train_pred = model.predict(X_train)
train_rmse = np.sqrt(mean_squared_error(y_train, train_pred))
test_pred = model.predict(X_test)
test_rmse = np.sqrt(mean_squared_error(y_test, test_pred))
print("Train RMSE: {:0.2f}".format(train_rmse))
print("Test RMSE: {:0.2f}".format(test_rmse))
 
def predict_random(df_prescaled, X_test, model):
    sample = X_test.sample(n=1, random_state=np.random.randint(low=0, high=10000))
    idx = sample.index[0]
    actual_fare = df_prescaled.loc[idx,'fare_amount']
    day_names = ['Monday','Tuesday','Wednesday','Thursday','Friday', 'Saturday','Sunday']
    day_of_week = day_names[df_prescaled.loc[idx,'day_of_week']]
    hour = df_prescaled.loc[idx,'hour']
    predicted_fare = model.predict(sample)[0][0]
    rmse = np.sqrt(np.square(predicted_fare-actual_fare))
    print("Trip Details: {}, {}:00hrs".format(day_of_week, hour))
    print("Actual fare: ${:0.2f}".format(actual_fare))
    print("Predicted fare: ${:0.2f}".format(predicted_fare))
    print("RMSE: ${:0.2f}".format(rmse))
 
predict_random(df_prescaled, X_test, model)