Correlation

Here you’ll find everything about correlation and covariance, whether it’s Python tutorials that introduce Pearson correlation, articles that show how to implement it in more sophisticated use cases, or conceptual articles that explain different types of correlation.

Correlation is a statistical measure that indicates the degree to which two variables are related. It can take values between -1 and 1, where a value of -1 indicates a strong negative correlation (i.e., as one variable increases, the other decreases), a value of 0 indicates no correlation, and a value of 1 indicates a strong positive correlation (i.e., as one variable increases, the other also increases). Correlation is commonly used in statistics and machine learning to understand the relationship between different variables in a dataset. For example, it can be used to identify features that are strongly related to the target variable, or to identify features that are highly correlated with each other and may be redundant. A high degree of correlation between two variables can be useful for making predictions, because it indicates that there is a strong relationship between the variables that the model can use.