Code for optimization 1

optimization1.py

# Separate feature and target
X = df.drop(['ROP_AVG'], axis=1)
y = df['ROP_AVG']

# Train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, 
                                                    random_state=10)

# Make pipeline
steps = [('scaler', StandardScaler()), 
         ('gbr', GradientBoostingRegressor(min_samples_leaf=6, max_depth=20, 
                                           random_state=10))]
pipe = Pipeline(steps)

# Fit pipeline to training data
pipe.fit(X_train,y_train)

# Evaluate model with R2 metric
train_score = pipe.score(X_train, y_train)
test_score = pipe.score(X_test, y_test)
print(f'R2 on train set: {train_score:.2f}')
print(f'R2 on test set: {test_score:.2f}')