# Split data into features $($X$)$ and target $($y$)$

X $=$ data$\_$df$.$drop$(\text{'}$target$\_$column', axis$=1)$

y $=$ data$\_$df$[\text{'}$target$\_$column'$]$

# Split data into training and testing sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=42)$

# Create and train a Linear Regression model

model $=$ LinearRegression$()$

model.fit$($X$\_$train, y$\_$train$)$

# Make predictions

y$\_$pred $=$ model.predict$($X$\_$test$)$

# Evaluate the model

mse $=$ mean$\_$squared$\_$error$($y$\_$test, y$\_$pred$)$