Which process involves preparing data for better accuracy in predictive analytics?

Data preparation is a crucial step in predictive analytics that focuses on organizing and refining raw data to enhance its quality, which ultimately leads to better accuracy in predictive models. This process includes various activities such as cleaning, selecting, and transforming raw data into a suitable format for analysis.

During data preparation, issues like missing values, inconsistencies, and irrelevant features are addressed. By ensuring that the data is accurate, complete, and relevant, the predictive models built on this data can yield more reliable results. For instance, properly handling missing values or outlier data points can significantly influence the effectiveness of model training and validation.

Though data mining, data transformation, and data visualization are all important aspects of data analysis, their primary focus does not inherently prioritize preparing data for predictive accuracy. Data mining involves exploring large datasets to discover patterns, data transformation focuses on changing data formats or structures, and data visualization pertains to representing data graphically to better understand trends or insights. These processes may occur after the data preparation phase and certainly benefit from high-quality prepared data.