code: import numpy as np
  np.random.seed(1337)
  from keras.models import Sequential
  from keras.layers import Dense
  import matplotlib.pyplot as plt
  
  
  model = Sequential()
  model.add(Dense(units=50, input_dim=1, activation='relu'))
  model.add(Dense(units=50, activation='relu'))
  model.add(Dense(units=1, activation='sigmoid'))
  model.add(Dense(units=1, activation='linear'))
  model.compile(optimizer='adam', loss='mean_squared_error')
  model.summary()
  
  # uk corona
  import json
  
  url = 'https://api.covid19uk.live/historyfigures'
  
  
  def read_url_to_json(url):
      import urllib.request as request
      webpage = request.urlopen(url)
      get_data = webpage.read()
      data = json.loads(get_data)
      return data
  
  
  read_data = read_url_to_json(url)
  each_data = read_data['data']
  uk_comfirmed_data = []
  
  for each in each_data:
      uk_comfirmed_data.append(each['confirmed'])
  
  uk_date_length = len(uk_comfirmed_data)
  uk_dates = list(range(1, uk_date_length + 1))
  
  uk_comfirmed_data = np.array(uk_comfirmed_data)
  uk_dates = np.array(uk_dates)
  
  uk_absorb_amount = uk_comfirmed_data[uk_date_length-1]
  
  uk_comfirmed_data_norm = uk_comfirmed_data / uk_absorb_amount
  
  # fit model
  model.fit(uk_dates, uk_comfirmed_data_norm, epochs=10000, shuffle=False)
  
  uk_comfirmed_data_predict = model.predict(uk_dates)
  uk_comfirmed_data_predict = uk_comfirmed_data_predict * uk_absorb_amount
  fig2 = plt.figure(figsize=(7, 5))
  plt.scatter(uk_dates, uk_comfirmed_data, label='Real Confirmed')
  plt.plot(uk_dates, uk_comfirmed_data_predict, label='Predict Result')
  plt.title('UK Confirmed VS Dates')
  plt.xlabel('Dates')
  plt.ylabel('Amount')
  plt.legend()
  plt.show()
apis: matplotlib.pyplot.legend matplotlib.pyplot.figure numpy.random.seed matplotlib.pyplot.title matplotlib.pyplot.show matplotlib.pyplot.ylabel numpy.array matplotlib.pyplot.plot matplotlib.pyplot.xlabel matplotlib.pyplot.scatter