Computing feature impact with logistic regression

Continuing your work at the insurance company, you built a predictive model to identify whether an individual is a smoker or not. Now, you need to analyze the model to determine the relevant factors influencing smoking status, helping the company assess risk more accurately and tailor insurance policies accordingly.

matplotlib.pyplot has been imported as plt. X_train and y_train are pre-loaded for you.

This exercise is part of the course

Explainable AI in Python

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Exercise instructions

Extract the coefficients from the model.
Plot the coefficients for the given feature_names.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)

model = LogisticRegression()
model.fit(X_train_scaled, y_train)

# Derive coefficients
coefficients = ____
feature_names = X_train.columns

# Plot coefficients
____
plt.show()

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