python linear regression

# import the class
from sklearn.linear_model import LogisticRegression

# instantiate the model (using the default parameters)
logreg = LogisticRegression()

# fit the model with data
logreg.fit(X_train,y_train)

#
y_pred=logreg.predict(X_test)

0
9
Awgiedawgie 440220 points

                                    >>> 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)

0
0
3.67
3
IllusiveBrian 18110 points

                                    
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()

3.67 (3 Votes)
0
4.17
6
Awgiedawgie 440220 points

                                    from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
reg = LinearRegression().fit(X, y)
reg.score(X, y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3, 5]]))

4.17 (6 Votes)
0
4
6
Phoenix Logan 186120 points

                                    from sklearn.linear_model import LinearRegression
lm = LinearRegression()
lm.fit(X, y)

4 (6 Votes)
0
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