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A process model for data mining, consisting of six phases to guide the data mining project.
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The first phase of CRISP-DM, focusing on understanding the project objectives and requirements from a business perspective.
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The second phase of CRISP-DM, involving familiarization with the data, identification of data quality problems, and discovery of insights.
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The third phase of CRISP-DM, where the final dataset is constructed from the initial raw data for modeling.
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The fourth phase of CRISP-DM, involving the application of various modeling techniques and calibration of their parameters.
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The fifth phase of CRISP-DM, where the quality of the models is thoroughly evaluated before deployment.
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The sixth phase of CRISP-DM, where the model is integrated into an information system for future machine learning.
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The core process of CRISP-DM, which involves the exploration and analysis of large amounts of data to discover meaningful patterns and relationships.
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A key aspect of the Data Understanding phase, ensuring that the data is adequate to represent relationships and serve as a reliable foundation.
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A tool used in the Business Understanding phase, built using the Decision Model and Notation standard, to convert business knowledge into a data mining problem definition.
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An essential aspect of both Modeling and Evaluation phases, which deals with in-depth examination of data to extract useful insights.
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A process in Data Preparation, which stands for Extract, Transform, and Load, automating the cleaning and preparation of data.
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Data points that differ significantly from other data points in the Data Understanding phase, which require investigation to ensure accurate modeling.
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Configurable settings in the Modeling phase that control the behavior of modeling techniques, often requiring optimization for best results.
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A crucial step in the Evaluation phase, determining which models perform best based on specific evaluation methods.
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Aspects considered in the Evaluation phase, ensuring that the data mining results meet the business's needs and expectations.
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A professional involved in CRISP-DM, responsible for executing the data mining process and delivering valuable insights.
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A concept related to Deployment, in which models continue to learn and improve as new data is gathered.
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A concept related to Deployment, where the model assigns data points to specific segments or groups.