The data preparation process defines a level of analysis recommended for
model training and testing of a specific dataset. g The data preparation
provides a level of feasibility to understand the analysis approach for
modeling and performance evaluation. g These methods applied to the data
preparation process offers a formal approach for implementation in
development. g 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). g g A data
preparation framework is implemented and map to the data tools according
to the user application needs. g 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. g g This approach is defined by the input and target
results relative to the measurement levels (binary, interval, and nominal). g
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. g 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. g The forecasting of methods to
assess how to better achieve results can be computed according to the
recommended features in the data cleansing process. g 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. g The modeling results can be scale using an effective data
cleansing process by capturing data volumes, inconsistently, information
correctness, completeness, and availability. g g 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. g g 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.
