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SAMIUL ISLAMID: 369418Application Of Decision Tree Approach To StudentSelection Model- A Case Study
Data mining is a process of finding patterns, anomalies, and correlations within large data sets topredict outcomes. Data mining is also known as knowledge of Discovery in Data. We can useData mining to answer questions that cannot be addressed through simple query and reportingtechniques. With the help of Data mining, one can discover hidden data information, but the datamining process would not be able to tell you the value of the information to your organization.Data mining involves the use of existing data that was collected for other purposes. This makesdata mining somewhat unique in that a lot of cleaning and organizing of the data is requiredbefore analyses.CRISP-DM stands for Cross Industry Standard Process of Data Mining. The CRISP-DMmethodology shows a structured approach to planning a data mining project. CRISP-DM is arobust and well-proven methodology. The CRISP-DM process model aims to conduct large datamining projects, with less costly, more reliable, more repeatable, more manageable, and fastermanner.The CRISP-DM model is an idealized sequence of events, but in reality, the tasks (Not all) canbe performed in a different sequence, and it will often be necessary to backtrack to previous tasksand repeat specific actions. The CRISP-DM model does not try to capture all possible routesthrough the data mining process. The CRSP-DM model shows the overall lifecycle of a datamining project. It is divided into many phases, which we will discuss in detail.

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