old_contents: 
  # pip install pandas scikit-learn scipy numpy
  import pandas as pd
  from sklearn.linear_model import Ridge as Classifier
  
  # sudo apt install python3-tk
  # pip install matplotlib
  import matplotlib.pyplot as plt
  
  house_data = pd.read_csv('house_data.csv')[:100]
  
  house_data['sqm_living'] = house_data['sqft_living']*0.092903
  
  prices = house_data['price']
  features = house_data[['sqm_living']]
  classifier = Classifier()
  classifier.fit(features, prices)
  print('100 sqm', classifier.predict(100))
  print('100 sqm', classifier.predict(200))
  
  print(house_data[:10])
  
  # prices.plot(x='sqft_living', y='price')
  # plt.show()
  print(house_data.keys())
  
  features = house_data[['sqm_living', 'bedrooms']]
  classifier = Classifier()
  classifier.fit(features, prices)
  print('100 sqm, 1 bedroom', classifier.predict([[100, 1]]))
  print('200 sqm, 2 bedrooms', classifier.predict([[200, 2]]))
  
  
  features = house_data[['sqm_living', 'bedrooms', 'waterfront']]
  classifier = Classifier()
  classifier.fit(features, prices)
  print('boring ass house', classifier.predict([[200, 2, 0]]))
  print('house at water', classifier.predict([[200, 2, 1]]))
  
  
  
new_contents: 
  # pip install pandas scikit-learn scipy numpy
  import pandas as pd
  from sklearn.linear_model import Ridge as Classifier
  
  # sudo apt install python3-tk
  # pip install matplotlib
  import matplotlib.pyplot as plt
  
  house_data = pd.read_csv('house_data.csv')[:100]
  
  house_data['sqm_living'] = house_data['sqft_living']*0.092903
  
  prices = house_data['price']
  features = house_data[['sqm_living']]
  classifier = Classifier(alpha=0)
  classifier.fit(features, prices)
  print('100 sqm', classifier.predict(100))
  print('100 sqm', classifier.predict(200))
  
  print(house_data[:10])
  
  # prices.plot(x='sqft_living', y='price')
  # plt.show()
  print(house_data.keys())
  
  features = house_data[['sqm_living', 'bedrooms']]
  classifier = Classifier(alpha=0)
  classifier.fit(features, prices)
  print('100 sqm, 1 bedroom', classifier.predict([[100, 1]]))
  print('200 sqm, 2 bedrooms', classifier.predict([[200, 2]]))
  
  
  features = house_data[['sqm_living', 'bedrooms', 'waterfront']]
  classifier = Classifier(alpha=0)
  classifier.fit(features, prices)
  print('boring ass house', classifier.predict([[200, 2, 0]]))
  print('house at water', classifier.predict([[200, 2, 1]]))
  
  
  
subject: Set regularization parameter to zero