Python-Learning/src/Learning/Linear Regression.py at master · ElioDiNino/Python-Learning · GitHub

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from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.preprocessing import PolynomialFeatures
import statsmodels.api as sm

x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1))
y = np.array([5, 20, 14, 32, 22, 38])
model = LinearRegression().fit(x, y)
r_sq = model.score(x, y)
print('Coefficient of determination:', r_sq)
print('Intercept:', model.intercept_)
print('Slope:', model.coef_)

y_pred = model.predict(x)
print('Predicted response:', y_pred, sep='\n')


x = [[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]]
y = [4, 5, 20, 14, 32, 22, 38, 43]
x, y = np.array(x), np.array(y)
model = LinearRegression().fit(x, y)
r_sq = model.score(x, y)
print('Coefficient of determination:', r_sq)
print('Intercept:', model.intercept_)
print('Slope:', model.coef_)

y_pred = model.predict(x)
print('Predicted response:', y_pred, sep='\n')


x = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1))
y = np.array([15, 11, 2, 8, 25, 32])
transformer = PolynomialFeatures(degree=2, include_bias=False)
transformer.fit(x)
x_ = transformer.transform(x)
'''
x_ = PolynomialFeatures(degree=2, include_bias=False).fit_transform(x)
'''
print(x_)
model = LinearRegression().fit(x_, y)
r_sq = model.score(x_, y)
print('Predicted response:', y_pred, sep='\n')


x = [[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]]
y = [4, 5, 20, 14, 32, 22, 38, 43]
x, y = np.array(x), np.array(y)
x = sm.add_constant(x)
model = sm.OLS(y, x)
results = model.fit()
print(results.summary())
print('coefficient of determination:', results.rsquared)
print('adjusted coefficient of determination:', results.rsquared_adj)
print('regression coefficients:', results.params)

print('predicted response:', results.fittedvalues, sep='\n')
print('predicted response:', results.predict(x), sep='\n')