Multicollinearity
Variables
Key Assumptions
Factors
Variables
Data Mining Methods in Business
Key Factors in Predictions
Variables 
In predictive modeling, carefully select variables that contribute significantly to the model's performance while avoiding
multicollinearity ...
Key Assumptions Multicollinearity Independent variables are assumed to be uncorrelated with each other
...
Factors Multicollinearity: When independent factors are highly correlated, it can distort the analysis
...
Variables Management While variables are essential in business analytics and data mining, managing them can pose challenges:
Multicollinearity: This occurs when two or more variables are highly correlated, which can skew analysis results
...
Data Mining Methods in Business into relationships between variables Can handle multiple predictors Challenges Assumes a linear relationship
Multicollinearity can affect results Association Rule Learning Association rule learning is a method used to discover interesting relationships between variables in large datasets
...
Key Factors in Predictions Redundancy: Avoid including highly correlated features to prevent
multicollinearity ...
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