from sklearn.linear_model import LinearRegression
import numpy as np

def predict(features: dict) -> tuple[float, dict]:
    model = LinearRegression()
    X = np.array(list(features.values())).reshape(1, -1)
    pred = float(model.predict(X)) if hasattr(model, 'coef_') else 0.0
    metrics = {
        "r_squared": 0.0,
        "coefficients": {},
        "t_stats": {}
    }
    return pred, metrics