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Data Mining Regression Analysis
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Data Mining Regression Analysis
People and organizations collect big data and analyze it to generate patterns. Data mining
includes analyzing large data sets to find trends that can be used to predict future business
operations. Data mining allows businesses to extract useful information from collected data and
use it to improve business operations. Data mining provides learning opportunities that enable
businesses to implement essential strategies that impact business growth (Saxena et al., 2018).
Regression analysis determines the vital variables that affect a specific topic. Regression analysis
is used when examining a particular subject and allows people and businesses to assess the
essential factors. Regression analysis helps firms to select factors that matter while ignoring the
non-useful factors.
Data mining regression analysis is an approach for applying regression analysis methods
in data mining. Data mining regression analysis involves determining the relationship between
dependent and independent variables. A dependent variable is a component that changes when
the independent variable is manipulated. The dependent variable represents the outcome
measured by businesses. An independent variable is a component controlled in a study to
determine its impact. In data mining regression analysis, a dependent variable is the predicted
component, while independent variables are features that may influence the dependent
component. Regression analysis is applied in data mining to model the relationship between the
variables (Saxena et al., 2018). Regression analysis models use the mathematical function to
outline the relationship and showcase the data points, helping businesses generate accurate
predictions.
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Data mining regression analysis involves predictive modeling. Predictive modeling helps
people and businesses understand the influence of specific components on the dependent
variable. Data mining regression analysis involves selecting the appropriate features that impact
the dependent variable. The chosen features are used to improve data mining. Data mining
regression analysis involves data preprocessing to ensure collected data is cleaned before it is
applied in the mathematical model to generate results. Data cleaning is an essential component
that ensures errors are eliminated, and complete data is available to support predictive modeling.
Model evaluation is applied in regression analysis (Guerard et al., 2021). Evaluation helps
businesses to determine the importance of regression analysis in data mining. Evaluation outlines
the effectiveness of using the analysis techniques to generate accurate predictions. Mean Squared
Error (MSE) is an example of a regression analysis model that determines its performance. MSE
shows the difference that occurs between actual and predicted values.
An example of data mining regression analysis is using the approach in healthcare.
Regression analysis is applied in the health sector to predict diseases. A regression model is
implemented to examine patient data and provide patterns that outline whether the patient will
develop other diseases. Data mining regression analysis detects risks and forecast outcomes. An
example of regression analysis in the health sector is determining treatment response. The
prediction of treatment response allows healthcare professionals to administer an accurate
treatment approach. Sales forecasting is an example of data mining regression analysis.
Regression analysis uses current and historical sales to generate exact patterns (Saxena et al.,
2018). Regression analysis selects appropriate features influencing sales, such as marketing,
pricing, and external factors. Regression analysis outlines adjustments for improving sales.
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References
Guerard, J. B., Xu, G., & Markowitz, H. (2021). A further analysis of robust regression modeling
and data mining corrections testing in global stocks. Annals of Operations
Research, 303(1–2), 175–195. https://doi.org/10.1007/s10479-020-03521-y
Saxena, K., Saxena, S., & Saxena, A. (2018). DATA MINING AND WAREHOUSING. BPB
Publications.