Predicting bike sharing demand

In the previous dataset, the were strongly correlated with the labels. However, in some time series, we have features that might be less correlated or not correlated with the labels at all. The main idea behind machine learning is that the algorithm tries to figure out by itself which features are valuable and which are not. Especially in deep learning, we want to keep the feature engineering limited. In the following recipe, we will be predicting the demand for bike sharing rentals. The data includes some interesting features, such as weather type, holiday, temperature, and season.

How to do it...

1. First, we all libraries:

from sklearn import preprocessing
import pandas as pd
import numpy as np
from math import pi, sin, cos
from datetime import datetime

from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping

2. The training and test data is stored in two...