In order to assure a better convergence of the backpropagation methods, we should try to normalize the input data. So, we will be applying the classic scale and centering technique, subtracting the mean value, and scaling by the floor() of the maximum value. To get the required values, we use the pandas describe() method:
print(df.describe()) array=(df.values - 145.33) /338.21 plt.subplot() plot_test, = plt.plot(array[:1500], label='Normalized Load') plt.legend(handles=[plot_test]) Load count 140256.000000 mean 145.332503 std 48.477976 min 0.000000 25% 106.850998 50% 151.428571 75% 177.557604 max 338.218126
This is the graph of our normalized data:
In this step, we will prepare our input dataset, because we need an ...
