X = df.copy()
y = X.pop("CompressiveStrength")

# Create synthetic features
X["FCRatio"] = X["FineAggregate"] / X["CoarseAggregate"]
X["AggCmtRatio"] = (X["CoarseAggregate"] + X["FineAggregate"]) / X["Cement"]
X["WtrCmtRatio"] = X["Water"] / X["Cement"]

# Train and score model on dataset with additional ratio features
model = RandomForestRegressor(criterion="mae", random_state=0)
score = cross_val_score(
    model, X, y, cv=5, scoring="neg_mean_absolute_error"
)
score = -1 * score.mean()

print(f"MAE Score with Ratio Features: {score:.4}")