What Is Correlation in Machine Learning?
Correlation:
Correlation explains how one or more variables are related to each other. These variables can be input data features which have been used to forecast our target variable.
Correlation, statistical technique which determines how one variables moves/changes in relation with the other variable. It gives us the idea about the degree of the relationship of the two variables. It’s a bi-variate analysis measure which describes the association between different variables. In most of the business it’s useful to express one subject in terms of its relationship with others.
For example: No of testing vs no of positive cases in Corona.
1. If two variables are closely correlated, then we can predict one variable from the other.
2. Correlation plays a vital role in locating the important variables on which other variables depend.
3. It’s used as the foundation for various modeling techniques.
4. Proper correlation analysis leads to better understanding of data.
5. Correlation contribute towards the understanding of causal relationship (if any).
Positive Correlation: Two features (variables) can be positively correlated with each other. It means that when the value of one variable increase then the value of the other variable(s) also increases.
Negative Correlation: Two features (variables) can be negatively correlated with each other. It means that when the value of one variable increase then the value of the other variable(s) decreases.
No Correlation: Two features (variables) are not correlated with each other. It means that when the value of one variable increase or decrease then the value of the other variable(s) doesn’t increase or decreases.
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- https://github.com/amitupadhyay6/My-Python/blob/Linear-Regression/Linear%20Regression/LG%20-%20Boston-Corelation%20Applied.ipynb
