from sklearn.linear_model import LinearRegression

def perform_regression(data, data_name, target_name):
    X = data[data_name]
    y = data[target_name]

    if not isinstance(y.iloc[0], (int, float)):
        raise ValueError("The target variable should be numeric (continuous) for regression.")

    model = LinearRegression()
    model.fit(X, y)
    
    return model

def make_prediction(model, feature_names, input_values):
    prediction = model.predict([input_values])

    return prediction[0]