Pearson Correlation

  • Correlation tells us how strongly two random variables are related to each other.

  • Pearson Co-relation assume that both the datasets are from normal distribution, so it can't be used with discrete variables.

  • Pearson Co-relation is not affected by scale and so easy to interpret

  • Ranges from $[-1, 1]$

  • We can interpret as,

    • $-1$ as totally Negative relationship
    • $+1$ as totally Positive relationship
    • $\approx 0$ as no relationship
  • Pearson-correlation depends on Variance and Co-Variance

[!def] Formula of Pearson Co-relation
Co-relation(x, y) = \frac{Co-Variance(x, y)}{\sqrt{variance(x)} \sqrt{variance(y)}}


  1. Cosine Similarity vs Pearson Correlation