@fc35ccfd: Yes, it is 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 "predictor2". We then include this interaction term along with the original predictor variables in the model.

By including interaction terms, the coefficients will represent the change in the outcome variable associated with a one-unit change in the predictor variable, while holding all other predictors constant, including the interaction term.

I hope this helps! Let me know if you have any further questions.