Results analysis

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...