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October 7, 2017 22:34
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import numpy as np | |
from sklearn.model_selection import train_test_split | |
from sklearn.linear_model import LinearRegression | |
income = [[2000], [2500], [2700], [3900], [4100], [4550]] | |
expenditure = [1950, 2400, 2500, 3800, 3950, 4410] | |
X = np.array(income) | |
Y = np.array(expenditure) | |
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, random_state=1) | |
model = LinearRegression() | |
model.fit(X_train, Y_train) | |
print("The intercept of the model: {}".format(model.intercept_)) | |
print("Coefficient of variable: {}".format(model.coef_)) | |
# The sum squared error | |
print("Sum of squared error: {}".format(np.sum(model.predict(X_test) - Y_test) ** 2)) | |
# Explained variance score: 1 is perfect prediction | |
print('Variance score: {}'.format(model.score(X_test, Y_test))) |
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linear regression