It often involves applying live models within an organization’s decision-making processes-for example, real-time personalization web page, product recommender system, or scoring of marketing leads.
DATA
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Model Deployment: From Creation to Practical Implementation
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6. Deployment The creation of the model is generally not the end of the project. Even if the purpose of the model is to analyze the data and increase the understanding of the data, the knowledge or insight gained from the modeling needs to…
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Data Mining Results Decision Phase Completion
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At the end of this phase, a decision on using the data mining results should be reached.
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Evaluation Phase in Data Science Project Lifecycle
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5. Evaluation The evaluation phase is one of the most crucial phases of any data science project lifecycle. At this stage in your project, you must have built machine-learning models.
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Evaluating AI Models on Unseen Data and Performance Metrics
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Models might be performing well on your training data, but it is necessary to test and evaluate it on unseen data for the model to achieve its objectives. Appropriate evaluation metrics are measured and well-tested at this stage.
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Data Preparation Phase Requirements for AI Techniques
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Some techniques have specific requirements for the format of data it needs. Therefore, going back to the data preparation phase is often necessary at this stage.
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Data Preparation Techniques for Machine Learning Models
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Tasks include data table, feature selection, feature engineering, as well as feature transformation and cleaning of data for modeling tools and techniques.
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Modeling Techniques and Parameter Calibration in Data Mining
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4. Modeling In this phase, various modeling techniques depending on the problem statement, are selected and applied, and their parameters are calibrated to find optimal modeling performance. Typically, there are several techniques for the same data mining problem type.
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Data Preparation Phase for AI Modeling
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3. Data Preparation The data preparation phase covers all activities needed to construct the final dataset fed into the modeling tools from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order.
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Data Understanding Phase: Initial Collection and Exploratory Analysis
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2. Data Understanding The data understanding phase starts with initial data collection and proceeds with activities such as feature description, primary data analysis, and exploratory data analysis that enable…