PYTHON

looping through models and plotting their performance

models = {"Linear Regression": LinearRegression(), "Ridge": Ridge(alpha=0.1), "Lasso": Lasso(alpha=0.1)}
results = []

# Loop through the models' values
for model in models.values():
  kf = KFold(n_splits=6, random_state=42, shuffle=True)
  
  # Perform cross-validation
  cv_scores = cross_val_score(model, X_train, y_train, cv=kf)
  
  # Append the results
  results.append(cv_scores)
  
# Create a box plot of the results
plt.boxplot(results, labels=models.keys())
plt.show()
# Import mean_squared_error
from sklearn.metrics import mean_squared_error

for name, model in models.items():
  
  # Fit the model to the training dat
  model.fit(X_train_scaled, y_train)
  
  # Make predictions on the test set
  y_pred = model.predict(X_test_scaled)
  
  # Calculate the test_rmse
  test_rmse = mean_squared_error(y_test, y_pred, squared=False)
  print("{} Test Set RMSE: {}".format(name, test_rmse))