Final answer:
The phase that includes cleaning, transforming, and organizing raw data to be used for modeling tools is known as the Data Preparation or Data Preprocessing phase. It ensures that the final dataset is ready for analytical modeling, which is essential for creating accurate models and facilitating effective decision-making.
Step-by-step explanation:
The phase that covers all activities to construct the final dataset from the initial raw data is known as the Data Preparation or Data Preprocessing phase. This phase includes various tasks such as cleaning the data, selecting the relevant information, handling missing data, normalizing the data, and potentially transforming variables to ensure that the data is ready to be used by modeling tools. The goal of this phase is to convert raw data into a format that can be effectively used to create predictive or descriptive models. This often involves statistical methods to summarize and visualize data to better understand it and to discern any patterns or insights that could be beneficial for the subsequent modeling process.
Data preparation is a critical step because the quality and structure of the data fed into the modeling tools can significantly influence the accuracy and performance of the resulting models. Once the dataset is suitably prepared, it can be used in machine learning and data mining applications to formulate models, theories, and hypotheses, contributing to knowledge discovery and decision-making processes in various domains.