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