The data preparation process defines a level of analysis recommended for
model training and testing of a specific dataset. f The data preparation provides
a level of feasibility to understand the analysis approach for modeling and
performance evaluation. f These methods applied to the data preparation
process offers a formal approach for implementation in development. f The
approach for data preparation includes an examination of the recommended
practices regarding the data selection and collection (e.g., types, variables,
procedures, data management, and information). f f A data preparation
framework is implemented and map to the data tools according to the user
application needs. f The ability to deploy data preparation techniques with the
use of data handling practices allow for a better understanding to examine the
implementation approach.
As such, the data preparation techniques described presents an
implementation approach that is defined by the role, variable types, and
measurement levels. f f This approach is defined by the input and target results
relative to the measurement levels (binary, interval, and nominal). f As
described using a defined data preparation scheme, the data set will
implementation approach will serve the target variables to indicate the
relationships of roles associated with specific measurements and types in the
analysis. f The examination describes how results from each of the variable can
be identified as an attribute by the assigned labels according to the names,
observations, and functions. of data set indicates the target variables with the
relationships of roles associated to the specific measurement levels and
frequency counts.
The data cleansing process also considers the use for interpretation by
allowing the techniques such as regression and classification methods to be
applied to an application for forecasting. f The forecasting of methods to assess
how to better achieve results can be computed according to the recommended
features in the data cleansing process. f The tool (SAS Enterprise Miner)
promotes the ability to handle a data cleansing approach as identified in this
section to effectively manage the integration of data involving two different
data sets for comparison and preference with modeling results. f The modeling
results can be scale using an effective data cleansing process by capturing data
volumes, inconsistently, information correctness, completeness, and
availability. f f The multi-environment data set can create complexity in the data
analysis related to difficulties to map data appropriate, which may result to
incomplete and missing data. f f The proposed concept can be applied to
optimized parameters and methods to adopt an integrated framework by
exploring the best-case scenario for a multi-complex data set environment in
data cleansing.
