Day #10 Data Science Learning - Correlations

Correlation

• If you have correlation you can use machine learning to predict variables
• Mutual relationship connection between two or more things
• Correlation shows inter dependence between two variables
• Measure - How much one changes when other also changes ?
• Popularly Used - Pearson Correlation coefficient
• Value ranges from -1 to +1
• Negative correlation (Closer to -1) - One value goes up other goes down
• Closer to Zero (No Correlation)
• Closer to 1 (Positive Correlation)

Correlation - Relationship between two values
Causation - Reason for change in value (Cholesterol vs weight, Dress Size Vs Cholesterol). Identify if it is incidental.

Handling highly correlated variables

• Remove two correlated variables, unless correlation=1 or -1, in which case one of the variables is redundant.
• Perform a PCA
• Permutation feature importance (Link)
• Greedy Elimination (GE): iteratively eliminate feature of highest correlated feature pair
• Recursive Feature Elimination (RFE): recursive elimination of features with respect to their importance
• Lasso Regulariosion (LR): use L1 regularisation to remove features with zero weight
• Principle Component Analysis (PCA): transform data set with PCA and choose components with highest variations

Ref  - Link1, Link2 , Link3

Happy Learning!!!