Neural Network Projects with Python

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-actual_fare)) print("Trip Details: {}, {}:00hrs".format(day_of_week, ...