• Extension of Co-Variance
• 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

• As we can see co-relation is dependent on Co-Variance and Variance

TODO:

1. Cosine Similarity vs Pearson Correlation