tuto_1.py

# Use Linear Regression to estimate continuous values

import matplotlib.pyplot as plt
from sklearn import datasets, metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

boston = datasets.load_boston()

print(boston.keys())
print(boston.feature_names)
print(boston.DESCR)
print(boston.data[:5])
print(boston.target[:5])
print(boston.data.shape)
print(boston.target.shape)

X = boston.data
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=16)
model = LinearRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

plt.scatter(y_test, predictions)
plt.xlabel("Actual prices")
plt.ylabel("Predicted prices")
plt.show()

print(predictions)
print(model.score(X_test, y_test))
print(metrics.mean_squared_error(y_test, predictions))