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Exercise

Feature Importance plots for admissions analysis

As part of the data science team at a university, your task is to evaluate which factors truly drive admissions decisions and which ones are less important. While the admissions committee knows CGPA plays a key role, they want to confirm this and uncover any other important factors that may influence outcomes. Using a RandomForestRegressor model, you'll visualize feature importance to clearly identify which aspects of applicants' profiles matter most and which have less of an impact on the decision process.

The shap library and the training data (X_train, y_train) have been pre-loaded for you.

Instructions

100 XP
  • Derive the shap_values using a TreeExplainer.
  • Use the derived shap_values to plot the feature importances with a bar plot and analyze it.