CRISP-DM: Data Mining Process

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Data Analytics Task 2024-1

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Mots Croisés CRISP-DM: Data Mining ProcessVersion en ligne Data Analytics Task 2024-1 par santiago cruz santisteban 1 A process model for data mining, consisting of six phases to guide the data mining project. 2 The first phase of CRISP-DM, focusing on understanding the project objectives and requirements from a business perspective. 3 The second phase of CRISP-DM, involving familiarization with the data, identification of data quality problems, and discovery of insights. 4 The third phase of CRISP-DM, where the final dataset is constructed from the initial raw data for modeling. 5 The fourth phase of CRISP-DM, involving the application of various modeling techniques and calibration of their parameters. 6 The fifth phase of CRISP-DM, where the quality of the models is thoroughly evaluated before deployment. 7 The sixth phase of CRISP-DM, where the model is integrated into an information system for future machine learning. 8 The core process of CRISP-DM, which involves the exploration and analysis of large amounts of data to discover meaningful patterns and relationships. 9 A key aspect of the Data Understanding phase, ensuring that the data is adequate to represent relationships and serve as a reliable foundation. 10 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. 11 An essential aspect of both Modeling and Evaluation phases, which deals with in-depth examination of data to extract useful insights. 12 A process in Data Preparation, which stands for Extract, Transform, and Load, automating the cleaning and preparation of data. 13 Data points that differ significantly from other data points in the Data Understanding phase, which require investigation to ensure accurate modeling. 14 Configurable settings in the Modeling phase that control the behavior of modeling techniques, often requiring optimization for best results. 15 A crucial step in the Evaluation phase, determining which models perform best based on specific evaluation methods. 16 Aspects considered in the Evaluation phase, ensuring that the data mining results meet the business's needs and expectations. 17 A professional involved in CRISP-DM, responsible for executing the data mining process and delivering valuable insights. 18 A concept related to Deployment, in which models continue to learn and improve as new data is gathered. 19 A concept related to Deployment, where the model assigns data points to specific segments or groups. 2 3 8 9 5 6 7 12 4 11 1 10 14 13 16 15 18 19 17

Mots Croisés

CRISP-DM: Data Mining ProcessVersion en ligne

Data Analytics Task 2024-1

par santiago cruz santisteban

A process model for data mining, consisting of six phases to guide the data mining project.

The first phase of CRISP-DM, focusing on understanding the project objectives and requirements from a business perspective.

The second phase of CRISP-DM, involving familiarization with the data, identification of data quality problems, and discovery of insights.

The third phase of CRISP-DM, where the final dataset is constructed from the initial raw data for modeling.

The fourth phase of CRISP-DM, involving the application of various modeling techniques and calibration of their parameters.

The fifth phase of CRISP-DM, where the quality of the models is thoroughly evaluated before deployment.

The sixth phase of CRISP-DM, where the model is integrated into an information system for future machine learning.

The core process of CRISP-DM, which involves the exploration and analysis of large amounts of data to discover meaningful patterns and relationships.

A key aspect of the Data Understanding phase, ensuring that the data is adequate to represent relationships and serve as a reliable foundation.

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.

CRISP-DM: Data Mining Process

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Mots Croisés

CRISP-DM: Data Mining ProcessVersion en ligne

par santiago cruz santisteban

A process model for data mining, consisting of six phases to guide the data mining project.

The first phase of CRISP-DM, focusing on understanding the project objectives and requirements from a business perspective.

The second phase of CRISP-DM, involving familiarization with the data, identification of data quality problems, and discovery of insights.

The third phase of CRISP-DM, where the final dataset is constructed from the initial raw data for modeling.

The fourth phase of CRISP-DM, involving the application of various modeling techniques and calibration of their parameters.

The fifth phase of CRISP-DM, where the quality of the models is thoroughly evaluated before deployment.

The sixth phase of CRISP-DM, where the model is integrated into an information system for future machine learning.

The core process of CRISP-DM, which involves the exploration and analysis of large amounts of data to discover meaningful patterns and relationships.

A key aspect of the Data Understanding phase, ensuring that the data is adequate to represent relationships and serve as a reliable foundation.

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.

An essential aspect of both Modeling and Evaluation phases, which deals with in-depth examination of data to extract useful insights.

A process in Data Preparation, which stands for Extract, Transform, and Load, automating the cleaning and preparation of data.

Data points that differ significantly from other data points in the Data Understanding phase, which require investigation to ensure accurate modeling.

Configurable settings in the Modeling phase that control the behavior of modeling techniques, often requiring optimization for best results.

A crucial step in the Evaluation phase, determining which models perform best based on specific evaluation methods.

Aspects considered in the Evaluation phase, ensuring that the data mining results meet the business's needs and expectations.

A professional involved in CRISP-DM, responsible for executing the data mining process and delivering valuable insights.

A concept related to Deployment, in which models continue to learn and improve as new data is gathered.

A concept related to Deployment, where the model assigns data points to specific segments or groups.