Yes, it's possible to use the same code to perform multiple linear regression with interaction terms. In order to include interaction terms in the model, you need to create new variables that represent the interaction between the predictor variables. Here's an example of how you can modify the code to include interaction terms:

```python

from sklearn.linear_model import LinearRegression

import pandas as pd

# Assuming you have a dataset called "data" with variables "outcome", "predictor1", and "predictor2"

df = pd.read_csv("data.csv")

# Create interaction term

df["interaction"] = df["predictor1"] * df["predictor2"]

# Create X and y arrays from dataframe columns

X = df[["predictor1", "predictor2", "interaction"]]

y = df["outcome"]

# Train the model

model = LinearRegression()

model.fit(X, y)

# Print the coefficients

print(model.coef_)

```

In this example, we create a new variable called "interaction" which is the product of "predictor1" and "predict