Session Ready
Exercise

Correlation visualization

Though correlations do not imply causation, they quantify the strength and direction to which two variables are associated. This is especially useful in situations where A/B tests are not feasible due to lack of resources or limited data/user-base.

The admissions dataset is loaded for you and includes various information like GRE score, TOEFL score, SOP (Statement of Purpose), LOR (Letter of Recommendation), CGPA, and chance of admission. You will examine the relationship between some of these attributes and how the chances of admission changes with changes in these variables.

Instructions 1/3
undefined XP
  • 1
    • Import the Seaborn library and give it the alias sns.
    • Inspect the relationship of the Serial No., TOEFL Score, SOP, Chance of Admit variables visually, in that order, using a grid of scatter plots.
  • 2
    • Examine the relationship's strength and direction using Pearson's correlation coefficients between the Serial No., TOEFL Score, SOP, Chance of Admit variables, in that order.
  • 3
    • Visualize Pearson's correlation coefficients in a heat map with annotated coefficients.
Loading