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.
Cet exercice fait partie du cours
A/B Testing in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Import seaborn for visualization
import ____ as ____
# Visualize the variables in a pairplot
____.____(admissions[['____', '____', '____', '____']])
plt.show()