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Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
Sklearn Ridge - Training a Ridge Regression Model

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Python Code::
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error, mean_absolute_error
# initialise & fit a ridge regression model with alpha set to 1
# if the model is overfitting, increase the alpha value
model = Ridge(alpha=1)
model.fit(X_train, y_train)
# create dictionary that contains the feature coefficients
coef = dict(zip(X_train.columns, model.coef_.T))
print(coef)
# make prediction for test data
y_pred = model.predict(X_test)
# evaluate performance
print('RMSE:',mean_squared_error(y_test, y_pred, squared = False))
print('MAE:',mean_absolute_error(y_test, y_pred))

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