python linear regression
from sklearn.linear_model import LinearRegression
#x, y = np.array([0,1,2,3,4,5])
model = LinearRegression().fit(x.reshape(-1,1), y.reshape(-1,1))
r_sq = model.score(x.reshape(-1,1), y.reshape(-1,1))
q = model.intercept_
m = model.coef_
y_fit = np.array([i*m[0] for i in x]+q[0])python linear regression
>>> from scipy import stats
>>> import numpy as np
>>> x = np.random.random(10)
>>> y = np.random.random(10)
>>> slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)python linear regression
import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('tips')
sb.regplot(x = "total_bill", y = "tip", data = df)
plt.show()Copyright © 2021 Codeinu
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