Now that we have our neural network trained, let's use it to make some predictions to understand its accuracy.
We can create a function to make a prediction using a random sample from the testing set:
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...
